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Reasoning the FMA Ontologies with TrOWL. Jeff Z. Pan, Yuan Ren , Nophadol Jekjantuk, and Jhonatan Garcia University of Aberdeen, UK ORE2013. The FMA ontology. The Foundational Model of Anatomy ontology is “an evolving computer-based knowledge source for biomedical informatics”
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Reasoning the FMA Ontologies with TrOWL Jeff Z. Pan, Yuan Ren, Nophadol Jekjantuk, and Jhonatan Garcia University of Aberdeen, UK ORE2013
The FMA ontology • The Foundational Model of Anatomy ontology is “an evolving computer-based knowledge source for biomedical informatics” • Developed with Protégé as a FRAME-BASED system • Consists of several components such as Metaknowledge • Evolves (latest version released in 2010) • Highly expressive • Several OWL translations • DLR and FullR: OWL DL/FULL versions without/with metamodelling • Constitutional: alternative OWL DL translation with metamodelling • OWL2G_noMTC: OWL2 translation from FAM 3.0 without metamodelling • DLR_M1/M2: portion of DLR enriched with the class-based approach (Glimm et al., 2010) to accommodate metaclasses
TrOWL: Tractable reasoning infrastructure for OWL 2 • Semantic Approximation (AAAI2007) • Pre-compute and compile the materialisation of OWL 2 ontologies in OWL 2 QL • Sound and complete for conjunctive queries without non-distinguished variables • Tractable in run-time • Syntactic Approximation (AAAI2010) • Normalise OWL 2 axioms into nominal-safe EL++ with additional data structures to maintain non-EL semantics • Approximate deduction on the normalisation results • Sound, incomplete but practically high recall for many ontologies • Tractable TBox classification and ABox materialisation • Oracle 11g support, SPARQL 1.1 query answering (leveraging OWL-BGP), local closed world reasoning, Jena API, etc.
Syntactic Approximation • Normalisation • Representing non-EL expressions with fresh names • Maintain complementary relations • Deduction • CEL rules • Additional rules • E.g. A subClassOf B => not B subClassOf not A • Example ontology: • A subClassOfforall r B • forall r C subClassOf D • B subClassOf C • => • A subClassOf D X1 X2 A Some Some D ALL ALL A D r nB nC r B C B C
Metamodelling in FMA Ontology • FMA frame-based ontology contains metamodelling • E.g. Physical_anatomical_entityinstanceOfAnatomical_entity_template • Physical_anatomical_entitysubClassOfAnatomical_entity • Different implementations in OWL ontologies • FMA FullR uses OWL Full; • FMA Consititutional encodes metaclass assertions with class subsumptions, metaproperty assertions with existential and universal restrictions; • OWL 2 DL with punning semantics • A class and an individual with same IRI will still be treated as different entities, leading to incomplete results • OWL 2 DL with class-based approach • Introducing representative individual of each concept • Encoding subsumptions/class assertions with object property relations
Evaluation Results • FMA ontologies are in general very difficult to reason with • Especially with Metamodelling involved • TrOWL performs generally well on FMA ontologies • Generally faster than fully-fledged, universal, intractable reasoners; • The only one to classify FMA-OWL2G_noMTC TBox in 1 hour; • Practically high recall
Dealing with Unsatisfiable Concepts • Translated versions of FMA contain many unsatisfiabilities • FMA Constitutional: 33,433 / 41,648 • FMA OWL2G_noMTC: 67,771 / 85,005 • Investigating such unsatisfiabilities is difficult • Hard to compute justifications • Requires a lot of entailment checkings • Too many unsatisfiability to look into • We want to get into the core of the problem efficiently
Finding the Core Unsatisfiabilities • Kalyanpur et al.’s root and derived unsatisfiable concepts • B is parent of A • A is derived • Non-derived unsatisfiable concept is root • A derived concept can have alternative justification that contains no parent • Eliminating all root concepts do not necessarily eliminate all unsatisfiability • Still need to compute justifications and entailment checkings Just. (A subClassOfBot) Just. (B subClassOfBot)
Finding the Core Unsatisfiabilities • Type I and Type II unsatisfiable concepts • Purely based on the derivation relations between axioms • Suitable with a forward-chaining completion-based algorithm • Type I concepts are full-unsatisfiable in reasoning • Type II concepts are semi-unsatisfiable in reasoning • not immediately subsumed by all concepts • propagates Type II • Can become Type I if appropriate inference occurs Type I axiom1 A subClassOfBot Type I and Type II axiom2 B subClassOfBot axiom3
Application on FMAs • Repairing the Type I concepts will resolve all existing unsatisfiabilities • From TrOWL’s perspective • Fewer enough Type I makes debugging much easier • E.g. 145 Type I in FMA Constitutional, only 0.43% of all the unsatisfiable concepts; 6 axioms directly involved, out of the 122,136 logical axioms
Summary and Future Work • TrOWL and its syntactic approximation facility is well suited for the reasoning, metamodelling and debugging of the FMA ontologies • Striking a balance among expressiveness, performance and quality • Future works • A completeness-guarantee? • Why does TrOWL have high recalls on certain ontologies? • A potential tractable DL that covers FMA family? • A fully-fledged completion-based reasoner for OWL2 DL? • Will be intractable • Parallelisation? • Changing CEL rules to ELK rules? • Parallelising the additional approximate deduction rules • Improved entailment checking • Currently using the dual-ontology classification algorithm from CEL • Changing to a goal-driven algorithm?
Thank You! • http://trowl.eu