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Discovering User Goals To Improving Search Engine Applicability

758 words - 4 pages

The users are commonly using the internet for several information needs. Still, the abstruse word\topic submitted to search engine does not satisfy user information needs, because different users may have different information on dissimilar facets upon the submission of same query to search engine. So discovering different user search goals becomes complicated. The estimation and representation of user search goals can be very useful in improving search engine applicability and user knowledge. This paper proposes an approach for concluding user search goals by analyzing user query logs from several search engines. This proposed method is used to determine dissimilar user search goals for a query by clustering the user feedback sessions. The Feedback sessions are built from click-through logs of various search engines. This method generates virtual-documents to better represent feedback sessions for clustering. Finally, clustering virtual documents to discover different user search goals and describe them with some keywords. Then these user search goals are used to restructure the web search results.
Measuring the semantic relevance between words is an unavoidable problem in web mining, and language processing techniques. Web mining applications require the ability to accurately measure the semantic closeness between concepts or entities. Retrieving a set of documents that is semantically close to a given user query is the main problem in Information Retrieval (IR). Estimation of semantic similarity between words efficiently is demanding for differing natural language processing tasks such as automatic text summarization, textual entailment and Word Sense Disambiguation (WSD).
In manually built general-purpose lexical ontologies such as WordNet, Semantically relevant words of a particular word are listed. In WordNet, a synset contains a set of equivalent words for a particular word in sense. Yet, semantic similarity between entities changes overtime and across domains. Consider an example; blackberry is frequently associated with mobiles on the web. Such sense of blackberry is not listed in most general-purpose thesauri or dictionaries. Let A be a user, who searches for blackberry on the web, might be interested in this sense of blackberry and not blackberry as a fruit. Modern words are constantly being created as well as new senses are assigned to actual words. Maintaining ontologies manually to...

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