Searching and Mining with Semantic Categories

Semantic Search Engine Or Question-Answering System?

It is always obvious to declare that traditional search engines deal with terms for the index organization and numbers for the quantity of documents indexed and provided to a search. And it is usual to assimilate a system, which identify specific information not provided with keyword queries, as a question-answering system. But in the standard information retrieval paradigm, in which the user provided with a ranked list of references to documents thought to contain information needed, it requires the user to search through the documents to satisfy his needs. Another approach to meting user’s information need in a more focused way is to provide specific answers to specific questions. Search engines as information retrieval per excellence, can be thought of allowing users to satisfy information needs. The main limitation of this paradigm is that it requires user’s involvement to identify the information they require: they must (1) express their needs by keywords and (2) must read through the documents to find the information they were looking for.

Information retrieval on the Web today makes little use of NLP processing. The perceived value of improved understanding is greatly outweighed by practical difficulty of storing complex linguistic annotations in a scalable indexing and search framework. Linguistics can help to identify automatically textual categorizations, organized as points of view of text mining, which can satisfy user’s needs. Our search engine tries to take advantage of both classical IR and QA systems. It acts like a classic search engine in which, a user formulates a query with terms and semantic categories and the IR systems answers by providing a list of references for documents containing textual segments (sentences, paragraphs, …), identified as discourse and semantic relations (causality, definition, quotation, …). It is identifiable to QA systems, by providing precise information and the user does not have to explore the document contents to satisfy his targeted needs, but our system does not use any knowledge database and does not process user’s queries as natural language expression.