Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Knowledge Discovery from Biomedical Ontologies in Cross Domains

  • Feichen Shen,

    Affiliation Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America

  • Yugyung Lee

    Affiliation School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, Missouri, United States of America

Knowledge Discovery from Biomedical Ontologies in Cross Domains

  • Feichen Shen, 
  • Yugyung Lee


In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.


There is an increasing demand for sharing and integration of medical data in biomedical research. Heterogeneous information networking on the cloud are designed to enable compliant sharing of data based on the relationships across domains [1]. The Linked Open Data project is a notable effort for creating a knowledge space of RDF documents linked together and sharing a common ontology [2]. RDF is a metadata data model designed by the World Wide Web for conceptual modeling of information on the Web [3]. SPARQL Protocol and RDF Query Language is an RDF query language for semantic query language to retrieve data stored in RDF format [4]. According to the Linked Open Data project, the Web of Data currently consists of 4.7 billion RDF triples, which are interlinked by around 142 million RDF links (May 2009) [5]. Bio2RDF (Linked Data for the Life Sciences) [6] is one of the Linked Open Data projects in life science domains and has successfully converted bioinformatics databases such as KEGG, DrugBank, MGI, HGNC and several of NCBI databases into ontologies using Semantic Web technologies. Bio2RDF contains over 2.5 million triples and 0.19 million outlinks and 0.19 million inlinks [7].

In order to improve a health care system, it is required to conduct the integration of knowledge and data by facilitating medical ontologies and to support semantic interoperability systems and practices [8]. For the purpose, semantic interoperability is essential between heterogeneous ontologies and datasets [9]. The benefits of semantic interoperability are clear for improving accuracy and efficiency of diagnoses and treatment by sharing patient data and providing semantic-based criteria. However, integration and analysis of heterogeneous ontologies and datasets are a huge challenge in biomedical research since the mapping between datasets from different sources is not trivial [10]. For example, drug discovery research heavily relies on multiple information sources to validate potential drug candidates as shown in the Open PHACTS project [11].

In complicated domains, it not only takes time to develop and maintain ontologies [12], but it is also difficult to integrate relevant data that would be both practical and useful for biomedical research [13]. There have been various studies on using semantic techniques to improve data integration and share biomedical ontologies and datasets such as BioPortal [14], Bio2RDF [6] and OBO [15]. However, these efforts merely support physical integration of multiple biomedical ontologies without considering latent semantic relations of data. Furthermore, none of them has the ability to discover those semantic patterns in a systematic way. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In particular, human intervention is strongly required so that these are not suitable for comprehensive and accurate knowledge discovery especially from a large amount of data.

We need a systematic approach for more effective integration and analysis of ontologies [12]. In particular, we need innovative methodologies and applications for data integration and sharing [10]. This may be feasible through analysis of the heterogeneous information networks that represent different types of objects and links in cross domains [1]. In order to support dynamic processing of integrated cross domain data, a network-based data model such as resource description framework standards (RDF) and RDF Query Language (SPARQL) can be used for knowledge discovery from complex biomedical systems [16].

In this paper, we propose a semantic framework, called the Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different domains. In MedKDD, we fully focus on the analysis of semantic patterns in heterogeneous information networks for knowledge discovery across multiple domains. In our study, we consider an ontology as a domain and information retrieval across multiple ontologies in highly specialized medical domains as cross domain knowledge discovery. Any relationships across multiple domains (ontologies) are defined as cross domain relationships. Our model would be applicable to domains that have any common concepts, individuals or predicates (relationships) of ontologies. The building blocks that make up the best system of knowledge discovery with multiple domains are (i) a pattern based approach for predicate neighborhood defined for the heterogeneous information network, (ii) integrating the cross domain relations by evidences gathering from these patterns, (iii) graph partition and quantitative analysis using data mining algorithms, and (iv) exploration and discovery through query processing.

We demonstrate the cross domain knowledge discovery in the MedKDD framework using a case study with nine primary biological ontologies of Bio2RDF [17] including ClinicalTrials [18], DrugBank [19], OMIM [20], PharmGKB [21], SIDER [22], KEGG [23], CTD [24], HGNC [25], MGI [26]. We have implemented the MedKDD system and the experimental results clearly showed the validity of the MedKDD framework that was designed for Knowledge discovery from heterogeneous information networks across a medical domain.

The major content of this paper is organized as follows: We first present the MedKDD framework in Section Materials and Methods. We then describe the implementation of the MedKDD system and the experimental results in Section Results. We present discussion in Section Discussion. The conclusion and future work is discussed in Section Conclusion.

Materials and Methods

We now present the MedKDD framework that aims to support knowledge discovery from cross domains by the construction of a hierarchy of topics in biomedical research. In the topic hierarchy, topics are analyzed for preserving neighboring information of relationships that are relevant in a given context (topic) in a heterogeneous information network. The topic models based on the predicates (relations) and their neighborhood patterns are defined as a graph in different levels of abstraction. We first rationalize a predicate-centric model Cross Domain Neighborhood Patterns (CDNP) that specifies high connectivity on the RDF/OWL graph for information sharing and integration. Second, we define the association measurement between predicates used in the CDNP patterns in the network. Third, we present the Predicate-based Hierarchical Agglomerative Clustering (PHAL) algorithm to cluster the heterogeneous information network based on the CDNP patterns.

Cross Domain Neighborhood Patterns (CDNP)

In the MedKDD framework, the knowledge model is defined by levels of abstraction: (i) the smallest component is a predicate (relation) from a heterogeneous information network (RDF/OWL graphs), (ii) the intermediate component is a pattern that is defined by groups of predicates, (iii) at a higher abstraction level, a topic can be discovered from groups of patterns, and (iv) the highest level of abstraction that can be presented as an analytical view of multiple ontologies (cross domains). The relationships of domains can be determined from a comprehensive analysis of the discovered topics and patterns of predicates.

As the predicates define the relationships between subjects and objects, it is interesting to see that the relationships among subjects and objects are nicely defined through patterns and topics. In this paper, we define the Cross Domain Neighborhood Patterns (CDNP) that describe the association and collaboration among different predicates (relations) and concepts in heterogeneous information networks. In this analysis, only domain specific predicates are considered without considering OWL built-in predicates. There are two types of the CDNP patterns: Cross-Domain Share and Cross-Domain Connectivity.

Definition 1: Cross-Domain Share Pattern This pattern describes the resources sharing relationships between predicates where the resources are concepts from a heterogeneous information network (RDF graphs). Given two triples 〈Si, Pi, Oi〉, 〈Sj, Pj, Oj〉, the conditions of the share pattern were defined as follows: where the logical OR operator (||) returns the Boolean value true if either or both operands is true and returns false otherwise, the logical AND operator (&&) returns the Boolean value true if both operands are true and returns false otherwise. For all (denoted by ∀) Si, for all Pi and for all Oi are in a domain Di and for all Sj, for all Pj, and for all Oj are in a domain Dj, but these two domains Di and Dj are different.

There are three types of Share patterns are defined as follows:

  • The Provider pattern describes the relationship with a pair of predicates sharing a common object, describes the provider role of entity giving information to Consumers. This role has more out-degree edges than in-degree edges.
  • The Consumer pattern describes the relationship with a pair of predicates sharing a common subject, describes the role of entity receiving information from Providers. Consumer has more in-degree edges than out-degree edges.
  • The Reacher pattern describes the relationship with a pair of predicates having a same concept as a subject and object, describes the role connecting the Provider role with the Consumer role.

Fig 1 shows the share patterns such that (a) Provider pattern: the object hv:resource is shared through two predicates pv:x-hgnc and kv:x-hgnc (b) Consumer pattern: the subject SIO_001077:Gene is shared with two predicates mgv:x-ensembl-protein and kv:x-uniprot (c) Reacher pattern: a concept kv:Resource is shared by two predicates dv:x-kegg and kv:pathway.

Fig 1. Cross Domain Share Patterns.

Examples of three share patterns (a) Provider, (b) Consumer, (c) Reacher are shown in this figure. In this diagram, the circle represents a concept and the triangle represents a predicate.

Definition 2: Cross-Domain Connectivity Pattern This pattern describes the connectivity relationships at least three predicates in a heterogeneous information network from different domains. This Connectivity pattern is defined using the Reacher pattern from Definition 1. A subject (Si) in a source domain (Di) is connected to an object (Oi) in a target domain (Dj) through cross-domain connectivity predicates (Pi, PjPc and DiDj). The pattern of the source domain or the target domain is defined as a Reacher pattern. There are two types of the Connectivity pattern: Directional Connector (DC) and Non-Directional Connector (NDC).

  • The DC pattern describes the connectivity pattern considering the direction of the edges between predicates whose distance is higher than equal to 2.
  • The NDC pattern is same with the DC pattern in terms of the predicate collaboration for indirect connectivity, however, the edge directions are not considered in this NDC pattern.

This Connectivity pattern is formally defined as follows: Given a Reacher pattern 〈Ss, Ps, Os〉 and a new triple 〈Si, Pi, Oi〉, the conditions of the connectivity pattern were as follows: where the logical AND operator (&&) returns the Boolean value true if both operands are true and returns false otherwise. For all (denoted by ∀) Ss, for all Ps and for all Os are in a domain Ds and for all Si, for all Pi, and for all Oi are in a domain Di, but these two domains Ds and Di are different.

Fig 2 shows the Connectivity patterns such that the subject and object are connected through three predicates: (a) Directional Connector (DC) among three predicates dv:x-hgnc, hv:x-omim, ommimv:x-mgi (b) Non-Directional Connector (NDC) among three predicates mgv:x-refseq-transcript, ctdv:pathway, and ctdv:disease.

Fig 2. Cross Domain Connectivity Patterns.

Two connectivity patterns (a) Directional Connector (DC) and (b) Non-Directional Connector (NDC) are shown in this figure. In this diagram, the circle represents a concept and the triangle represents a predicate. A color is assigned to each dataset as follows: DrugBank: Red; HGNC: Pink; MGI: Green; PharmGKB: Cyan; ClinicalTrials: Yellow; OMIM: Sky Blue; SIDER: Gray; KEGG: Orange; CTD: Magenta. The prefixes describe the domain of the concepts and predicates. ctdv: dv: ensev: hv: kv: mgv: phv: refv: unv:

Definition 3: Topic The topic describes bounded contexts through association patterns of both shared and connected predicates in a heterogeneous information network. Different topics may have completely different associations among any common predicates or concepts in heterogeneous domains. In a graph to represent the topic (called the topic graph), a group of predicates collaborate each other to share and connect information through the predicates of the CDNP patterns.

Definition 4: Topic Boundary The topic boundary (denoted as B) defines the scope of context in which the information can be associated and shared, and connected in a heterogeneous information network. The association and collaboration of information is described in terms of sets of concepts and relations within the given boundary on the heterogeneous information network.

Boundaries between contexts (topics) can be determined by various factors. Usually the dominant one is strongly associated with others so that this can be measured by high in-degree/out-degree and distance in a heterogeneous information network. This boundary can be set differently depending on the domains of interest. Multiple contexts can be found within the same domain context and similarly a single context can be founded across multiple domains. This paper focuses on the second kind of association.

The cross domain patterns are discovered with the bounded contexts which are a central concept in the knowledge discovery. The clustering technique is applied to partition a large and complex network into multiple smaller topics in the same context in an optimal manner. The bounded contexts are specifically tailored for a set of cross domain patterns. The boundary B is determined based on the distance L (without considering direction) between any two predicates.

Definition 5: Degree of Diversity The degree of diversity is defined to measure the degree of the association between predicates from different ontologies (domains) in a heterogeneous information network. The diversity degree is defined with an optimal weight assigned to links between predicates from different domains.

The weight will be computed to measure the degree of the association between predicates from different domains using the formula in Definition 6. The rationale is to capture diverse relations between predicates from multiple domains by giving a higher weight to the links across domains while giving a lower weight to links in a single domain.

Definition 6: Cross Domain Diversity Weight The weight represents the cross domain connectivity linking between predicates from different domains. This weight is computed based on the neighborhood predicates that are cross domains. For a given topic Ti with an average similarity association score , if a predicate pair {pi, pj} forms a cross domain relationship, i.e., piDi pjDj; DiDj, pi, pjP with an association score wij, we define as a cross domain association weight between predicates pi and pj, such that

Let DW(pi, pj) be the diversity weight between two cross domain predicates pi, pj. Let SW(pi, pj) be the similarity weight between two predicates pi, pj (without considering cross domain) Let be the neighborhood association weight for a given predicate pi (an average association weight with its neighborhood) (1)

In this paper, a threshold heuristic is employed to compute a topic boundary B for given datasets. We are encouraged by results on determining a topic boundary, where a heuristic has been devised increasing diverse association within a single topic on the topic boundary B as 3. The maximum distance between predicates (without considering the direction) in a topic is 3. For the given topic boundary B = 3, as shown in Fig 3, the cross domain diversity weight was computed for predicates P3 and P4 using Eq (1).

Fig 3. Cross Domain Diversity Weighting (Before/After).

The cross domain diversity weight (DW) for the edge between predicate P3 and predicate P4 is computed as 0.35 using Eq (1). SW(P3, P4) = 0.2 and = = 0.5 and = = 0.23. DW(P3, P4) = = = Max(0.35,0.21) = 0.35.

In this paper, we now present the relationships between domains that have been discovered by modeling the predicate neighborhood pattern and conducting the pattern-based topic discovery. Our work is related to the Ontology Alignment defined in [27] as a set of correspondences between two or more ontologies, corresponding relation holding according to a particular matching algorithm with classes, individuals, properties of ontologies.

Definition 7: Domain Association The Domain Association defines the association among domains that depicts a high level of views on cross domain collaboration. Based on the predicate collaboration in the CDNP patterns, the domain association and collaboration model can be defined. For each pattern, the top K predicates are considered to build the domain association model that represents the abstract relationships between these topics.

To describe the relationships between domains, three additional roles such as Bridger, Balancer, and Hub are defined.

  • The Bridger role describes a collaborative relationship among domains in multiple domains and passes along information between them. This role plays a very important role to link two or more domains.
  • The Hub role describes about a center of the domain, called the influential domains, that are strongly connected to other domains.
  • The Balancer role describes the balanced collaboration in terms of receiving and producing information. The pattern can be identified based on the similar in-degree and out-degree edges of domain graphs.

CDNP Association Measurements

We now define the measurement for the Cross Domain Neighborhood Patterns (CDNP) in terms of sets of concepts and relations (predicates) across the multiple domains. For this purpose, we describe how to quantify associations between different predicates across domains. It is based on the CDNP pattern describing the relationships between predicates Pi and Pj through a concept C across domains. The association measurement for the CDNP patterns varies based on different neighboring levels for each pair of predicates. Basically, we give a higher shared score to predicates with more shared concepts and lower scores to predicates with less shared concepts. Similarly, we give a higher connection score to closer predicates and lower scores to further predicates. We formally define the association measurement between predicates for the Cross-Domain Share patterns and Cross-Domain Connectivity patterns.

Definition 8: Association Distance The association distance defines the distance between associated predicates in a heterogeneous information network. Given a directed graph G(C, P), concepts C denote subject S and object O and P predicate in a RDF schema graph, respectively. Let d(Pi, Pj) represent the number of concepts C between Pi and Pj. r(Pi, Pj) determines if a predicate Pi is reachable from another predicate Pj where the domain Di of Pi is not the same from the domain Dj of Pj, i.e., DiDj, without considering the direction of links). l(Pi, Pj) indicates the shortest distance between Pi and Pj. (2)

The direct association describes the direct relationship between Pi and Pj in the distance L = 1 (without considering a direction) that is within the boundary B. The indirect association describes any relationship between Pi and Pj in distance L computed by Eq (2) within the boundary B, i.e., 1 < LB. The share pattern is the directed association while the Connectivity pattern is the indirect association. We now define these two probability based similarity scores: i) [SA](Pi, Pj) is defined a share pattern of any two predicates Pi and Pj ii) [CA](Pi, Pj) for a Connectivity pattern of any two predicates.

Definition 9: Share Association Given predicates Pi and Pj in a directed RDF schema graph G(C, P). Let C(Pi) and C(Pj) denote the entities (subjects or objects) that are directly connected to Pi and Pj regardless of the direction. l(Pi, Pj) is the reachability test for the given predicates Pi, Pj. SA(Pi, Pj) indicates the probability-based association matrix for a share pattern between Pi and Pj. (3)

Definition 10: Connectivity Association For a Connectivity pattern of any two predicates Pi and Pj, CA(Pi, Pj) defines the probability-based association for a Connectivity pattern between Pi and Pj based on the Share Pattern. For the given Share Associations SA(Pi, Pk) and SA(Pk, Pj) and the distance between the predicates l(Pi, Pj), the connectivity association can be computed as follows: (4)

The definition is influenced by the chain matrix multiplication problem (a kind of dynamic programming) of determining the optimal sequence for performing a series of operations. After we get the similarity score for all pairs of predicates, we use the formula in Eqs (3) and (4) to generate a predicate association matrix for clustering.

Definition 11: Predicate Association Matrix Given the total number of predicates n and the probability-based association score for cross domain share patterns SA(Pi, Pj) and Cross Domain Connectivity Patterns CA(Pi, Pj) between predicates Pi and Pj, PA[Pi, Pj] indicates an association matrix for all pairs of predicates Pi and Pj (5)

Predicate-based Hierarchical Agglomerative Clustering

There are various different approaches in clustering heterogeneous information networks. In [28], we designed the Hierarchical Predicate-based K-Means clustering (HPKM) algorithm for discovery of relevant topics from integrated multiple sources and forms a topic hierarchy. The HPKM algorithm is an excellent way to summarize an integrated view of multiple ontologies as shown in Fig 4. However, we observe that HPKM is not suitable for cross domain knowledge discovery from heterogeneous information network. The reason is that the HPKM’s top-down approach focuses on global clustering based on homogeneous perspectives, however, ignoring the diverse and local perspectives of the network.

Fig 4. Top Down Topic Hierarchy Constructed by the Hierarchical Predicate-based K-Means clustering Algorithm.

The top down topic hierarchy with three levels has seven topics at the third level. The number assigned to the edges indicates the distribution of predicates to its child node. The sum of the numbers should be one (e.g., 0.17 + 0.83 at the top level). A color is assigned to each domain as follows: DrugBank: Red; HGNC: Pink; MGI: Green; PharmGKB: Cyan; ClinicalTrials: Yellow; OMIM: Sky Blue; SIDER: Gray; KEGG: Orange; CTD: Magenta.

In this paper, we designed a new algorithm, called the Predicate-based Hierarchical Agglomerative Clustering (PHAL), for topic discovery from the heterogeneous information network of the multiple domains. PHAL is a hierarchical bottom-up clustering algorithm by applying Hierarchical Agglomerative clustering (HAC) [29] to the heterogeneous information network of cross domain ontologies. PHAL is creating a topic hierarchy through the analysis of the patterns quantified by the CDNP association measurement. PHAL starts with each predicate as a singleton cluster and then successively merges pairs of clusters while traversing up through its ancestors in the hierarchy.

Fig 5 shows a topic hierarchy generated from the PHAL algorithm. The PHAL algorithm has four phases as shown below and the pseudo codes are shown in Algorithms 1 and 2.

Fig 5. Bottom Up Topic Hierarchy Constructed by the Predicate-based Hierarchical Agglomerative Clustering Algorithm.

Bottom up topic hierarchy with 43 topics. Topic ID is assigned to each cluster in this hierarchy. A color is assigned to each domain as follows: DrugBank: Red; HGNC: Pink; MGI: Green; PharmGKB: Cyan; ClinicalTrials: Yellow; OMIM: Sky Blue; SIDER: Gray; KEGG: Orange; CTD: Magenta.

Phase 1: Hierarchical Agglomerative Clustering This phase focuses on clustering predicates from the heterogeneous information network of the given datasets using Hierarchical Agglomerative Clustering [29]. This algorithm is a bottom-up approach to build a hierarchy of topics based on the CDNP patterns until all predicates in the network belong to a topic group. The results from this learning process are a set of topics (InitialMap) in a hierarchical structure similar to the topics shown in Fig 5.

Phase 2: Construction of Topics Starting at Level Mid Given the tree from Phase 1, we first compute the mid-level of the tree (i.e., Mid = H/2, where H is the height of the hierarchy generated from Phase 1). The topics at the mid-level Mid are assigned to FinalTopicSet. If there is no topic at the level Mid, then go upward until find any topic groups on the subsequent level of the Mid (i.e., Mid-1) in the hierarchy. Among 43 topics shown in Fig 5, Topics 2-11 are the topic groups captured at the level Mid.

This phase illustrates the constructing process of topics for the remaining topics, which do not belong to the topic groups InitialMap. Starting from the level Mid—1, we start traversing the tree upward to construct topic groups with each topic at the the subsequent level of the Mid level (i.e., Mid—1) and assign it to FinalTopicSet. Repeat this step at Mid—2 until reaching the tree root. In addition, we have made a special topic group (i.e., Topic1) that is a collection of the singleton topics whose size is 1. Topics 12-43 in Fig 5 are newly constructed during this phase.

Phase 3: Hierarchical Topic Refinement There are some cases such that relevant concepts are disconnected. This is due to the hard partition in which a predicate was not allowed to join more than one topic. To handle the issue, a refinement process is conducted to construct a more complete topic model with the respective predicates and their neighborhood. More precisely, for any two pairs of predicates, if they form a Connectivity pattern and then we include their intermediate predicates to the topic and update those topics in FinalTopicSet. From this refinement process, a predicate may join more than one topic group that results into fuzzy clustering.

Algorithm 1 Hierarchical Heterogeneous Clustering

Input: X = {x1, …xn}

Output: Topic Set T = {t1, …, tk}

/* Phase 1: Hierarchical agglomerative clustering

Define level L = 0

 Consider each element in X as a topic, save them in InitialMap with level L = 0

 Put pair 〈L, X〉 to InitialMap

While true do

  if The active set InitialMap only has one item then



   Extract all topics at current level L from InitialMap

   Choose pair p and qX with the best distance computed using formula

   M = M ∪ {p, q} //Save all pairs to set M

   L = L + 1 //Update the level

   for each pair of elements p and q in set M do

    Merge p and q into a new topic u

     Add u to set X

     Update InitialMap with 〈L, X




/* Phase 2: Construction of Topics Starting at Mid Level

Get the tree height L determined from Phase 1

 Compute the middle level of the tree Mid = Roundup (L/2)

 //Construct the topics while traversing the tree upward until it reaches the tree root

while Mid > 0 do

  if There is at least one topic at level Mid of InitialMap then

   Extract topics T = {T1, …, Ti} at level Mid by checking 〈Mid, T〉 from InitialMap

   Define set Z containing all the remaining topics

   //Initializing the topic index

   index = |T| + 2 // Excluding T1 and the initial topics T = {T1, …, Ti}

   FinalTopicSet = FinalTopicSet + T

   for each topic zi in Z do

    if zi.size = 1 then

     Add zi to the special topic Topic1

      Update FinalTopicSet with the special topic Topic1


     Add zi to Topicindex

      Update FinalTopicSet with Topicindex






   Mid = Mid-1



return FinalTopicSet

Algorithm 2 Hierarchical Topic Refinement

Input: FinalTopicSet = {t1, …, tk}

Output: FinalTopicSet = //refined topics with new predicates

/* Phase 3: Hierarchical Topic Refinement

for each topic t in FinalTopicSet do

for any two predicates pi and pj in topic t do

  if pi and pj are connected through a Connectivity pattern & d(pi, pj) = 2 then

   find the intermediate predicate pt between pi and pj

    add predicate pt to topic t


  if pi and pj are connected through a Connectivity pattern & d(pi, pj) = 3 then

   find the two intermediate predicates pm and pn between pi and pj

    add predicate pm to topic t

    add predicate pn to topic t






The MedKDD system was implemented using Java in Eclipse Juno Integrated Development Environment [30]. Apache Jena API [31] was used to analyze multiple ontologies in OWL. We used R computing environment [32] for our experimental validation and implemented a software plugin for query and schema graph visualization using CytoScape 3.0.2 [33]. In addition, we have built a SPARQL query endpoint on a single machine that is hosted at the UMKC Distributed Intelligent Computing (UDIC) lab. The OPEN LINK Virtuoso server version 6.1.3 was installed and the nine domains (ClinicalTrials [18], DrugBank [19], OMIM [20], PharmGKB [21], SIDER [22], KEGG [23], CTD [24], HGNC [25], MGI [26]) were imported into the graph domain The endpoint for SPARQL query services is

Fig 6 shows the MedKDD tool that are designed for browsing the generated topics and performing the interactive query design and processing. The tool shows the list of topics generated from the nine ontologies in OWL. For a selected topic, questions both in free text and SPARQL query format will be automatically generated. The topic graph and query graph can be visualized for the selected query. When the query button is clicked, the SPARQL query will be executed and the query output will be shown in the bottom right box. Then, the corresponding topic graph will be displayed on the canvas in the right panel. Moreover, by clicking the query graph button, the relevant concepts and predicates in the SPARQL query will also be highlighted as seen in Fig 6.

Fig 6. MedKDD Tool: Cross Domain Knowledge Discovery.

The top left panel shows the list of topics in Bio2RDF Topic 15. The right panel shows the visualization of one of the topics discovered from this dataset. The bottom left panel show the SPARQL query for a selected topic and the query results from the execution of the selected query.

Topic Discovery in Cross Domains

For the given nine ontologies in OWL shown in Table 1, we have conducted the pattern analysis for topic discovery. We have computed the rankings of predicates, patterns, and topics discovered from our knowledge discovery process and also summarized the relationships among domains based on the discovered patterns and topics.

  • CDNP Patterns in Topic Discovery An analysis is conducted to gain a better understanding of the CDNP patterns in cross domain topic discovery. Table 2 shows the Cross Domain Neighborhood Patterns (CDNP) discovered from 43 topics: 1676 Provider Patterns, 5953 Consumer Patterns, 3572 Reacher Patterns, 1990 Directional Connector patterns and 14434 Non-Directional Connector patterns. Interestingly, 77% of the CDNP patterns we discovered are cross domain (50% of the Provider patterns, 45% of the Consumer Patterns, 40% of the Reacher Patterns, 100% of the Directional Connector patterns, and 100% of the Non-Directional Connector patterns). The share patterns in the lower level are part of the Connectivity patterns in the higher level. From these results, we confirm that the CDNP patterns play a significant role in integrating data and finding cross domain topics from heterogeneous information networks.
  • Predicate and Concept Ranking: The predicates (the primary component in MedKDD) and their associated concepts are ranked based on their in-degree and out-degree. From this analysis, we found out the roles of ontologies for cross domain collaboration in heterogeneous information networks. Among 374 concepts, top concepts such as clinv:Resource, kv:Resource, dv:Resource, phv:Resource are shown in Fig 7(a). As shown in Fig 7(b), among 330 predicates, top 10 predicates such as dv:source and are from three ontologies such as DrugBank, ClinicalTrials, and PharmGKB. These predicates and concepts are mainly from the primary ontologies including ClinicalTrials, KEGG, DrugBank, and PharmGKB.
  • Cross Domain Predicate and Concept Ranking: The contents of cross domains were ranked based on the in-degree/out-degree of cross domain concepts and predicates. We observed the cross domain rankings with predicates and concepts were different from the non-cross domain rankings. However, the ontologies playing important roles are similar. Fig 8 shows 40 cross domain concepts and predicates. Among them, SIO:Drug, kv:Resource and SIO:Gene are top three cross domain concepts of PharmGKB (SIO normalized), KEGG, and DrugBank (SIO normalized). kv:pathway, and dv:x.kegg are top three cross domain predicates of KEGG, ClinicalTrials, and DrugBank, respectively.
  • Topic Ranking with Cross Domain Features: These patterns are ranked according to primary features such as cross domain predicates, predicate popularity (in-degree/out-degree of the predicates), and domain verity (the number of domains in which the patterns are captured). Fig 9(a) shows top 5 topics (Topic 16, Topic 25, Topic 23, Topic 22 and Topic 26) computed by the cross domain features. Table 3 shows the top 3 predicates and top 2 unique predicates of these topics.
  • Topic Ranking with Cross Domain Neighborhood Patterns: Topics are ranked based on the CDNP patterns. Fig 9(b) shows top five topics (Topic 16, Topic 25, Topic 23, Topic 22 and Topic 26). The ranking based on the counts of the CDNP patterns (Provider, Consumer, Reacher, CD and NCD patterns) is very similar to the ranking computed by the predicate popularity, cross domain predicate, and variety shown in Fig 9(a). This confirms that the proposed pattern-based approach reflects an excellent understanding of the important features of the network such as density, verity, and popularity.

Fig 7. Top Concepts and Predicates.

(a) Top 10 Concepts (b) Top 25 Predicates.

Fig 8. Cross Domain Topic Ranking.

(a) Feature-based Ranking (b) Pattern-Based Ranking. Popularity is measured by In-degree/Out-degree of predicates. Verity is measured by the number of domains involved. The numbers in the bar graph are the topic ID (ranged: 1—43).

Comparative Analysis for Cross Domain Knowledge Discovery

The comparative analysis will provide valuable insight into the effectiveness of the Cross Domain Neighborhood Patterns (CDNP) and the CDNP-based topic discovery model. The evaluation of the proposed model has been conducted using practical examples of the cross domain predicate patterns and topic discovery. We show the patterns are useful in knowledge discovery from multiple ontologies through evaluation and validation of the proposed model compared to other approaches in knowledge discovery from diverse domains.

Comparative Analysis: Top Down Clustering vs. Bottom Up Clustering.

The case studies involve the comparative analysis with the HPKM and PHAL algorithms and experiments with the both algorithms to confirm the effectiveness of the proposed method. For the given nine ontologies shown in Table 1, we have conducted the topic discovery by applying the proposed PHAL algorithm and the HPKM algorithm. As mentioned previously, HPKM is an excellent way to summarize an integrated cross-domain ontologies, as shown in Fig 4. However, HPKM could not capture interesting patterns from heterogeneous information networks of cross domains. From the HPKM analysis in Table 4, only seven coarse grained topics were discovered and two of them are cross domain. It is because predicates from a single domain are strongly related compared to ones from cross domain. From the PHAL analysis in Table 4, we found 43 topics from the heterogeneous information networks of the given cross domains and 93% of the discovered patterns (40 topics are cross domains and 3 topics are single domain) are cross domains. In addition, we computed the average predicate number per topic, the average in-degree and output-degree per topic, the average density per topic and the association score per topic. The density was computed using where N is the number of nodes (concepts and predicates) and E is the number of edges (links between nodes). The association score were computed by the Predicate Association formula Eq (5). Zero is defined as the smallest number. The closer to zero, the smaller it is. The results demonstrate the PHAL algorithm provides superior outcomes compared with HPKM in topic discovery from heterogeneous information networks.

Table 5 shows that there are 330 unique predicates and 275 unique concepts. Interestingly, about 88% of the predicates and 65% of the concepts are cross domain. Fig 7(a) and 7(b) show top 10 concepts and top 25 predicates, respectively. Fig 8(a) and 8(b) show the top 40 cross domain concepts and predicates, respectively. The nine ontologies used in our case study show high potentials to be used for cross-domain analysis and linking for semantic interoperability.

As seen in Table 5, about 26% of concepts (99 out of 374) appear in more than one domain even before the clustering while all 330 predicates are unique (this means each predicate appears in only one domain among 9 domains). Specifically, a generic concept like Resource appears 92 times and pubmed_vocabulary:Resource appears in all 9 domains. This indicates that concepts like Resource are mainly used for a high level mapping between different domains. Thus, these concepts are too abstract to be of practical use of such data. For the data integration, data normalization was performed to map 30 Semanticscience Integrated Ontology (SIO) concepts to domain concepts. In addition, about 45% (149 of 330 predicates) are named with a prefix x. This indicates that the predicates are also too abstract to provide meaningful relationships between concepts. After clustering, the size of predicates became doubled and the concepts quintupled. All the predicates except sider_vocabulary:reported.frequency are fully contributed to the integration of cross domains and discovery of relevant patterns. Through the normalization and clustering, relevant concepts and predicates were integrated and clustered according to their contexts.

Comparative Analysis: MedKDD vs. SLAP.

We have conducted a comparative analysis with the Semantic Link Association Prediction (SLAP) [34] that was designed for detecting drug target association. This experiment was designed to compare between SLAP and MedKDD in terms of their capacity in handling cross topic and cross domain knowledge discovery using the six common datasets of MedKDD and SLAP shown in Table 1. MedKDD has an advanced capability on information retrieval for the relationships between two concepts, e.g., 〈Drug → Gene〉, the relationships among multiple concepts across topics, e.g., 〈Drug → Target → Gene〉, and the relationships across domains (DrugBank and OMIM), e.g., :〈Drug → Target〉 ⇒ OMIM:Uniprot}, where the symbol → represents a path from one concept to another within a single domain and the symbol ⇒ represents a path from one concept to anther across domain. Similarly, SLAP also has the ability to retrieve the information on the association between drugs and targets. However, SLAP does not support the information retrieval for any other association besides the drug and target association.

First, we have conducted several queries that are designed to retrieve the association among the key concepts in DrugBank (i.e., drug, target, gene). In order to demonstrate the knowledge discovery process with multiple datasets, the top five drug instances such as NADH, Beta-D-Glucose, Flavin adenine dinucleotide, Pyridoxal Phosphate, and Citric Acid shown in Table 6 were selected among 6071 possible drug instances in terms of the number of targets and their associated genes. As seen from Table 6, one drug may have m targets and then m targets relate to n genes, where .

Table 6. Top 5 Drug Instances & Cross Topic Query Results of MedKDD & SLAP.

In MedKDD, among 43 topics discovered from the Bio2RDF ontologies, there is a path between Topic 16 and Topic 27 through the common concepts such as dv:Drug(SIO_010038), dv:Target(SIO_010423) and dv:Gene(SIO_001121) as shown in Fig 10. Specifically, the path includes dv:Drug (SIO_010038)dv:targetdv:Target(SIO_010423)dv:x-genecardsdv:Gene(SIO_001121) across these two topics. Table 6 shows the comparative analysis between the MedKDD and the SLAP frameworks in terms of the number of genes detected for top five drug instances. MedKDD could retrieve all the information for the given queries while SLAP retrieved either only partial information or no information at all. We have found that SLAP does not perform well in this experiment. It is because SLAP mainly focuses on the prediction on links between chemical compounds and targets with specific predicates including bind, hasGo, hasSubstructure, hasPathway, hasTissue, and PPI. Thus, some of information could not be retrieved from the query processing. However, the GraphKDD framework does not put any restriction on this query processing so that it has a capability to find any associations for a given query on drug, target, and gene.

Fig 10. The Cross Domain Query Graphs for Topic 16 and Topic 27.

In the cross domain query graph, the circle represents a concept and the triangle represents a predicate. A color is assigned to each domain as follows: DrugBank: Red; HGNC: Pink; MGI: Green; PharmGKB: Cyan; ClinicalTrials: Yellow; OMIM: Sky Blue; SIDER: Gray; KEGG: Orange; CTD: Magenta. The cross domain query graph is composed with the paths between Topic 27 and Topic 16 such as i) {dv:Drug (SIO_010038)dv:targetdv:Target(SIO_010423)dv:x-genecardsdv:Gene(SIO_001121)}; ii){DrugBank:〈dv:Drug (SIO_010038)dv:targetdv:Target(SIO_010423)dv:x-uniprot〉 ⇒ :omimv:Uniprot}; iii) {omimv:Resourceomimv:x-uniprotomimv:Uniprot}.

Second, for cross domain knowledge discovery, top five drug instances (i.e., NADH, L-Glutamic Acid, Pyridoxal Phosphate, Ethanol, and Zonisamide) were also selected according to the number of the instances associated with target, gene and OMIM resource. A cross domain query was designed with the following paths such as i) {dv:Drug (SIO_010038)dv:targetdv:Target(SIO_010423)dv:x-genecardsdv:Gene(SIO_001121)}; ii){DrugBank:〈dv:Drug (SIO_010038)dv:targetdv:Target(SIO_010423)dv:x-uniprot〉 ⇒ :omimv:Uniprot}; iii) {omimv:Resourceomimv:x-uniprotomimv:Uniprot}. Table 7 shows the information retrieval comparison between the MedKDD and the SLAP frameworks in terms of the association with targets, genes, and OMIM resources for the top five drug instances. Fig 11 shows the SPARQL query and query results for the drug instances and their association with target, gene, OMIM resources. Similar to the first case, for the query across two domains DrugBank and OMIM, MedKDD retrieved all the relevant information while SLAP could not retrieve any information except partial information about drug and gene association.

Table 7. Top 5 Drug Instances & Cross Domain Query Results of MedKDD & SLAP.

Fig 11. The Cross Domain SPARQL query and Query Results.

The figure shows the SPARQL query (left) and query results for the cross domain query designed with the paths shown in Fig 10.

In the comparative analysis, we demonstrated MedKDD’s capacity retrieving the association relationships between multiple concepts or predicates ether cross topics within a single domain or across domains. MedKDD shows the 100% accuracy rate in retrieving this information from the topics of nine different domains. Although SLAP proposed a strong statistical model to predict the association between drugs and genes, SLAP has a very limited capacity in retrieving information across topic (drug, target, and gene) or across domains (DrugBank and OMIM). The SLAP prediction of drug and gene interactions was strictly limited to the association between chemical compounds and targets. This result implicates the effectiveness of the MedKDD framework in discovering knowledge even across topics or across domains compared to the cross domain query processing approach, namely SLAP.

Domain Collaboration Patterns in Cross Domains

Based on top five CDNP patterns (Provider, Consumer, Reacher, Directional Connector, Non-Directional Connector), we analyzed the collaboration across domains as shown in Fig 12. Topic graphs are depicted in Fig 13. For each case study, we now show its topic pattern graph of concepts and predicates and the instances of concepts in this topic graph.

Fig 12. Cross Domain Pattern Graphs.

(a) Topic 25: Provider Pattern Graph (b) Topic 15: Consumer Pattern Graph (c) Topic 22: Reacher Pattern Graph (d) Topic 16: DC Pattern Graph (e) Topic 23: NDC Pattern Graph.

Fig 13. Cross Domain Collaboration Graphs.

(a) Domain Provider Pattern (b) Domain Consumer Pattern (c) Domain Reacher Pattern (d) Domain Directional Connector Pattern (e) Domain Non-Directional Connector Pattern. A color is assigned to each domain as follows: DrugBank: Red; HGNC: Pink; MGI: Green; PharmGKB: Cyan; ClinicalTrials: Yellow; OMIM: Sky Blue; SIDER: Gray; KEGG: Orange; CTD: Magenta.

Case 1: Provider Patterns in Domain Collaboration Five domains (DrugBank, PharmGKB, ClinicalTrials, KEGG and CTD) are involved in the collaboration of the Provider pattern. In this collaboration, we found that DrugBank and KEGG are a Provider, CTD is a Balancer, and PharmGKB is a Consumer as well as a Bridger. ClinicalTrials is its Consumer. Fig 12(a) shows a domain collaboration graph for the given Provider pattern. Fig 13(a) shows the provider pattern graph of Topic 25.

Fig 1(a) shows a Provider pattern in Topic 25. This pattern describes the collaboration of two predicates, namely phv:x-hgnc and kv:x-hgnc to integrate information from three domains. Specifically, PharmGKB Resource links to KEGG Gene (SIO normalized) through HGNC Gene symbol. Table 8 shows 5 instances of the concepts in the Provider pattern of Topic 25.

Case 2: Domain Collaboration with Consumer Patterns Five domains, namely KEGG, OMIM, DrugBank, CTD, and PharmGKB, are involved in this case. We found that CTD is a Consumer of KEGG, OMIM and PharmGKB. DrugBank are a Balancer with KEGG. Fig 12(b) shows a domain collaboration graph for the Consumer pattern, CTD. Fig 13(b) shows the Consumer pattern graph of Topic 15.

Fig 1(b) shows a Consumer pattern in Topic 15. This Consumer pattern shows the collaboration between predicates mgv:x-ensembl-protein and kv:x-uniprot as a Consumer of the PharmGKB concept (SIO normalized), SIO_001077:Gene. The collaboration is established across three domains such as KEGG, MGI and PharmGKB. In this pattern, due to the collaboration of these two Consumer predicates, the Uniprot concept Resource is linked to the Ensemble concept Resource through PharmGKB concept Gene (SIO normalized). Table 9 shows 5 instances of the concepts in the Consumer pattern in Topic 15.

Case 3: Domain Collaboration with Reacher Patterns Only two predicates from two domains, namely PharmGKB and ClinicalTrials, are involved in the Reacher pattern. From this pattern analysis, we found that PharmGKB plays a Provider and ClinicalTrials a Consumer from this collaboration. Fig 12(c) shows the domain collaboration with the Reacher pattern between PharmGKB and ClinicalTrials. Fig 13(c) shows the Reacher pattern graph of Topic 22.

Fig 1(c) shows the Reacher patterns in Topic 22. This Reacher pattern was formed with the predicates kv:pathway and dv:x-kegg across four domains (PharmGKB, DrugBank, KEGG, CTD). Through the collaboration of these two predicates in this pattern, the PharmGKB concept Drug (SIO normalized) is linked to the KEGG concept Resource and the KEGG concept Resource is linked to the CTD concept Pathway (SIO normalized). Table 10 shows 5 instances of the concepts in the Reacher pattern of Topic 22.

Case 4: Domain Collaboration with Directional Connector Patterns From the pattern analysis with top 40 predicates, all nine domains have the Directional Connector (DC) patterns. Fig 12(d) shows the domain collaboration through the DC patterns with 54 links among these domains. We have found that CliniclalTrials, DrugBank and SIDER play the role of Provider and CTD, HGNC, KEGG, MGI, OMIM, PharmGKB Consumer. Furthermore, KEGG, PharmGKB, SIDER, HGNC play the role of Bridger. The connection among the domains were established through the Bridger pattern. Fig 13(d) shows the DC pattern graph of Topic 16.

Fig 2(a) shows a DC pattern in Topic 16. In this DC pattern of Topic 16, three predicates such as dv:x-hgnc, hv:x-omim and omimv:x-mgi were used to connect concepts across five domains (KEGG, DrugBank, HGNC, OMIM, MGI). In this pattern, the KEGG concept Enzyme (SIO normalized) links to the HGNC concept Resource. The HGNC concept Resource links to the OMIM concept Resource, and the OMIM concept Resource links to the MGI concept Resource. We found all the paths within the bounded context (the maximum distance between predicates, B = 3) determined by the DC patterns. One of them is the path 〈SIO_010343:Enzymedv:x-hgnchv:Resourcehv:x-omimomimv:Resourceomimv:x-mgimgv:Resource〉. Table 11 shows 5 instances of the concepts in the DC pattern of Topic 16.

Case 5: Domain Collaboration with Non-Directional Connector Patterns In the Non-Directional Connector (NDC) pattern discovery, all the 9 domains are involved. Fig 12(e) shows the ontology collaboration through the NDC patterns. These 9 ontologies are connected with 72 links, which means all of them are fully connected. Interestingly, all of them have the same number of in-degree and out-degree, so that they are well balanced. Thus, no Bridge pattern is required in this collaboration. Fig 13(e) shows the NDC pattern graph of Topic 23.

Fig 2(b) shows a domain collaboration graph generated from the NDC pattern in Topic 23. This NDC pattern is composed with four predicates such as mgv:x-refseq-transcript, ctdv:pathway and ctdv:disease that are used to connect nine different domains (KEGG, DrugBank, MGI, HGNC, SIDER, PharmGKB, ClinicalTrials, OMIM, CTD). Specifically, in this pattern, those three predicates are used to connect six concepts such as KEGG Gene (SIO normalized), Refseq resource, KEGG Resource, CTD Chemical, KEGG Pathway (SIO normalized) and CTD Chemical-disease-association. Table 12 shows 5 instances of the NDC pattern in Topic 23.

Case 6: Domain Collaboration with Topics The 43 topics discovered from 9 domains are shown in Fig 14. First, we present how the 43 topics are composed with the concepts and predicates from these domains. Topic 16, Topic 23, Topic 25 are the most diverse topics whose concepts and predicates are from all 9 different domains. On the other hand, Topic 4, Topic 10 and Topic 17 are from a single domain, DrugBank, MGI, SIDER, respectively. Second, the number of topics that were discovered from KEGG, DrugBank, ClinicalTrials is 35, 29, 28, respectively. They are three highest topic numbers among 9 domains. On the other hand, CTD and HGNC contain less than 10 topics, which are two smallest ones. In terms of the size of these domains, ClinicalTrials, DrugBank and KEGG are the top three biggest ones while HGNC and CTD are the bottom two smallest ones. Thus, we found that the size of domains (specially, the number of predicates) is strongly related to the number of topics in these domains.

Fig 14. 43 Topics Discovered from 9 Medical Domains.

40 Cross Domain Topics and 3 Single Domain Topics.


Knowledge Discovery with Heterogeneous Medical Data

There are many efforts that have been made for semantic annotation of heterogeneous data and perform knowledge discovery on biomedical data [3437]. Most of these work have mainly focused on building or using ontologies for data normalization, connecting, and reasoning. Chen et al. [34] annotated different domains into a single ontology and provided an approach to find existing links between existing sources and targets as well as predict missing links between potential sources and targets. Data normalization and data integration platforms have been built for single domain and cross domain knowledge discovery. For the purpose, some medical ontologies are introduced, namely Bio2RDF (Linked Data for the Life Sciences) [6], TMO (Translational Medicine Ontology) [38], Chem2Bio2RDF (Linked Open Data Portal for Chemical Biology) [39], SIO (Semanticscience Integrated Ontology) [40], ATC (Anatomical Therapeutic Chemical) and DrugBank [41], Chem2Bio2OWL(Ontology for Chemogenomics/Systems Chemical Biology) [42], LLD (Linked Life Data) [43], LODD (Linked open drug data) [44] and LinkedCT (A Linked Data Space for Clinical Trials) [45].

We now discuss existing work on knowledge discovery. For relation extraction, ontologies are helpful for extraction of relations in the form of thesaurus, dictionary, or general corpus [46], for extraction of semantic knowledge of relations based on Metathesaurus and Semantic Network of UMLS [47], and for semantic search indexes [48]. Semantic rules have been applied to extract relations from publications [49]. Relations can also be extracted based on specific patterns such as protein-to-protein relations [50], gene-disorder association [51], and diseases and drugs [52]. Shotton et al. [53] presented semantic enhancement methods through citation context and semantically relations for biomedical research articles on tropical diseases.

A variety of research have been conducted in systematical and computational knowledge discovery with cross domain datasets. HeteSim is a general framework that was designed for relationship discovery and linking detection from heterogeneous networks [54, 55]. The iPHACE framework was designed to extract knowledge between drug-target interaction [56]. ChemProt [57] provided a database to discover relationships between disease and chemical biology. STITCH 3 [58] performed knowledge discovery between chemicals and proteins. Oprea et al. [59] built an integrated platform of drugs, targets, and clinical outcomes for supporting Drug repositioning. Kinnings et al. [60] discovered relationship between drug and disease by deploying chemical and systems biology. In [61] an ontology of chemical information entities was developed for the integration of calculated properties of chemical entities within a semantic web context. Campillos et al. [62] identified drug target by using side-effect similarity and then found the association among drug, target, and side effect. Connectivity Map [63] was designed to use gene-expression signatures in discovery of relationships among small molecules, disease, gene, and drug.

However, our approach is different in that, first of all, we focus on a more general approach for graph structural pattern analysis and topic discovery from heterogeneous information networks. In addition, we have combined an unsupervised learning algorithm with a pattern discovery technique to provide a more systematic way of knowledge discovery from multiple domains.

Ontology Mapping and Alignment

Our approach for finding roles in ontology collaboration is related to existing work in ontology matching, alignment, classification and mapping [27]. Ontology mappings and aliments are essential in advanced semantic searchesand reasoning over integrated ontologies [64]. Recent work on ontology alignment have emphasized the importance of attributes in mapping between source and target concepts as well as the role played by the neighborhood of a concept [65, 66]. Specifically, [65] are interested in the identification of evolving mapping among multiple ontologies, characterizing their evolution as well as facilitating the impacted mappings. Similarity measures were defined for identification of relevant attributes for the mappings [66]. A semantic analysis for understanding the meaning of data has been achieved through mappings and alignments in biomedical systems [67]. The proposed approach in this paper would be effective in the analysis of collaboration between ontologies and their roles. This analysis will be useful to identify potential candidates for mappings and alignments that guarantee a consistent integration of models and interoperability for biomedical applications.

Pattern-based Analysis

Pattern based knowledge analysis has been conducted in many aspects of biomedical research. van Leeuwen [68] proposed an interactive way to mine data by applying pattern-based mining method. Warrender and Lord [69] proposed an axiom based pattern driven approach in biomedical ontology engineering. Wang et al. [70] designed a biomedical pattern discovery algorithm based on a supervised learning approach. Rafiq et al. [71] developed an algorithm to discover temporal patterns in genomic databases. In [72], Gotz presented a method for data mining and visual analysis on clinical event patterns using electronic health record data. WHIDE was proposed for co-location pattern mining in multivariate bioimages [73]. Huang et al. [74] presented a clinical pathway pattern discovery method by using probabilistic topic models. Lasko et al. [75] proposed an unsupervised learning method for computational phenotype pattern discovery using clinical data. These works were different from ours because the discovered patterns in our approach were further analyzed for transforming to topics by clustering and ranking, and then represented in a hierarchical manner.

Our work is motivated by previous work that emphasised the importance of ontological relations. Tartir et al. [76] pointed out that there are numerous meaningful relations other than class-subclass relations that would be useful for understanding the ontologies. Shi et al. [77] provided a predicate oriented path finding approach by analyzing facts in large knowledge graphs. VEPathCluster [78] proposed a combination of vertex-centric and edge-centric approach for meta path graph analysis for enhancement of clustering quality of cross domain datasets. Sabou et al. [79] considered ontological relations to be the primary criterion for the summary extraction of ontologies, in which a relatively small number of concepts typically have a high degree of connectivity through hops. Pesquita et al. [80] proposed classification according diverse strategies suing different semantic similarity measures such as node-based/edge-based and pair-wise/group-wise.

In our study, we hypothesize that an association measurement based on predicate neighborhood patterns would be more effective in finding relevant information than a concept-based measurement. Our approach defined a new model of predicate-based patterns and neighboring closeness for an automatic knowledge discovery. In this paper, we fully focus on the discovery of cross domain patterns from the heterogeneous information network representing different types of objects and links in multiple biological ontologies. The MedKDD framework was designed to effectively discover topics from multiple ontologies by partition them into smaller topic graphs and constructing a topic hierarchy. The topic hierarchy was constructed based on the analysis of the discovered patterns and participating graphs into smaller sub-graphs. To our knowledge, there is no existing work that aim to discover cross domain topics based on predicate-oriented neighborhoods patterns discovered from multiple ontologies and use the discovered topics for knowledge discovery across domains.


In this paper, we presented the MedKDD framework for knowledge discovery and semantic interoperability through the discovery of the Cross Domain Neighborhood Patterns (CDNP) from the heterogeneous information network of the multiple medical ontologies. In MedKDD, we developed the bottom-up hierarchical clustering (HPAL) algorithm and discovered cross domain topics from the given multiple ontologies. We demonstrated that cross domain cohesive topics can be dynamically discovered from heterogeneous information networks of multiple ontologies and used for cross domain knowledge discovery. The MedKDD framework was evaluated using a case study with nine ontologies of Bio2RDF and compared with the cross domain query processing approach, namely SLAP. Overall, the experimental results confirm that the MedKDD framework is effective in the cross domain knowledge discovery from heterogeneous information networks of multiple ontologies.

Future work will include the development of Apache Spark framework that is an extension of Hadoop for parallel and distributed knowledge discovery processing from heterogeneous information network [81]. For the assertion retrieval and clustering, we will explore existing parallel and distributed approaches such as the NIMBLE project [82], Apache Mahout library, and the Distributed Co-clustering (DisCo) framework [83] that have been used successfully in diverse applications for extremely large datasets.

Author Contributions

  1. Conceived and designed the experiments: FS YL.
  2. Performed the experiments: FS YL.
  3. Analyzed the data: FS YL.
  4. Contributed reagents/materials/analysis tools: FS YL.
  5. Wrote the paper: FS YL.


  1. 1. Sun Y, Han J. Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explorations. 2012;14(2):20–28.
  2. 2. Linked Open Data Project. Available: Accessed 2 August 2016.
  3. 3. Resource Description Framework. Available: Accessed 2 August 2016.
  4. 4. SPARQL Protocol and RDF Query Language. Available: Accessed 2 August 2016.
  5. 5. Ji Q, Gao Z, Huang Z. Reasoning with noisy semantic data. The Semantic Web: Research and Applications. Springer Berlin Heidelberg. 2011 May;497–502.
  6. 6. Belleau F, Nolin MA, Tourigny N, Rigault P, Morissette J. Bio2RDF: Towards a mashup to build bioinformatics knowledge systems. Journal of Biomedical Informatics. 2008;41:706–716. pmid:18472304
  7. 7. Demartin G, Denoye L, Douce A, Fachry KN, Gallinar P, Gev S, et al. Report on INEX 2008. ACM SIGIR Forum. 2009 Jun;43(1):17–36.
  8. 8. Ivanović M, Budimac Z. An overview of ontologies and data resources in medical domains. Expert Systems with Applications. 2014;41(11):5158–5166.
  9. 9. Jain AK. Data clustering: 50 years beyond K-means. Pattern recognition letters. 2010 Jun;31(8):651–666.
  10. 10. Dumontier M, Wild DJ. Linked data in drug discovery. IEEE Internet Computing. 2012 Nov;16(6):68–71.
  11. 11. Williams AJ, Harland L, Groth P, Pettifer S, Chichester C, Willighagen EL, et al. Open PHACTS: Semantic interoperability for drug discovery. Drug Discovery Today. 2012 Nov;17(21-22):1188–1198. pmid:22683805
  12. 12. Baorto D, Li L, Cimino JJ. Practical experience with the maintenance and auditing of a large medical ontology. Journal of biomedical informatics. 2009 Jun;42(3):494–503. pmid:19285569
  13. 13. Ziegler P, Dittrich KR. Three decades of data integration—all problems solved? R. Jacquart (Ed.), IFIP Congress Topical Sessions, Kluwer. 2004;3–12.
  14. 14. Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, et al. BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic acids research. 2011 Jul;39(2):541–545.
  15. 15. Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature biotechnology. 2007 Nov;25(11):1251–1255. pmid:17989687
  16. 16. Gombocz E. Semantic cross-domain integration: The intersection of research, public, and clinical data; creating applicable knowledge for decision support in patient-centric healthcare. NCBO Webinar Series. 2011 May.
  17. 17. Bio2RDF: Linked Data for the Life Sciences. Available: Accessed 2 August 2016.
  18. 18. ClinicalTrials. Available: Accessed 2 August 2016.
  19. 19. DrugBank. Available: Accessed 2 August 2016.
  20. 20. OMIM. Available: Accessed 2 August 2016.
  21. 21. PharmGKB. Available: Accessed 2 August 2016.
  22. 22. SIDER. Available: Accessed 2 August 2016.
  23. 23. KEGG. Available: Accessed 2 August 2016.
  24. 24. CTD. Available: Accessed 2 August 2016.
  25. 25. HGNC. Available: Accessed 2 August 2016.
  26. 26. MGI. Available: Accessed 2 August 2016.
  27. 27. Euzenat J, Shvaiko P. Ontology matching. Springer-Verlag. 2007 Jul.
  28. 28. Shen F. A Graph Analytics Framework For Knowledge Discovery. Ph.D. Dissertation, University of Missouri—Kansas City. 2016. Available at Accessed 2 August 2016.
  29. 29. Zepeda-Mendoza ML, Resendis-Antonio O. Hierarchical agglomerative clustering. Encyclopedia of Systems Biology. Springer New York. 2013;886–887.
  30. 30. Eclipse Juno Integrated Development Environment. Available: Accessed 2 August 2016.
  31. 31. McBride B. Jena: Implementing the rdf model and syntax specification. In Proceedings of the Second International Conference on Semantic Web. 2001 May;40:23–28.
  32. 32. The R Project for Statistic. Available: Accessed 2 August 2016.
  33. 33. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003 Nov;13(11):2498–2504. pmid:14597658
  34. 34. Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLoS Comput Biol. 2012 Jul;8(7):e1002574. pmid:22859915
  35. 35. Doing-Harris K, Livnat Y, Meystre S. Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. Journal of biomedical semantics. 2015 Apr;6(1):1.
  36. 36. Mate S, Köpcke F, Toddenroth D, Martin M, Prokosch HU, Bürkle T, et al. Ontology-based data integration between clinical and research systems. PloS one. 2015 Jan;10(1):e0116656. pmid:25588043
  37. 37. Willighagen EL, Waagmeester A, Spjuth O, Ansell P, Williams AJ, Tkachenko V, et al. The ChEMBL database as linked open data. Journal of cheminformatics. 2013 May;5(1):1–12.
  38. 38. Luciano JS, Andersson B, Batchelor C, Bodenreider O, Clark T, Denney CK, et al. The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside. J. Biomedical Semantics. 2011 May;2(S-2):S1.
  39. 39. Chen B, Dong X, Jiao D, Wang H, Zhu Q, Ding Y. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC bioinformatics. 2010 May;11(1):255. pmid:20478034
  40. 40. Dumontier M, Baker CJ, Baran J, Callahan A, Chepelev L, Cruz-Toledo J, et al. The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery. J. Biomedical Semantics. 2014 Mar;5(1):1.
  41. 41. Croset S, Hoehndorf R, Rebholz-Schuhmann D. Integration of the anatomical therapeutic chemical classification system and drugbank using owl and text-mining. GI Workgroup Ontologies in Biomedicine and Life Sciences (OBML). 2012 Sep.
  42. 42. Chen B, Ding Y, Wild DJ. Improving integrative searching of systems chemical biology data using semantic annotation. J. Cheminformatics. 2012 Mar;4(1):1.
  43. 43. Momtchev V, Peychev D, Primov T, Georgiev G. Expanding the pathway and interaction knowledge in linked life data. Proc. of International Semantic Web Challenge. 2009 Oct.
  44. 44. Samwald M, Jentzsch A, Bouton C, Kalles∅e CS, Willighagen E, Hajagos J, et al. Linked open drug data for pharmaceutical research and development. Journal of cheminformatics. 2011 May;3(1):19. pmid:21575203
  45. 45. Hassanzadeh O, Kementsietsidis A, Lim L, Miller RJ, Wang M. Linkedct: A linked data space for clinical trials. arXiv preprint. arXiv:0908.0567. 2009 Aug.
  46. 46. Drouin P. Detection of Domain Specific Terminology Using Corpora Comparison. LREC. 2004 May.
  47. 47. Maynard D, Ananiadou S. Identifying terms by their family and friends. Proceedings of the 18th conference on Computational linguistics-Volume 1. Association for Computational Linguistics. 2000 Jun;530–536
  48. 48. Jonquet C, Musen MA, Shah NH. Building a biomedical ontology recommender web service. Journal of biomedical semantics. 2010 Jun;1(1):1.
  49. 49. Friedman C, Kra P, Yu H, Krauthammer M, Rzhetsky A. GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics. 2001 Jun;17(1):74–82.
  50. 50. Fundel K, Küffner R, Zimmer R. RelEx—Relation extraction using dependency parse trees. Bioinformatics. 2007 Feb;23(3):365–371. pmid:17142812
  51. 51. Ray S, Craven M. Representing sentence structure in hidden Markov models for information extraction. International Joint Conference on Artificial Intelligence. LAWRENCE ERLBAUM ASSOCIATES LTD. 2001 Aug;17(1):1273–1279.
  52. 52. Srinivasan P, Rindflesch T. Exploring text mining from MEDLINE. Proceedings of the AMIA Symposium. American Medical Informatics Association. 2002;722
  53. 53. Shotton D, Portwin K, Klyne G, Miles A. Adventures in semantic publishing: exemplar semantic enhancements of a research article. PLoS Comput 2009 Apr;5(4):e1000361.
  54. 54. Shi C, Kong X, Huang Y, Philip SY, Wu B. Hetesim: A general framework for relevance measure in heterogeneous networks. Knowledge and Data Engineering, IEEE Transactions on. 2014 Oct;26(10):2479–2492.
  55. 55. Kong X, Zhang J, Yu PS. Inferring anchor links across multiple heterogeneous social networks. Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013 Oct;179–188.
  56. 56. Garcia-Serna R, Ursu O, Oprea TI, Mestres J. iPHACE: integrative navigation in pharmacological space. Bioinformatics. 2010 Apr;26(7):985–986. pmid:20156991
  57. 57. Taboureau O, Nielsen SK, Audouze K, Weinhold N, Edsgärd D, Roque FS, et al. ChemProt: a disease chemical biology database. Nucleic acids research. 2011 Jan;39(1):367–372.
  58. 58. Kuhn M, Szklarczyk D, Franceschini A, Von Mering C, Jensen LJ, Bork P. STITCH 3: zooming in on protein—chemical interactions. Nucleic acids research. 2012 Jan;40(1):876–880.
  59. 59. Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O, Mathias SL, et al. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing. Molecular informatics. 2011 Mar;30(2-3):100–111. pmid:22287994
  60. 60. Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, Bourne PE. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol. 2009 Jul;5(7):e1000423. pmid:19578428
  61. 61. Hastings J, Chepelev L, Willighagen E, Adams N, Steinbeck C, Dumontier M. The chemical information ontology: provenance and disambiguation for chemical data on the biological semantic web. PLoS One. 2011 Oct;6(0):e25513. pmid:21991315
  62. 62. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science. 2008 Jul;321(5886):263–266. pmid:18621671
  63. 63. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. science. 2006 Sep;313(5795):1929–1935. pmid:17008526
  64. 64. Kitamura Y, Segawa S, Sasajima M, Tarumi S, Mizoguchi R. Deep semantic mapping between functional taxonomies for interoperable semantic search. In Asian Semantic Web Conference. Springer Berlin Heidelberg. 2008 Dec;137–151
  65. 65. Dos Reis JC, Pruski C, Reynaud-Delaître C. State-of-the-art on mapping maintenance and challenges towards a fully automatic approach. Expert Systems with Applications. 2015 Feb;42(3):1465–1478.
  66. 66. Dinh D, Dos Reis JC, Pruski C, Da Silveira M, Reynaud-Delaître C. Identifying change patterns of concept attributes in ontology evolution. The Semantic Web: Trends and Challenges. Springer International Publishing. 2014 May;768–783.
  67. 67. Lambrix P, Strömbäck L, Tan H. Information integration in bioinformatics with ontologies and standards. In Semantic techniques for the web. Springer Berlin Heidelberg. 2009;343–376.
  68. 68. Van Leeuwen M. Interactive data exploration using pattern mining. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Springer Berlin Heidelberg. 2014;169–182.
  69. 69. Warrender JD, Lord P. A pattern-driven approach to biomedical ontology engineering. arXiv preprint. 2013 Dec;arXiv:1312 (0465).
  70. 70. Wang H, Azuaje F, Black N. An integrative and interactive framework for improving biomedical pattern discovery and visualization. IEEE Transactions on Information Technology in Biomedicine. 2004 Mar;8(1):16–27. pmid:15055798
  71. 71. Rafiq MI, O’Connor MJ, Das AK. Computational method for temporal pattern discovery in biomedical genomic databases. In 2005 IEEE Computational Systems Bioinformatics Conference (CSB’05). 2005 Aug;362–365
  72. 72. Gotz D, Wang F, Perer A. A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. Journal of biomedical informatics. 2014 Apr;48:148–159. pmid:24486355
  73. 73. Kölling J, Langenkämper D, Abouna S, Khan M, Nattkemper TW. WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages. Bioinformatics. 2012 Apr;28(8):1143–1150.
  74. 74. Huang Z, Dong W, Ji L, Gan C, Lu X, Duan H. Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of biomedical informatics. 2014 Feb;47:39–57. pmid:24076435
  75. 75. Lasko TA, Denny JC, Levy MA. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one. 2013 Jun;8(6):e66341. pmid:23826094
  76. 76. Tartir S, Arpinar IB, Sheth AP. Ontological evaluation and validation. Theory and Applications of Ontology: Computer Applications. Springer Netherlands. 2010;115–130.
  77. 77. Shi B, Weninger T. Fact Checking in Large Knowledge Graphs-A Discriminative Predicate Path Mining Approach. arXiv preprint. 2015 Oct;arXiv:1510(05911).
  78. 78. Zhou Y, Liu L, Buttler D. Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015 Aug;1563–1572
  79. 79. Sabou M, d’Aquin M, Motta E. Exploring the semantic web as background knowledge for ontology matching. Journal on data semantics XI. Springer Berlin Heidelberg. 2008;156–190.
  80. 80. Pesquita C, Faria D, Falcao AO, Lord P, Couto FM. Semantic similarity in biomedical ontologies. PLoS Comput Biol. 2009 Jul;5(7):e1000443. pmid:19649320
  81. 81. Wang P, Xu B, Wu Y, Zhou X. Link prediction in social networks: the state-of-the-art. Science China Information Sciences. 2015 Jan;58(1):1–38.
  82. 82. Ghoting A, Krishnamurthy R, Pednault E, Reinwald B, Sindhwani V, Tatikonda S, et al. SystemML: Declarative machine learning on MapReduce. In 2011 IEEE 27th International Conference on Data Engineering. 2011 Apr;231–242.
  83. 83. Papadimitriou S, Sun J. Disco: Distributed co-clustering with map-reduce: A case study towards petabyte-scale end-to-end mining. In 2008 Eighth IEEE International Conference on Data Mining. 2008 Dec;512–521.