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Jiao Tao11, Li Ding22, Deborah L. McGuinness33
Tetherless World ConstellationRensselaer Polytechnic Institute
Troy, NY, USA
11 PhD Student PhD Student22 Postdoctoral Research Fellow Postdoctoral Research Fellow
33 Tetherless World Senior Constellation Professor Tetherless World Senior Constellation Professor
Instance Data Evaluation for Semantic Web-Based
Knowledge Management Systems
Semantic Web-based KMS• The Semantic Web is a next generation of the
Web which formally defines the relations among terms with ontologies, gives well-defined meaning to information, and enables machines to comprehend the content on the Web (Berners-Lee, Hendler, & Lassila 2001).
• Semantic Web-based Knowledge Management Systems enable the next generation of KMS– Applies semantic web technologies to improve on
traditional knowledge-management approaches or realize emerging knowledge-services requirements (Davies, Lytras, & Sheth 2007)
– Schemas are represented as ontologies (O) and data is SW instance data (D)
Data Evaluation in SW-based KMS: State of the Art
• In SW-based KMS, instance data often accounts for orders of magnitude more data than ontology (Ding & Finin 2006).
• However most data evaluation work (Rocha et al. 1998) focuses on ontology evaluation, i.e., checking whether the ontologies correctly describe the domain of interest.
• There is very little, if any, work on evaluating the conformance between ontologies and instance data.
1. Create KMS schemaas ontologies O(including embedded semantic expectations)
Web Web
OO DD
3. Instantiate KMS ontologies
4. Publish KMS instance data D
OO
2. Acquire KMS ontologies
Do semantic expectations match between O and D?
No syntax errors?
Instance Data Evaluation in SW-based KMS
Semantic expectation mismatches:(i) Logical inconsistencies(ii) Potential issues
Generic Evaluation Process (GEP)
• Load instance data D– Is loading failing?
• Parse instance data D– Is D syntactically correct?
• Load referenced ontologies O = {O1,O2, …}– Is Oi reachable? where Oi defines the terms used by D.
• Inspect logical inconsistencies in D– Is Oi logically consistent?– Merge all consistent referenced ontologies into O'– Are D+O’ logically consistent?
• Inspect potential issues in D– Compute DC = INF(D,O') which includes all triples in D and O', and all
inferred sub-class/sub-property relations– Is there any potential issue in D?
Potential Issues
• Unexpected Individual Type (UIT) Issue– rdfs:domain– rdfs:range– owl:allValuesFrom
• Redundant Individual Type (RIT) Issue• Non-specific Individual Type (NSIT) Issue• Missing Property Value (MPV) Issue
– owl:cardinality– owl:minCardinality
• Excessive Property Value (EPV) Issue– owl:cardinality– owl:maxCardinality
Graph Patterns of Potential Issues
• Example: Missing Property Value Issue
Make sure all instances of wine have a Maker specified
SPARQL Solutions forPotential Issue Detection
• Example: MPV Issue
Implementation and Evaluation
• Demo: TW OIE Service http://onto.rpi.edu/demo/oie/
• Comparative experiment results
Status, Current and Future Work• TW OIE implemented and Service provided as part of the
Inference Web Explanation Framework (IW – McGuinness and Pinheiro da Silva, 2004)
• Ongoing work: characterize and detect potential (integrity) issues in instance data
• An Initial Investigation on Evaluating Semantic Web Instance Data (WWW 2008)• Characterizing and Detecting Integrity Issues in OWL Instance Data (OWLED 2008
EU) • Integrity Constraint Modeling and Checking for Semantic Web Data An Answer Set
Programming-based Approach (submitted to ESWC 2009)
• Future work:• Formal representation for expressive integrity constraints• Automatic updates to data to fix problems• Enhanced explanation capabilities
References• T. Berners-Lee, J. Hendler, and O. Lassila, The Semantic Web: A
New Form of Web Content that Is Meaningful to Computers Will Unleash a Revolution of New Possibilities, Scientific American, pp. 34–43, 2001.
• J. Davies, M. Lytras, and A. Sheth, Semantic-Web-Based Knowledge Management, IEEE Internet Computing, Vol. 11, No. 5, pp. 14-6, 2007.
• L. Ding, and T. Finin, Characterizing the Semantic Web on the Web, ISWC, pp. 242-257, 2006.
• R. A. Rocha, S. M. Huff, P. J. Haug, D. A. Evans, and B. E. Bray, Evaluation of a Semantic Data Model for Chest Radiology: Application of a New Methodology, Methods of Information in Medicine, Vol. 37, No.4-5, pp. 477-490, 1998.
• D. L. McGuinness and P. Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics. Vol.1 No.4., pp 397-413, 2004.
Extras
Semantic e-Science Data Evaluation
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WWW Toolkit
Proof Markup Language (PML)Learners
JTP/CWM
SPARK
UIMA
IW Explainer/Abstractor
IWBase
IWBrowser
IWSearch
Trust
Justification
Provenance
*
KIF/N3
SPARK-L
Text Analytics
IWTrust
provenanceregistration
search enginebased publishing
Expert friendlyVisualization
End-user friendly visualization
Trust computationOWL-S/BPELSDS
Trace of web service discovery
Learning Conclusions
Trace of task execution
Trace of information extraction
Theorem prover/Rules
Inference Web Explanation Architecture
• Semantic Web based infrastructure• PML is an explanation interlingua
– Represent knowledge provenance (who, where, when…)– Represent justifications and workflow traces across system boundaries
• Inference Web provides a toolkit for data management and visualization
McGuinness – Microsoft eScience – December 8, 2008
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Global View
• Explanation as a graph• Customizable browser options
– Proof style– Sentence format– Lens magnitude– Lens width
• More information– Provenance metadata– Source PML– Proof statistics– Variable bindings– Link to tabulator– …
Views of Explanation
Explanation (in PML)
filtered focused global
abstraction
discourse
provenancetrust
McGuinness – Microsoft eScience – December 8, 2008
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Provenance View• Source metadata: name, description, …• Source-Usage metadata: which fragment of
a source has been used when
Views of Explanation
Explanation (in PML)
filtered focused global
abstraction
discourse
provenancetrust
Links
• Tetherless World Instance Ontology Instance Evaluator: http://onto.rpi.edu/demo/oie/
• Inference Web inference-web.org • Semantic eScience class link (with book to
follow) http://tw.rpi.edu/wiki/Semantic_e-Science
McGuinnessNSF/NCAR May 6, 2008
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