scholarly journals PageRank-based Word Sense Induction within Web Search Results Clustering

2014 ◽  
Author(s):  
Jose G. Moreno ◽  
Gael Dias
Keyword(s):  
Web Search ◽  
Word Sense ◽  
Search Results ◽  
Word Sense Induction ◽  
Search Results Clustering

scholarly journals Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

2013 ◽  
Vol 39 (3) ◽  
pp. 709-754 ◽  
Author(s):  
Antonio Di Marco ◽  
Roberto Navigli
Keyword(s):  
Information Search ◽  
Web Search ◽  
Word Sense ◽  
Clustering Methods ◽  
Search Results ◽  
Search Result ◽  
Word Sense Induction ◽  
Novel Approach ◽  
Web Clustering ◽  
Word Senses

Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query. In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (i.e., meanings) of an ambiguous query and then cluster the search results based on their semantic similarity to the word senses induced. Our experiments, conducted on data sets of ambiguous queries, show that our approach outperforms both Web clustering and search engines.

scholarly journals Graph-Based Concept Clustering for Web Search Results

2015 ◽  
Vol 5 (6) ◽  
pp. 1536 ◽  
Author(s):  
Supakpong Jinarat ◽  
Choochart Haruechaiyasak ◽  
Arnon Rungsawang
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Knowledge Source ◽  
Short Text ◽  
Clustering Techniques ◽  
External Knowledge ◽  
Search Results ◽  
Clustering Quality ◽  
Informative Content ◽  
Search Results Clustering

A search engine usually returns a long list of web search results corresponding to a query from the user. Users must spend a lot of time for browsing and navigating the search results for the relevant results. Many research works applied the text clustering techniques, called web search results clustering, to handle the problem. Unfortunately, search result document returned from search engine is a very short text. It is difficult to cluster related documents into the same group because a short document has low informative content. In this paper, we proposed a method to cluster the web search results with high clustering quality using graph-based clustering with concept which extract from the external knowledge source. The main idea is to expand the original search results with some related concept terms. We applied the Wikipedia as the external knowledge source for concept extraction. We compared the clustering results of our proposed method with two well-known search results clustering techniques, Suffix Tree Clustering and Lingo. The experimental results showed that our proposed method significantly outperforms over the well-known clustering techniques.

A Genetic Fuzzy Semantic Web Search Agent Using Granular Semantic Trees for Ambiguous Queries

2010 ◽  
pp. 426-438
Author(s):  
Yan Chen ◽  
Yan-Qing Zhang
Keyword(s):  
Fuzzy Logic ◽  
Web Search ◽  
Word Sense Disambiguation ◽  
Knowledge Systems ◽  
Web Pages ◽  
Word Sense ◽  
Web Searching ◽  
Search Results ◽  
Sense Disambiguation ◽  
Semantic Tree

For most Web searching applications, queries are commonly ambiguous because words or phrases have different linguistic meanings for different Web users. The conventional keyword-based search engines cannot disambiguate queries to provide relevant results matching Web users’ intents. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge systems to measure associations among words. The contexts of queries are used for disambiguation in these methods. However, due to the fact that numerous combinations of words may appear in queries and documents, it is difficult to extract concepts’ relations for all possible combinations. Moreover, queries are usually short, so contexts in queries do not always provide enough information to disambiguate queries. Therefore, the traditional WSD methods are not sufficient to provide accurate search results for ambiguous queries. In this chapter, a new model, Granular Semantic Tree (GST), is introduced for more conveniently representing associations among concepts than the traditional WSD methods. Additionally, users’ preferences are used to provide personalized search results that better adapt to users’ unique intents. Fuzzy logic is used to determine the most appropriate concepts related to queries based on contexts and users’ preferences. Finally, Web pages are analyzed by the GST model. The concepts of pages for the queries are evaluated, and the pages are re-ranked according to similarities of concepts between pages and queries.

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