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1 - Automatic Construction of Domain Ontology Using Wikipedia and Enhancing it by Google Search Engine
Sedigheh Khalatbari
10.7508/jist.2015.04.006

The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of doma More

The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals’ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more than the information that was obtained at the stage of automatic construction of ontology from Wikipedia. Manuscript profile
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2 - Effective solving the One-Two Gap Problem in the PageRank algorithm
Javad Paksima Homa Khajeh
10.7508/jist.2018.21.007

One of the criteria for search engines to determine the popularity of pages is an analysis of links in the web graph, and various methods have already been presented in this regard. The PageRank algorithm is the oldest web page ranking methods based on web graph and is More

One of the criteria for search engines to determine the popularity of pages is an analysis of links in the web graph, and various methods have already been presented in this regard. The PageRank algorithm is the oldest web page ranking methods based on web graph and is still used as one of the important factors of web pages on Google. Since the invention of this method, several bugs have been published and solutions have been proposed to correct them. The most important problem that is most noticed is pages without an out link or so-called suspended pages. In web graph analysis, we noticed another problem that occurs on some pages at the out degree of one, and the problem is that under conditions, the linked page score is more than the home page. This problem can generate unrealistic scores for pages, and the link chain can invalidate the web graph. In this paper, this problem has been investigated under the title "One-Two Gap", and a solution has been proposed to it. Experimental results show that fixing of the One-Two gap problem using the proposed solution. Test standard benchmark dataset, TREC2003, is applied to evaluate the proposed method. The experimental results show that our proposed method outperforms PageRank method theoretically and experimentally in the term of precision, accuracy, and sensitivity with such criteria as PD, P@n, NDCG@n, MAP, and Recall. Manuscript profile
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3 - Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features
Elham Esmaeeli-Gohari Sajjad Zarifzadeh
10.7508/jist.2020.01.004

Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the More

Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e.g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e.g., by more than 20% in terms of p@10, compared with some well-known algorithms. Manuscript profile

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