TY - JOUR
T1 - Evaluating ontology mapping techniques
T2 - An experiment in public safety information sharing
AU - Kaza, Siddharth
AU - Chen, Hsinchun
N1 - Funding Information:
This research was supported in part by: (1) the NSF DG program: “COPLINK Center: Information and Knowledge Management for Law Enforcement,” #9983304; (2) NSF KDD program: “COPLINK Border Safe Research and Testbed," #9983304; (3) NSF ITR program: “COPLINK Center for Intelligence and Security Informatics Research- A Crime Data Mining Approach to Developing Border Safe Research,” #0326348; and (4) Department of Homeland Security (DHS) and Corporation for National Research Initiatives (CNRI) through the “BorderSafe” initiative, #2030002. We thank Tim Petersen and Daniel Casey of the Tucson Police Department for their contributions to this research.
PY - 2008/11
Y1 - 2008/11
N2 - The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain.
AB - The public safety community in the United States consists of thousands of local, state, and federal agencies, each with its own information system. In the past few years, there has been a thrust on the seamless interoperability of systems in these agencies. Ontology-based interoperability approaches in the public safety domain need to rely on mapping between ontologies as each agency has its own representation of information. However, there has been little study of ontology mapping techniques in this domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. In addition, we implement an information theory based tool called MIMapper that uses WordNet and mutual information between data instances to map ontologies. We find that three tools: PROMPT, Chimaera, and LOM, have average F-measures of 0.46, 0.49, and 0.68 when matching pairs of ontologies with the number of classes ranging from 13-73. MIMapper performs better with an average F-measure of 0.84 in performing the same task. We conclude that the tools that use secondary sources (like WordNet) and data instances to establish mappings between ontologies are likely to perform better in this application domain.
KW - Intelligence and security informatics
KW - Mutual information
KW - Ontology mapping
KW - Public safety information sharing
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U2 - 10.1016/j.dss.2007.12.007
DO - 10.1016/j.dss.2007.12.007
M3 - Article
AN - SCOPUS:53349109725
SN - 0167-9236
VL - 45
SP - 714
EP - 728
JO - Decision Support Systems
JF - Decision Support Systems
IS - 4
ER -