Applying information retrieval techniques to the World Wide Web (WWW) environment is a challenge, mostly because of its hypertext/hypermedia nature and the richness of the meta-information it provides. We present four keyword-based search and ranking algorithms for locating relevant WWW pages with respect to user queries. The first algorithm, Boolean Spreading Activation, extends the notion of word occurrence in the Boolean retrieval model by propagating the occurrence of a query word in a page to other pages linked to it. The second algorithm, Most-cited, uses the number of citing hyperlinks between potentially relevant WWW pages to increase the relevance scores of the referenced pages over the referencing pages. The third algorithm, TFxIDF vector space model, is based on word distribution statistics. The last algorithm, Vector Spreading Activation, combines TFxIDF with the spreading activation model. We conducted an experiment to evaluate the retrieval effectiveness of these algorithms. From the results of the experiment, we draw conclusions regarding the nature of the WWW environment with respect to document ranking strategies.

The paper describes the World Wide Web Index and Search Engine (WISE) for Internet resource discovery. The system is designed around a resource database containing meta information about WWW resources and is automatically built using an indexer robot, a special WWW client agent. The resource database allows users to search for resources based on keywords, and to learn about potentially relevant resources without having to directly access them. Such capabilities can significantly reduce the amount of time that a user needs to spend in order to find the information of his/her interest. We discuss WISE's main components: the resource database, the indexer robot, the search engine, and the user interface, and through the technical discussions, we highlight the research issues involved in the design, the implementation and the evaluation of such a system.

A neural model for temporal pattern generation is used and analyzed for training with multiple complex sequences in a sequential manner. The network exhibits some degree of interference when new sequences are acquired. It is proven that the model is capable of incrementally learning a finite number of complex sequences. The model is then evaluated with a large set of highly correlated sequences. While the number of intact sequences increases linearly with the number of previously acquired sequences, the amount of retraining due to interference appears to be independent of the size of existing memory. The model is extended to include a chunking network which detects repeated subsequences between and within sequences. The chunking mechanism substantially reduces the amount of retraining in sequential training. Thus, the network investigated here constitutes an effective sequential memory. Various aspects of such a memory are discussed.

A neural network model of complex temporal pattern generation is proposed and investigated analytically and by computer simulation. Temporal pattern generation is based on recognition of the contexts of individual components. Based on its acquired experience, the model actively yields system anticipation, which then compares with the actual input flow. A mismatch triggers self-organization of context learning, which ultimately leads to resolving various ambiguities in producing complex temporal patterns. The architecture of the model incorporates a short term memory for building associations between remote components and recurrent connections for self-organization and component generation in a temporal pattern. Synaptic modification is based on a one-shot normalized Hebbian rule, which is shown to exhibit temporal masking. The major conclusion, namely the network model which can learn to generate any complex temporal pattern, is established analytically. An estimate on the efficiency of the training algorithm is provided. Multiple temporal patterns can be incrementally acquired by the system, exhibiting a form of retroactive interference. Neural and cognitive plausibility of the model is discussed.>