Relating Taxonomies with Regulations

Published

May 13, 2008

Author(s)

Chin P. Cheng, Jiayi Pan, Gloria T. Lau, Kincho H. Law, Albert T. Jones

Abstract

Increasingly, taxonomies are being developed for a wide variety of industrial domains and specific applications within those domains. These taxonomies attempt to represent formally the vocabularies commonly used by domain practitioners.  These formal representations have the potential to automate information retrieval and improve decision-making.  Those decisions must comply with existing government regulations and codes of practice, which are not always known to the practitioners.  Although both are available in digital form online, practitioners cannot retrieve easily relevant regulations and codes that apply to particular decisions.   To address this problem, we propose an approach to relate regulations with existing industry-specific taxonomies. The mapping from a single taxonomy to a single regulation is a trivial keyword matching task.  In this paper, we examine techniques to map a single taxonomy to multiple regulations, as well as to map multiple taxonomies to a single regulation.  Those techniques include Cosine similarity, Jaccard coefficient and market-basket analysis.  These techniques provide a metric that measures the similarity between concepts from different taxonomies.  We describe these techniques and metrics, and evaluate them using   examples from the building industry.  These examples show the potential regulatory benefits from the mapping between various taxonomies and regulations.

Proceedings Title

Proceedings of the 9th Annual International Conference on Digital Government Research

Conference Dates

May 18-21, 2008

Conference Location

Montreal, 1, CA

Pub Type

Conferences

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Keywords

Heterogeneous Ontologies, Taxonomy Interoperability, Relatedness Analysis, Regulation Retrieval

Citation

Cheng, C. , Pan, J. , Lau, G. , Law, K. and Jones, A. (2008), Relating Taxonomies with Regulations, Proceedings of the 9th Annual International Conference on Digital Government Research, Montreal, 1, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824637 (Accessed October 31, 2024)