K-MORPH: Knowledge Morphing via Reconciliation of Contextualized Sub-ontologies Sajjad Hussain NICHE Research Group, Faculty of Computer Science, Dahousie University, Canada Abstract. A knowledge-intensive problem is often not solved by an indi- vidual knowledge source; rather the solution needs to draw upon multi- ple, and even heterogeneous, knowledge sources. The synthesis of mul- tiple knowledge sources to derive a ‘comprehensive’ knowledge source is a non-trivial problem. We discuss the need of knowledge morphing, and propose a Semantic Web based framework K-MORPH for deriving a context-driven integration of multiple knowledge sources. We demon- strate the working of K-MORPH by merging contextualized sub-ontologies from three different prostate cancer pathway ontologies for the problem- context therapeutic decision support. 1 Introduction Knowledge-driven problem-solving demands ‘complete’ knowledge about the domain and its interpretation under different contexts. Since the availability of ‘complete’ knowledge is always challenging, therefore problem-solvers tend to manually integrate knowledge from multiple sources to formulate a com- prehensive knowledge object that satisfies the problem’s context. Knowledge morphing aims to formulate a comprehensive knowledge object, specific to a given context, through “the intelligent and autonomous fusion/integration of contextually, conceptually and functionally related knowledge objects that may exist in different representation modalities and formalisms, in order to establish a comprehensive, multi-faceted and networked view of all knowledge pertain- ing to a domain-specific problem”–Abidi 2005 [1]. The knowledge morphing approach extends the traditional notion of knowledge integration by providing the ability to ‘reason’ over the morphed knowledge to (a) infer new knowledge, (b) test hypotheses, (c) suggest recommendations and actions, and (d) query rules to prove problem-specific assertions or theorems. 2 K-MORPH: Solution Approach The need for knowledge morphing is motivated by the realization that integra- tion of knowledge sources should be driven by the problem-context–i.e. select and integrate only those knowledge fragments (within a knowledge source) that are relevant to the problem-context, as opposed to integrating the entire knowledge source. A well-defined problem-context, therefore, determines the scope of knowledge that is pertinent to the problem. For instance, in the do- main of healthcare, clinical guidelines incorporate broad knowledge about the diagnosis, treatment, prognosis and follow-up care for a particular disease. For the context of therapeutic decision support one needs only therapeutic knowledge, which should be selected from multiple guidelines to formulate comprehensive therapeutic knowledge. We argue that the integration of the entire knowledge source exacerbates complexity of establishing knowledge interoperability be- tween multiple knowledge sources. Therefore, in our approach, we define a Semantic Web based Knowledge Morphing Framework K-MORPH to pursue knowledge morphing through reconciliation of ontology-encoded knowledge sources via following main tasks: – Task # 1: Extracting contextualized sub-ontologies based on a given problem- context. – Task # 2: Merging contextualized sub-ontologies based on identified/context- specific alignments to generate a morphed ontology. – Task # 3: Detecting and resolving inconsistencies in the morphed ontology. 3 Related Work By modeling knowledge sources as ontologies, semantic interoperability among knowledge sources can be achieved via ontology reconciliation. However, ontol- ogy reconciliation under different contexts is still an undertaking challenge [2]. The literature suggests other approaches for knowledge integration problem from different perspectives. For instance, the ECOIN framework performs se- mantic reconciliation of independent data sources, under a defined context, by defining conversion functions between contexts as a network [3]. ECOIN exploits the modifiers and conversion functions, to enable context mediation between data sources, and reconcile and integrate source schemas with respect to their con- ceptual specializations. Another initiative towards knowledge integration is re- ported in Mao et. al. [4] that use a Semantic Web framework, and propose an agent-oriented architecture that adopts a local sub-ontology evolution mecha- nism for dynamic self-organization of domain knowledge to support intelligent and efficient planning for problem solving in a distributed environment. Fur- ther more, Kang et. al. [5] also propose a Semantic Web based framework for (i) extracting sub-ontologies based on the user demands (i.e. problem-context in K-MORPH); and (ii) integrating extracted sub-ontologies into the ontology of the user demand (i.e. morphed ontology in K-MORPH). Their approach is similar to K-MORPH. However it is still a proposal, and no concrete re- sults have been presented by them. A comparison of the above mentioned ap- proaches in terms of the proposed tasks of K-MORPH is shown as follows: Table 3.1: K-MORPH: Comparison with the State-of-Art K-MORPH Tasks ECOIN [3] Mao et. al. [4] Kang et. al. [5] Task #1 N Y Y Task #2 Y Y Y Task #3 N N N 4 K-MORPH Framework We adopt a Semantic Web (SW) architecture to address the problem of knowl- edge morphing by (a) extracting contextualized sub-ontologies for the problem- context at hand; (b) merging contextualized sub-ontologies; and (c) detecting and resolving inconsistencies in the morphed ontology. Main tasks of K-MORPH framework are briefly described in the following sub-sections. Task # 1: Extracting Contextualized Sub-ontologies Ontologies and contexts are used to model a domain with different views. On- tologies define a shared model that provides a global perspective, whereas con- texts are used to realize a local aspect of a domain. A contextualized ontology deals with an adaptation of its ontology model to support a local view, and pro- vides (i) a specific interpretation of its ontology concepts; and (ii) an implemen- tation of its procedural knowledge that can be applied in a particular context [6]. In K-MORPH, from the given ontologies, the user identifies a set of con- cepts (and roles) that can be pertinent to the problem-context at hand. Based on those user-selected concepts (and roles), we apply our rule-based extraction method to extract a contextualized sub-ontology (i.e. a RDF-Sub-Graph) com- prised of triples that correspond to the axioms and assertions for (i) the user- selected concept C (ii) individuals for C; (iii) roles in C; (iv) range-concepts for the roles in C; (v) sub-concepts of C; (vi) equivalent-concepts for C; (vii) restric- tions on C; (viii) complex concepts that are composed of C; (ix) only the roles of the super-concepts that are also associated with C, and (x) super-concepts of C as an RDF Blank-Nodes. Our approach for extracting contextualized sub- ontologies is described in details in [7]. Task # 2: Merging Contextualized Sub-ontologies Matching and alignment of ontologies have been carried out based on their lexi- cal, conceptual and structural similarities [2]. When dealing with structural sim- ilarities, similarity scores between ontology-entities can be further improved based on the similarities between their structurally connected entities [2]. We believe such alignments can became more ’trustworthy’ by finding similarities among entities that are driven from the underlying ontology axioms or asser- tions. Therefore we propose an ontology matching approach proof-level ontology matching (POM). Let two source ontologies O1 , O2 and a similarity matrix M such that Mij = hei , ej , si, where s is a similarity score between entities ei and ej from O1 and O2 respectively. Given two ontology-entities e, e′ , we write e ⊢ e′ , if there exists an axiom or assertion of e that derives an axiom or asser- tion of e′ . Given similarity matrix M, we calculate a similarity score between entities fi and fj , based on the similarity score ∀Mij = hei , ej , si such that ei ⊢ fi , ej ⊢ fj . Therefore, POM finds alignments not only based on structural similarities but also takes into account (similarities between) other deductively connected entities. we are currently working on our POM method, and will report further in up-coming publications. Task # 3: Detecting and Resolving Inconsistencies Inconsistencies–whether they occur during the process of ontology evolution or being produced during the ontology reconciliation process–result a poten- tial harm to an ontology structure, and decrease credibility in representing a consistent and shared vocabulary of an underlying domain. When an incon- sistency occurs, there are mainly two ways to deal with it: either resolve it, or reason with the inconsistent ontology [8]. We propose an approach for de- tecting and resolving inconsistencies. Our inconsistency detection deals with defined Integrity Constraint Rules RI of the form C1 ∧ C2 ∧ · · · ∧ Cn ⇒ ⊥. An inconsistent derivation consists of derivation steps that lead to ⊥ under RI . Our approach checks whether a given ontology O is inconsistent or not; by finding inconsistent derivations. For resolving inconsistencies, we generate sets of as- serted ancestors A1 , . . . , An , where removal of any Ai (and all derived triples of Ai ) resolves detected inconsistencies and results in a maximal consistent sub- ontology. We have implemented our approach in Euler [9]. Fig. 1. Morphed Knowledge about PC-Halifax:ActiveSurveillance 5 Experiment and Results In order to demonstrate the working of K-MORPH, we have developed a PC Test-case that uses three medical ontologies that describe the PC pathways for three different locations (Halifax, Calgary, and Winnipeg) [10]. Based on the problem-context therapeutic decision support, two concepts Treatment and Clini- cian were selected by the user. Based on the context-specific selected concepts, three contextualized sub-ontologies were extracted from the PC pathway on- tologies. Using the pre-defined (context-specific) correspondences, alignments were found between classes–including their properties and instances. Based on the identified alignments, therapeutic decision support context-axioms and PC domain-axioms, extracted contextualized sub-ontologies were then merged to generate possible ‘knowledge-links’ between the aligned PC treatments. Dur- ing the morphing process no inconsistencies were found, and finally the mor- phed ontology was generated. Figure 1 shows some of the exemplar results. In figure 1, the merged knowledge has determined that the treatment Active Surveillance in Halifax (represented by the instance PC-Halifax:ActiveSurveillance) can be conducted by a Primary Urologist. In the actual pathway, this information was not available for Halifax; but due to the ontology alignments this task was found similar to one in Calgary, and the actor performing this task in Calgary was extended to Halifax. 6 Conclusion Optimal and complete decision support needs a comprehensive knowledge- base. Developing such a self-contained knowledge-base as an independent en- tity is a challenging undertaking. In this paper, we presented our knowledge morphing approach that performs a context-driven integration of knowledge sources to generate a comprehensive knowledge-base for the problem-context at hand. We demonstrated the working K-MORPH by generating a compre- hensive knowledge-base from three different clinical pathways for prostate can- cer, pertaining to a problem-context therapeutic decision support. This research is funded by a grant from Agfa Healthcare (Canada). References 1. 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