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.

scholarly journals Metaheuristic for Word Sense Disambiguation: a Review

2018 ◽  
Vol 7 (3.20) ◽  
pp. 428
Author(s):  
Wafaa AL-Saiagh ◽  
Sabrina Tiun ◽  
Ahmed AL-Saffar ◽  
Suryanti Awang ◽  
A. S. Al-khaleefa
Keyword(s):  
Essential Role ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Human Language ◽  
Single Word ◽  
Search Results ◽  
Fields Of Study ◽  
Learning Techniques ◽  
Sense Disambiguation

Word Sense Disambiguation (WSD) is the process of determining the exact sense of a particular word in accordance to the context in a computational manner. Such task plays an essential role in multiple fields of study such as Information Retrieval and Information Extraction. With the complexity of human language, WSD came up to solve the problem behind the ambiguity between senses in which a single word would yield different meaning. In this vein, determining the exact meaning of the certain word would facilitate the process of identifying the category of such text, accurate corresponding search results and providing an accurately summarized portion. Several approaches have been proposed for the WSD including statistical, semantic and machine learning techniques. This paper aims to provide a review of such approaches by tackling and categorizing the related works in accordance to the main types. 

Extracting Concepts’ Relations and Users’ Preferences for Personalizing Query Disambiguation

2011 ◽  
pp. 66-80
Author(s):  
Yan Chen ◽  
Yan-Qing Zhang
Keyword(s):  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Relations ◽  
Word Sense ◽  
Web Searching ◽  
Knowledge Models ◽  
Clickthrough Data ◽  
Sense Disambiguation

For most Web searching applications, queries are commonly ambiguous because words usually contain several meanings. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge models to find the most appropriate sense for the ambiguous word. Since queries are usually short, the contexts of the queries may not always provide enough information for disambiguating queries. Thus, more than one interpretation may be found for one ambiguous query. In this paper, we propose a cluster-based WSD method, which finds out all appropriate interpretations for the query. Because some senses of one ambiguous word usually have very close semantic relations, we group those similar senses together for explaining the ambiguous word in one interpretation. If the cluster-based WSD method generates several contradictory interpretations for one ambiguous query, we extract users’ preferences from clickthrough data, and determine suitable concepts or concepts’ clusters that meet users’ interests for explaining the ambiguous query.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

scholarly journals Multimodal Word Sense Disambiguation in Creative Practice

2020 ◽  
Author(s):  
Manuel Ladron de Guevara ◽  
Christopher George ◽  
Akshat Gupta ◽  
Daragh Byrne ◽  
Ramesh Krishnamurti
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Creative Practice ◽  
Sense Disambiguation
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