Reasoning on the Semantic Web Reasoning on the Semantic Web Issues, vulnerabilities, and solutions Yannis Kalfoglou Web Sc...
Reasoning on the SW Work to date: Ontologies:   OWL family of languages : OWL Lite, OWL DL, OWL Full, recently  OWL 1.1   ...
Vulnerabilities – robust reasoning Kalfoglou et al. “On the emergent Semantic Web and overlooked issues”, ISWC04 <ul><li>H...
Vulnerabilities – robust reasoning Kalfoglou et al. “On the emergent Semantic Web and overlooked issues”, ISWC04 <ul><li>O...
Points to consider <ul><li>users need to see the  benefit of semantics </li></ul><ul><li>our reasoners should be able to c...
Distributed reasoning work <ul><li>InferenceWeb  -  http://iw.stanford.edu/2.0/   </li></ul><ul><ul><li>knowledge provenan...
Looking forward …a simple message (well… not that  simple  to implement!) <ul><li>Provide  web-scale reasoning  that  </li...
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Reasoning on the Semantic Web

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Vulnerabilities, issues and solutions with automated reasoning on the semantic web

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Reasoning on the Semantic Web

  1. 1. Reasoning on the Semantic Web Reasoning on the Semantic Web Issues, vulnerabilities, and solutions Yannis Kalfoglou Web Science Research Meeting Monday, 22 January 2007
  2. 2. Reasoning on the SW Work to date: Ontologies: OWL family of languages : OWL Lite, OWL DL, OWL Full, recently OWL 1.1 Ontology editing tools : Protégé, SWOOP, TopBraid, OntoStudio, OILed, OntoTrack Rules: Rules engines/languages : SWRL, RIF Theorem Provers: Hoolet Data : Data query languages : SPARQL, RQL, RDQL, SQL, MySQL; Data formats : RDF, XML APIs/Frameworks : Jena, Sesame, KAON2, etc. Some selective users’ experiences/expectations: Lack of sophisticated uses ” Minimal use of expressive OWL constructs by a majority of large scale ontologies (SNOMED-CT, FMA, GeneOntology, NCI Thesaurus, FOAF) [..] mostly taxonomic reasoning - subsumption“ Kershenbaum et al. (2006) “A view of OWL from the field: Use cases and experiences”. OWLED06, ISWC06 Workshop. Recent OWL version gets good reception “ OWL 1.1 gives “disjoint union” as a new construct – provides user defined data type functionality – use of comments to capture and express the rationale behind modelling decisions – we need tool support” Dolbear et al. (2006) “What OWL has done for geography and why we don’t need to map read”. OWLED06, ISWC06 Workshop [DL] reasoning: tuned towards qualitative reasoning over the ontology rather than quantitative reasoning over the instances. Reasoning Vulnerabilities Points Related work Looking fwd
  3. 3. Vulnerabilities – robust reasoning Kalfoglou et al. “On the emergent Semantic Web and overlooked issues”, ISWC04 <ul><li>Handling soundness and completeness </li></ul><ul><ul><li>precise technical terms that describe properties of formal systems or set of sentences – sound – complete </li></ul></ul><ul><ul><li>traditional KRR views shaped standardisation efforts for current SW technology for obvious practical reasons </li></ul></ul><ul><ul><ul><li>But: the envisioned applications of the SW are clearly beyond the capacity of FOL-based and DL-based technology </li></ul></ul></ul><ul><ul><li>problem with preserving and mechanising soundness and completeness on the SW is the lack of referential integrity and inconsistent knowledge produced by multiple resources </li></ul></ul>Reasoning Vulnerabilities Points Related work Looking fwd A formal system is sound when every sentence produced by the system’s inference rules operating on the system’s initial set of axioms logically follows from that set It is complete when every sentence that logically follows from the system’s initial set of axioms can be formally derived using the inference rules. A set of sentences are said to be complete if every sentence of the language can be proved or disproved using those rules. In an environment the size of the web we must abandon the classical idea of sound and complete reasoners, our reasoners will almost certainly have to be incomplete (no longer guaranteeing to return all logically valid results), but most likely also unsound : sometimes jumping to a logically unwarranted conclusion[…] answers will have to approximate.
  4. 4. Vulnerabilities – robust reasoning Kalfoglou et al. “On the emergent Semantic Web and overlooked issues”, ISWC04 <ul><li>Open and closed worlds </li></ul><ul><ul><li>traditional CWA: “ everything that is not known or cannot be proved to be true must be false ” </li></ul></ul><ul><ul><li>common in DB design but DLs adopt an “open world” semantics: absence of information in a DB is regarded as negative information in an ABox as lack of knowledge – in ABoxes information is generally viewed as incomplete </li></ul></ul><ul><ul><li>despite its controversial reception, CWA could be useful . </li></ul></ul><ul><ul><li>remedies: use of extra-logical operators to state negative information (logic), use ASP to draw conclusions based on the lack of evidence of the contrary, use of Local Closed World Assumption (LCWA): use both CWA information and allow other information to be treated as unknown </li></ul></ul><ul><ul><li>caution: a priory knowledge of local completeness (not scalable), asserting LCWA in vast knowledge sources could lead to inconsistencies </li></ul></ul>Reasoning Vulnerabilities Points Related work Looking fwd “ [..]the language must be able to state that a given ontology can be regarded as complete . This would sanction additional inferences to be drawn from that ontology. The precise semantics of such statement (and the corresponding set of inferences) remains to be defined, but examples might include assuming complete property information about individuals, assuming completeness of class-membership , and assuming exhaustiveness of subclasses .”
  5. 5. Points to consider <ul><li>users need to see the benefit of semantics </li></ul><ul><li>our reasoners should be able to cope with flawed content </li></ul><ul><li>Certification of sound inferences and vetting of ontologies by domain SME (human or artificial) </li></ul><ul><li>Inference engines to cope with variety of rule languages </li></ul>Reasoning Vulnerabilities Points Related work Looking fwd
  6. 6. Distributed reasoning work <ul><li>InferenceWeb - http://iw.stanford.edu/2.0/ </li></ul><ul><ul><li>knowledge provenance infrastructure, PML – Proof Markup Language </li></ul></ul><ul><li>IRS-I/II/III/IV? - http :// kmi.open.ac.uk/projects/irs </li></ul><ul><ul><li>SW services framework, WSDL, OCML, Lisp </li></ul></ul><ul><li>OnToBroker - http://ontobroker.semanticweb.org/ </li></ul><ul><ul><li>F-Logic and Horn Logic (Prolog), using translation rules, dynamic filtering (NAF) </li></ul></ul><ul><li>UPML - http://www.cs.vu.nl/~upml/ </li></ul><ul><ul><li>Framework to characterise the process flow of distributed reasoning, abstract. </li></ul></ul><ul><li>Sesame - http://www.openrdf.org/about.jsp </li></ul><ul><ul><li>RDF framework, used in InferenceWeb </li></ul></ul><ul><li>TAP - http://tap.stanford.edu/ </li></ul><ul><ul><li>Distributed KB, query interface, sharing descriptions based on a common vocabulary </li></ul></ul><ul><li>E-connections - http://www.mindswap.org/2004/multipleOnt/ </li></ul><ul><ul><li>KR language defined as a combination of other logic formalisms, expressible in Abstract Description System (ADS) framework. </li></ul></ul><ul><li>DERI’s WSMO/WSMF/WSMX Semantic Web Services work http://www.wsmo.org/ </li></ul><ul><ul><li>Modelling Framework, Modelling Ontology and Execution Environment (API, service discovery) </li></ul></ul>Reasoning Vulnerabilities Points Related work Looking fwd
  7. 7. Looking forward …a simple message (well… not that simple to implement!) <ul><li>Provide web-scale reasoning that </li></ul><ul><ul><li>deals with incomplete resources </li></ul></ul><ul><ul><li>deals with inconsistent resources </li></ul></ul><ul><ul><li>is context-aware </li></ul></ul><ul><ul><li>deals with OWA/CWA/LCWA (other WA?) </li></ul></ul><ul><ul><li>allows share inferences </li></ul></ul>Reasoning Vulnerabilities Points Related work Looking fwd

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