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

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

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

16

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

17

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

19