The Internet is composed of >50 billion Web pages and grows larger every day. As the number of links and specialty subject areas increases, 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 keywords and use machine learning to determine whether or not what is found is relevant. In this paper, we examine the design and data return of such a specialty knowledge database and crawler system developed to find information related to urban wood utilization—products made from timber harvested in cities and municipalities. Our search engine uses intelligent software to locate and update pertinent references related to urban wood as well as to categorize information with respect to common application and interest areas. At the time of this publication, the urban wood knowledge database has cataloged >700 publications regarding various aspects of urban wood.