TY - CONF
T1 - Evaluating ontology mapping techniques
T2 - 15th Workshop on Information Technology and Systems, WITS 2005
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 - 2005
Y1 - 2005
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 this 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-based information integration approaches and mapping techniques in the public safety domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. We find that PROMPT, Chimaera, and LOM have an average precision of 85% and a recall of 27% when matching pairs of ontologies with the number of classes ranging from 17-73. In addition, we find that tools that use secondary sources to establish mappings between ontologies are likely to perform better in this 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 this 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-based information integration approaches and mapping techniques in the public safety domain. We evaluate current mapping techniques with real-world data representations from law-enforcement and public safety data sources. We find that PROMPT, Chimaera, and LOM have an average precision of 85% and a recall of 27% when matching pairs of ontologies with the number of classes ranging from 17-73. In addition, we find that tools that use secondary sources to establish mappings between ontologies are likely to perform better in this domain.
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M3 - Paper
AN - SCOPUS:84905722205
SP - 195
EP - 200
Y2 - 10 December 2005 through 11 December 2005
ER -