Artificial Intelligence Lab, Department of MIS, University of Arizona / As new and emerging classes of information systems applications the applications become more overwhelming, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent in this “network-centric” information age. Information overload, a result of the ease of information creation and rendering via the Internet and the World Wide Web, has become more evident in people’s lives. Significant variations of database formats and structures, the richness of information media, and an abundance of multilingual information content also have created severe information interoperability problems-structural interoperability, media interoperability, and multilingual interoperability. The conventional approaches to addressing information overload and information interoperability problems are manual in nature, requiring human experts as information intermediaries to create knowledge structures and/or ontologies. As information content and collections become even larger and more dynamic, we believe a systemaided bottom-up artificial intelligence (AI) approach is needed. By applying scalable techniques developed in various AI subareas such as image segmentation and indexing, voice recognition, natural language processing, neural networks, machine learning, clustering and categorization, and intelligent agents, we can provide an alternative system-aided approach to addressing both information overload and information interoperability.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105127 |
| Date | January 2000 |
| Creators | Chen, Hsinchun |
| Contributors | Harum, S., Twindale, M. |
| Publisher | UIUC |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Book Chapter |
Page generated in 0.0021 seconds