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
comprehensive 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
pertaining 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.
The need for knowledge morphing is motivated by the realization that
integration 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
domain 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
between 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
problemcontext.
– Task # 2: Merging contextualized sub-ontologies based on
identified/contextspecific alignments to generate a morphed ontology.
– Task # 3: Detecting and resolving inconsistencies in the morphed ontology.
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Related Work
By modeling knowledge sources as ontologies, semantic interoperability among
knowledge sources can be achieved via ontology reconciliation. However,
ontology 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
semantic 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
conceptual specializations. Another initiative towards knowledge integration is
reported in Mao et. al. [
4
] that use a Semantic Web framework, and propose an
agent-oriented architecture that adopts a local sub-ontology evolution
mechanism for dynamic self-organization of domain knowledge to support intelligent
and efficient planning for problem solving in a distributed environment.
Further 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
results have been presented by them. A comparison of the above mentioned
approaches in terms of the proposed tasks of K-MORPH is shown as follows:
K-MORP H Framework
We adopt a Semantic Web (SW) architecture to address the problem of
knowledge morphing by (a) extracting contextualized sub-ontologies for the
problemcontext 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.
Ontologies define a shared model that provides a global perspective, whereas
contexts 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
provides (i) a specific interpretation of its ontology concepts; and (ii) an
implementation 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
concepts (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)
comprised of triples that correspond to the axioms and assertions for (i) the
userselected 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)
restrictions 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
subontologies is described in details in [
7
].
Task # 2: Merging Contextualized Sub-ontologies
Matching and alignment of ontologies have been carried out based on their
lexical, conceptual and structural similarities [
2
]. When dealing with structural
similarities, 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
assertions. 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
assertion 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
potential harm to an ontology structure, and decrease credibility in representing a
consistent and shared vocabulary of an underlying domain. When an
inconsistency 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
detecting 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
asserted ancestors A1, . . . , An, where removal of any Ai (and all derived triples of
Ai) resolves detected inconsistencies and results in a maximal consistent
subontology. We have implemented our approach in Euler [
9
].
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
Clinician were selected by the user. Based on the context-specific selected concepts,
three contextualized sub-ontologies were extracted from the PC pathway
ontologies. 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.
During the morphing process no inconsistencies were found, and finally the
morphed 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.
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