123 Jiao Tao 1, Li Ding 2, Deborah L. McGuinness 3 Tetherless World Constellation Rensselaer Polytechnic Institute Troy, NY, USA 1 PhD Student 2 Postdoctoral Research Fellow 3 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 schema as ontologies O (including embedded semantic expectations) Web O O D D 3. Instantiate KMS ontologies 4. Publish KMS instance data D O O 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 = {O 1,O 2, …} –Is O i reachable? where O i defines the terms used by D. Inspect logical inconsistencies in D –Is O i 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 for Potential Issue Detection Example: MPV Issue

Implementation and Evaluation Demo: TW OIE Service 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, J. Davies, M. Lytras, and A. Sheth, Semantic-Web-Based Knowledge Management, IEEE Internet Computing, Vol. 11, No. 5, pp. 14-6, L. Ding, and T. Finin, Characterizing the Semantic Web on the Web, ISWC, pp , 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 , 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 , 2004.

Extras

Semantic e-Science Data Evaluation

15 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 provenance registration search engine based publishing Expert friendly Visualization End-user friendly visualization Trust computation OWL-S/BPEL SDS 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, 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) filteredfocusedglobal abstraction discourse provenance trust

McGuinness – Microsoft eScience – December 8, Provenance View Source metadata: name, description, … Source-Usage metadata: which fragment of a source has been used when Views of Explanation Explanation (in PML) filteredfocusedglobal abstraction discourse provenance trust

Links Tetherless World Instance Ontology Instance Evaluator: Inference Web inference-web.orginference-web.org Semantic eScience class link (with book to follow) Sciencehttp://tw.rpi.edu/wiki/Semantic_e- Science

McGuinness NSF/NCAR May 6,