towards a standard for heterogeneous ontology integration and interoperability oliver kutz &...

Towards a Standard for Heterogeneous Ontology

Integration and Interoperability

Oliver Kutz & Christoph LangeResearch Center on Spatial Cognition (SFB/TR 8), University of Bremen,

Germany& Jacobs University Bremen, Germany

Joint work with

Till Mossakowski (DFKI)- Christian Galinski (Infoterm)

Seoul, South Korea - LaRC, June 2011

Ontology Interoperability• Critical issues are

• Semantic Heterogeneity

• Syntactic Heterogeneity

• Plurality of structuring & modularity concepts

• Plurality of documentation techniques

• Plurality of tools, editors, reasoners, etc.

• Plurality of (kinds of) services, devices, etc.

Overview• Motivating Examples for the use of the hyperontology framework

• Structured Ontology Design

• Matching in networks of ontologies

• Relations between ontologies: Refinements, Blending, etc.

• Universal logic addresses (onto)-logical pluralism and semantic heterogeneity

• Hyperontologies = structured and heterogeneous (networks of) ontologies

• A Sketch of a future standard: DOL: Distributed Ontology Language

Structured Ontologies

Dolce’s structuringin CASL, showing the import structure, i.e. the modular re-use

Matching Across Repositories• Ontology Repositories, e.g.

BioPortal, Orate, Colore, Tones: collections of ontologies for different purposes and in various ontology languages.

• create new ones out of existing ones by finding synonyms, extracting modules, and merging them together.

• Meaning shift and “chinese whispers”.

• problem of heterogeneity & scalability

• problem of “information overflow”

Heterogeneous Refinement of Dolce

• Different version of Dolce are available, e.g. in DL and FOL: What is their logical relationship?

Core

Projection

Approximation

heterogeneous refinement

definitional extension

connection through bridge theory

P(x)Q(x,y)

P(x)Q(x,y)

R(x) R(x,t)

R(x,t) ∼ R(x)(forget temporal

dimension)

S(x,y,z)

U(x,y)V(z)...

Dolce-LITE Dolce-FOL

Ontological Blending

Selectively combining two ontologies whilst preserving common structure (theory).

Motivation:Conceptual Blending and metaphor:

House + Boat = Houseboat Boat + House = Boatshouse

Pluralism in Ontologies• NCI Thesaurus

about 34.000 concepts arranged in 20 taxonomic trees, reference terminology for cancer research, sub-Boolean description logic EL.

• Galenmedical domain ontology, relatively large, but also relatively complex axiomatisation in a more expressive DL, namely OWL-DL.

• Dolce, BFO, GUM, GFOFoundational ontologies, first-order, higher-order, first-order modal logic being used. Complex axiomatisations.

Universal Logic

• Signatures: (non-logical symbols) propositions; predicates; functions, constants, terms.

• Grammar: (logical symbols) variables and quantifiers; modalities; identity symbol; substitution.

• Models: possible world; domains of discourse; accessibility (counterpart relations) ; object (individual)

• Satisfaction: vary the truth conditions for quantifiers; Booleans; Modalities; vary conditions for identity statements, etc.

Items that can be varied according to universal logic:

Benefits: Borrowing and combination of logics and reasoners, structuring, etc.

Heterogeneous Ontologies• In order to systematically link

and combine ontological modules formulated in different formalisms we need to:

• fix a logic graph

• give logic translations (institution co-morphisms)

Onto-Logical Translation Graph

Hyperontology example

Heterogeneous specification of

Mereology

A hyperontology is a heterogeneously interlinked network of

heterogeneous ontology modules.

Hyperontologies via Matching• 5 participating ontologies, all

connected via matchings.

• matching results in a single synset identifying all matched concepts, and inconsistency.

• removal of the Graphics ontology can cut synset into 2 distinct ones, can restore consistency.

• following more than one orange arrow means playing chinese whispers.

O1 O3

O2 O4

O5

The Problem of Module Extraction• JRAO is constructed using fragments of NCI and Galen

• NCI, Galen are too large to be imported completely

• Import only interesting ‘modules’

• Conservativity:Ensure that the ‘module’ is large enough to cover all relevant information (coverage)Ensure that no new information is added (safety)Add only relevant axioms (minimality)

Workflow & Tool Interoperability

Ontology Ontology RepositoryRepository

Hyperontology Hyperontology GraphGraph

select matching select matching configurationconfiguration

Matching Matching ConfiguratioConfiguratio

nn

FalconFalcon HetsHetsPelletPelletAlignment Alignment SpecificatiSpecificati

onon

produce formal produce formal specificationspecification

ModulesModules

Merged Merged OntologyOntology

consistenconsistency checkcy check

UserUser

yes

no

extract extract modulesmodules

match match pairwisepairwise

compute compute colimitcolimit

Hets - The Heterogeneous Tool Set• structured

representations (such as V-alignments), reuse/independent development of modules

• library of logics/formalisms supported, incl. OWL-DL

• various provers connected: incl. for OWL-DL, first-order, higher-order, model checker, etc

• computation of colimits

• checking for conservativities

DOL - Distributed Ontology Language

• general purpose framework for ontology interoperabilitylibrary of logics/formalisms supported, incl. most ontology languages

• well-defined formal semanticspairs of languages have common target ontology languageApplication T(O) of translation to ontology part of DOL syntax

• DIF: XML- and RDF-based interchange formats Mapping two ontology

languages into a third

DOL - Distributed Ontology Language

Mapping two ontology languages into a third

• support for various module languages as well as one universal lingua francaexplicit module extractioninternalise ontology mappings (first class citizens)make ontology translations available in the language

• distributed ontologies in terms of both

• different internet locations and

• different ontology languages.

Embedded Ontology Documentation

• … but also for human users of an ontology (make ontologies comprehensible)

• Knowledge Engineers and Service Developers – reuse!

• End Users – when services expose ontology documentation (“labels” and more) as online help

Ontological Structuring and Modularity is not only for machines …

Documentation State of the Art

• SKOS (Simple Knowledge Organization System): an OWL ontology with some non-OWL axioms

• “documented” in HTML manual, and unstructured source comments

Language Documentation Support

Documentation Features Unsupported so far

• Informal subsets of an ontology (not yet explicitly modularized)

• Subterms of complex axioms

• Literate Programming:natural language and formal expressions freely interwoven ⇒ generate ontology and manual from same source

Documentation in DOL• Use existing annotation facilities where possible

• In non-XML ontology languages, can't embed documentation⇒ “special” comments, or external, non-intrusive RDF standoff markup Reuse existing documentation vocabularies(e.g. OMV = Ontology Metadata Vocabulary)

• How to identify subjects? E.g. “the first three axioms”? How to do that in text-oriented ontology languages? – Use XPointer!

Conclusions• Ontologies are widely used to enable interoperability

• Currently no unified framework for ontology interoperability.

• Apply the state of the art in modularity, structuring and documentation, as developed e.g. in software engineering

• Enable synchronisation and orchestration of interoperable services

• OntoIOp (Ontology Integration and Interoperability) is being proposed in ISO/TC 37/SC 3 in order to fill this gap.