List of Articles search engine

• Open Access Article
  • Abstract Page
  • Full-Text
  
  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
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Speed up the Search for Proximity-Based Models
  J. Paksima A. Zareh V. Derhami
  One of the main challenges in the proximity models is the speed of data retrieval. These models define a distance concept which is calculated based on the positions of query terms in the documents. This means that finding the positions and calculating the distance is a More

  One of the main challenges in the proximity models is the speed of data retrieval. These models define a distance concept which is calculated based on the positions of query terms in the documents. This means that finding the positions and calculating the distance is a time consuming process and because it usually executed during the search time it has a special importance to users. If we can reduce the number of documents, retrieval process becomes faster. In this paper, the SNTK3 algorithm is proposed to prune documents dynamically. To avoid allocating too much memory and reducing the risk of errors during the retrieval, some documents' scores are calculated without any pruning (Skip-N). The SNTK3 algorithm uses three pyramids to extract documents with the highest scores. Experiments show that the proposed algorithm can improve the speed of retrieval. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - Search Engine for Structured Event Retrieval from News Sources
  A. mirzaeiyan s. aliakbary
  Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detect More

  Analysis of published news content is one of the most important issues in information retrieval. Much research has been conducted to analyze individual news articles, while most news events in the media are published in the form of several related articles. Event detection is the task of discovering and grouping documents that describe the same event. It also facilitates better navigation of users in news spaces by presenting an understandable structure of news events. With rapid and increasing growth of online news, the need for search engines to retrieve news events is felt more than ever. The main assumption of event detection is that the words associated with an event appear in the same time windows and similar documents. Accordingly, in this research, we propose a retrospective and feature-pivot method that clusters words into groups according to semantic and temporal features. We then use these words to produce a time frame and a human readable text description. The proposed method is evaluated on the All The News dataset, which consists of two hundred thousand articles from 15 news sources in 2016 and compared to other methods. The evaluation shows that the proposed method outperforms previous methods in terms of precision and recall. Manuscript profile