Web Information Retrieval using WordNet
نویسندگان
چکیده
Information retrieval (IR) is the area of study concerned with searching documents or information within documents. The user describes information needs with a query which consists of a number of words. Finding weight of a query term is useful to determine the importance of a query. Calculating term importance is fundamental aspect of most information retrieval approaches and it is traditionally determined through Term Frequency -Inverse Document Frequency (IDF). This paper proposes a new term weighting technique called concept-based term weighting (CBW) to give a weight for each query term to determine its significance by using WordNet Ontology. General Terms Term frequency (TF), Inverse Document Frequency (IDF), Vector Space Model, Extraction Algorithm.
منابع مشابه
Automatic Construction of Persian ICT WordNet using Princeton WordNet
WordNet is a large lexical database of English language, in which, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets). Each synset expresses a distinct concept. Synsets are interlinked by both semantic and lexical relations. WordNet is essentially used for word sense disambiguation, information retrieval, and text translation. In this paper, we propose s...
متن کاملSemantic Retrieval Approach for Web Documents
Because of explosive growth of resources in the internet, the information retrieval technology has become particularly important. However the current retrieval methods are essentially based on the full text matching of keywords approach lacking of semantic information and can’t understand the user's query intent very well. These methods return a large number of irrelevant information, and are u...
متن کاملUsing BFA with WordNet Based Model for Web Retrieval
The information retrieval [1,16] deals among other things with storage and retrieval of multimedia data, that can be usually represented as vectors in multidimensional space.This is especially suitable for text retrieval, where we store a collection (or corpus) of texts. More general, such a corpus can be a collection of web pages or, recently, of their annotated version as it is proposed in so...
متن کاملInformation Retrieval by Semantic Similarity
Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ...
متن کاملSynFinder: A System for Domain-Based Detection of Synonyms Using WordNet and the Web of Data
The detection of synonyms is a challenge that has attracted many contributions for the possible applications in many areas, including Semantic Web and Information Retrieval. An open challenge is to identify synonyms of a term that are appropriate for a specific domain, not just all the synonyms. Moreover, the execution time is critical when handling big data. Therefore, it is needed an algorith...
متن کامل