Domain-specific queries and Web search personalization: some investigations

Major search engines deploy personalized Web results to enhance users' experience, by showing them data supposed to be relevant to their interests. Even if this process may bring benefits to users while browsing, it also raises concerns on the selection of the search results. In particular, users may be unknowingly trapped by search engines in protective information bubbles, called "filter bubbles", which can have the undesired effect of separating users from information that does not fit their preferences. This paper moves from early results on quantification of personalization over Google search query results. Inspired by previous works, we have carried out some experiments consisting of search queries performed by a battery of Google accounts with differently prepared profiles. Matching query results, we quantify the level of personalization, according to topics of the queries and the profile of the accounts. This work reports initial results and it is a first step a for more extensive investigation to measure Web search personalization.Comment: In Proceedings WWV 2015, arXiv:1508.0338

OntoAna: Domain Ontology for Human Anatomy

Today, we can find many search engines which provide us with information which is more operational in nature. None of the search engines provide domain specific information. This becomes very troublesome to a novice user who wishes to have information in a particular domain. In this paper, we have developed an ontology which can be used by a domain specific search engine. We have developed an ontology on human anatomy, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system. This information can be used by people working in medical and health care domain.Comment: Proceedings of 5th CSI National Conference on Education and Research. Organized by Lingayay University, Faridabad. Sponsored by Computer Society of India and IEEE Delhi Chapter. Proceedings published by Lingayay University Pres

ProThes: Thesaurus-based Meta-Search Engine for a Specific Application Domain

In this poster we introduce ProThes, a pilot meta-search engine (MSE) for a specific application domain. ProThes combines three approaches: meta-search, graphical user interface (GUI) for query specification, and thesaurus-based query techniques. ProThes attempts to employ domain-specific knowledge, which is represented by both a conceptual thesaurus and results ranking heuristics. Since the knowledge representation is separated from the MSE core, adjusting the system to a specific domain is trouble free. Thesaurus allows for manual query building and automatic query techniques. This poster outlines the overall system architecture, thesaurus representation format, and query operations. ProThes is implemented on J2EE platform as a Web service.The project was supported in part by the Russian Fund of Basic Research, grant # 03-07-90342

Thesaurus-assisted search term selection and query expansion: a review of user-centred studies

This paper provides a review of the literature related to the application of domain-specific thesauri in the search and retrieval process. Focusing on studies which adopt a user-centred approach, the review presents a survey of the methodologies and results from empirical studies undertaken on the use of thesauri as sources of term selection for query formulation and expansion during the search process. It summaries the ways in which domain-specific thesauri from different disciplines have been used by various types of users and how these tools aid users in the selection of search terms. The review consists of two main sections covering, firstly studies on thesaurus-aided search term selection and secondly those dealing with query expansion using thesauri. Both sections are illustrated with case studies that have adopted a user-centred approach

Domain-Specific Web Search with Keyword Spices

Domain-specific web search engines are effective tools for reducing the difficulty in acquiring information from the web. Existing methods for building domain-specific web search engines require human expertise or specific facilities. However, we can build a domain-specific search engine simply by adding domain specific keywords called "keyword spices" to the user's input query and forwarding it to a generalpurpose web search engine. Keyword spices can be effectively discovered from web documents using machine learning technologies. This paper will describe domain-specific web search engines that use keyword spices for locating cooking recipes, restaurants, and used cars. To fully automate the construction of domain-specific search engines, we also present trials of using web pages in an existing web directory as training examples