SQE - Semantic Query Expansion

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The document discusses semantic query expansion (SQE) which aims to improve search results by applying semantic reasoning to expand queries with few results or narrow results with too many. The SQE algorithm first processes the user's query using a fulltext search engine. It then considers search history and semantic knowledge like WordNet to potentially expand the query by adding related terms. Results are reranked based on term frequency, inverse document frequency, and contextual weights from long, mid, and short-term contexts before being returned to the user.

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SQE – Semantic Query Expansion Digital Enterprise Research Institute National University of Ireland, Galway [email_address] jakub.demczuk @deri.org [email_address] [email_address]

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SQE picture: Search: Java web Lucene Query Expansion (WordNet,Taxonomies,FOAF) Results (TF/IDF) Java(PL) Java (Island) 60 10 SSCF Bookmarks FOAFRealm Web(www) Spider’s web 50 20 Results History Friends

Semantic Query Expansion in details: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Today’s conventional search engines hardly do provide the essential content relevant to the user’s search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query

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