Object-oriented Semantic and Sensory Knowledge Extraction from the Web

Abstract

Automatic knowledge extraction from such a very large document corpus as the Web is one of the hottest research topics in the domain of Artificial Intelligence and Database technologies. This chapter introduces my object-oriented and the existing methods to extract semantic (e.g., hyponymy and meronymy) and sensory (e.g., visual and aural) knowledge from the Web, and compares them by showing several experimental results. My object-oriented semantic knowledge extraction is based on property inheritance(s) and property aggregation, and repeatedly improves the extracted results of both hyponymy and meronymy relations. Meanwhile, my object-oriented sensory knowledge extraction is improved by utilizing the extracted hyponymy and meronymy relations. Finally, this chapter introduces my Sense-based Object-name Search (SOS) to enable users to identify the concrete name of a target object which they do not know only by inputting its hyponym (class-name) and some sensory descriptions, as an application system to utilize the Web-extracted semantic and sensory knowledge.