The Internet is composed of more than 6.2 billion Web pages and grows larger every day. As the number of links and specialty subject areas grows, it becomes ever more difficult to find pertinent information. For some subject areas, special-purpose data crawlers continually search the Internet for specific information; examples include real estate, air travel, auto sales, and others. The use of such special-purpose data crawlers (i.e., targeted crawlers and knowledge databases) also allows the collection and analysis of agricultural and forestry data. Such single-purpose crawlers can search for hundreds of key words and use machine learning to determine if what is found is relevant. In this article, we examine the design and data return of such a specialty knowledge database and crawler system developed to find information related to cross-laminated timber (CLT). Our search engine uses intelligent software to locate and update pertinent references related to CLT as well as to categorize information with respect to common application and interest areas. At the time of this publication, the CLT knowledge database has cataloged nearly 3,000 publications regarding various aspects of CLT.
Bibliographical noteFunding Information:
The work on which this article is based was funded in whole or in part through a grant awarded by the Wood Innovations Program, USDA Forest Service.