Improving access to and understanding of regulations through taxonomies  Original Research Article Government Information Quarterly, Volume 26, Issue 2, April 2009, Pages 238-245 Chin Pang Cheng, Gloria T. Lau, Kincho H. Law, Jiayi Pan, Albert Jones

 Increasingly, taxonomies are being developed to capture and represent those terms and vocabularies for a number of industry domains. Taxonomies describe concepts and entities in a subclass hierarchy through an “is–a” relationship. Since taxonomies contain well defined entities and hierarchical relationships, computers can interpret, understand, and reason about the terms and concepts described in a taxonomy. As a result, taxonomies can facilitate information interoperation and regulation retrieval. Interoperability is important because it allows practitioners – more importantly application programs – to access, relate, and combine information from multiple, heterogeneous sources. Recent studies by the National Institute of Standards and Technology (NIST) have reported that the lack of interoperability led to significant costs to the construction as well as the automotive industries ( [Brunnermeier and Martin, 1999] and [Gallaher et al., 2004] ).

Ontologies, which describe the general semantics of concepts and entity relationships that are not limited to an “is–a” hierarchy, have been proposed as a way to address interoperability problems. One recent forecast estimates that “By 2010, ontologies …will be the basis for 80% of application integration projects” (Jacobs & Linden, 2002). Ontologies serve as a means for information sharing because they capture the semantics of domain-specific information in a formal and computer interpretable form. Utilizing ontologies as a means to automate much of the integration process might be able to reduce cost and time significantly. We believe that they can also be used to facilitate access to government regulations.

Building a single ontology for an entire industry domain is both inefficient and impractical. Rather, small communities that need to exchange information frequently build ontologies targeted to their own users and applications (Ray, 2002). This results in multiple terminology classifications and data model structures. For instance, the architectural, engineering and construction (AEC) community has built several ontologies that describe the semantics of buildings and their components ( [Begley et al., 2005] and [Lipman, 2006] ). Even though these ontologies are all targeted towards the same user group, their structures, vocabularies and coverage differ depending on the application.

Government agencies, on the other hand, often use terminology and organize regulations based on their own needs, rather than the needs of the industrial communities they serve (Fountain, 2003). Both the agencies and the communities see a clear benefit of bridging these two distinct needs. One way to build such a bridge is to enable practitioners to browse and retrieve government regulations using their own terms and vocabularies — as captured in existing industry taxonomies. This would minimize the need for users to learn new vocabularies and organizational schemes. Metadata such as taxonomiesand ontologies have been leveraged to facilitate locating and retrieval of government information ( [Moen, 2001] and [Prokopiadou et al., 2004] ). These metadata, however, capture the semantics of the government information for conceptualization rather than representing domain knowledge from industry practitioners for browsing and retrieval. To bridge the needs of policy makers and the needs of industrial communities, we need methods and tools that map taxonomies to regulations. In the remainder of this paper, we describe a collection of such methods and tools.