WebSifter II: A Personalizable Meta-Search Agent based on Semantic Weighted Taxonomy Tree (original) (raw)

WebSifter II: A Personalizable Meta-Search Agent Based on Weighted Semantic Taxonomy Tree

2001

This paper addresses the problem of specifying, retrieving, filtering and rating Web searches so as to improve the relevance and quality of hits, based on the user's search intent and preferences. We present a methodology and architecture for an agent-based system, called WebSifter II, that captures the semantics of a user's decisionoriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. The terms in the tree can be refined by consulting a web taxonomy agent such as Wordnet. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to userspecified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating.

A semantic taxonomy-based personalizable meta-search agent

Proceedings of the Second International Conference on Web Information Systems Engineering

A Personalizable Agent for Semantic Taxonomy-Based Web Search

2002

This paper addresses the problem of specifying Web searches and retrieving, filtering, and rating Web pages so as to improve the relevance and quality of hits, based on the user’s search intent and preferences. We present a methodology and architecture for an agent-based system, called WebSifter II, that captures the semantics of a user’s decision-oriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. Consulting a Web taxonomy agent such as WordNet helps refine the terms in the tree. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating.

Intelligent Web Search via Personalizable Meta-search Agents

2002

This paper addresses several problems associated with the specification of Web searches, and the retrieval, filtering, and rating of Web pages in order to improve the relevance, precision and quality of search results. A methodology and architecture for an agent-based system, WebSifter is presented, that captures the semantics of a user's search intent, transforms the semantic query into target queries for existing search engines, and ranks resulting page hits according to a user-specified, weighted-rating scheme. Users create personalized search taxonomies, in the form of a Weighted Semantic-Taxonomy Tree. Consultation with a Webbased ontology agent refines the terms in the tree with positively-and negatively-related terms. The concepts represented in the tree are then transformed into queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, and page popularity. Experimental results indicate that WebSifter improves the precision of web searches, thereby leading to better information.

Learning for automatic personalization in a semantic taxonomy-based meta-search agent

Electronic Commerce Research and Applications, 2002

Providing most relevant page hits to the user is a major concern in Web search. To accomplish this goal, the user must be allowed to express his intent precisely. Secondly, page hit rating mechanisms should be used that take the user's intent into account. Finally, a learning mechanism is needed that captures a user's preferences in his Web search, even when those preferences are changing dynamically. Regarding the first two issues, we propose a semantic taxonomy-based meta-search agent approach that incorporates the user's taxonomic search intent. It also addresses relevancy improvement issues of the resulting page hits by using user's search intent and preferences based rating. To provide a learning mechanism, we represent the entire rating mechanism of semantic taxonomy-based meta-search agent approach as a feedforward neural network model and adopt the generalized delta rule as our basic learning scheme by modifying it to conform to our framework. Finally, the entire methodology including this learning mechanism is implemented in an agent-based system, WebSifter II. Empirical results of learning performance are also discussed.

PSSE: An Architecture For A Personalized Semantic Search Engine

INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, 2010

Semantic technologies promise a next generation of semantic search engines. General search engines don't take into consideration the semantic relationships between query terms and other concepts that might be significant to user. Thus, semantic web vision and its core ontologies are used to overcome this defect. The order in which these results are ranked is also substantial. Moreover, user preferences and interests must be taken into consideration so as to provide user a set of personalized results. In this paper we propose, an architecture for a Personalized Semantic Search Engine (PSSE). PSSE is a crawler-based search engine that makes use of multi-crawlers to collect resources from both semantic as well as traditional web resources. In order for the system to reduce processing time, web pages' graph is clustered, then clusters are annotated using document annotation agents that work in parallel. Annotation agents use methods of ontology matching to find resources of the semantic web as well as means of information extraction techniques in order to provide a well description of HTML documents. System ranks resources based on a final score that's calculated based on traditional link analysis, content analysis and a weighted user profile for more personalized results. We have a belief that the merge of these techniques together enhances search results.

Web search personalization with ontological user profiles

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management - CIKM '07, 2007

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.

A new architecture for web meta-search engines

2001

Web search engines have become the necessary tools for Web users to search useful information. However, users often face problems such as how to find the right information promptly with the least effort. This paper examines the limitations of current Web search engines and proposes a new Web meta-search engine architecture. The proposed architecture uses a multi-agent approach to process the user queries with greater personalization functionality and higher result quality than regular meta-search engines. Thus, the propose method improves the usability and efficiency of current search engines.

Enhancing Web Search with Semantic Identification of User Preferences

2011

Personalized web search is able to satisfy individual's information needs by modeling long-term and short-term user interests based on user actions, browsed documents or past queries and incorporate these in the search process. In this paper, we propose a personalized search approach which models the user search preferences in an ontological user profile and semantically compares this model against user

Agent-based web search personalization approach using dynamic user profile

Egyptian Informatics Journal, 2012

The World Wide Web has become the largest library through the history of the humanity. Having such a huge library made the search process more complex as the syntactic search engines offer an overwhelming amount of search results. Vocabulary problems like polysemy and synonymy can make the search results of traditional search engines irrelevant to users. Such problems trigger a strong need for personalizing the web search results based on user preferences. In this paper, we propose a new multi-agent system based approach for personalizing the web search results. The proposed approach introduces a model to build a user profile from initial and basic information, and maintain it through implicit user feedback to establish a complete, dynamic and up-to-date user profile. In the web search process, the model semantically optimizes the user query in two steps: query optimization using user profile preferences and query optimization using the WordNet ontology. The model builds on the advantages of the current search engines by utilizing them for retrieving the web search results. We present a detailed case study and simulation results evaluation to illustrate how the proposed model works and its expected value in increasing the precision of the traditional search engines and solving the vocabulary problems.

WebSifter II: A Personalizable Meta-Search Agent based on Semantic Weighted Taxonomy Tree (original) (raw)

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