571 research outputs found

    OntoAna: Domain Ontology for Human Anatomy

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    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

    Domain-Specific Web Search with Keyword Spices

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    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

    Answering Student Programming Questions using Domain-specific Search

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    Discussion forums are commonly used in online learning environments for teaching programming, to create a platform for students to discuss course content. This platform of interaction is not without its challenges, as students regularly repeat questions that others have asked, both within and across offerings of a particular course. If past answers can be reliably provided to students, it eliminates the need for repetition and provides students with immediate assistance. This study investigates an approach to enable this through the addition of a search feature that indexes and queries discussion forum messages from a previous year to answer student questions. In particular, the paper presents a comparison of different ranking approaches based on the exploitation of domain-specific features of a social discussion forum on a learning management system, in particular, the authority of respondents. Results show that information retrieval can yield relevant answers to students in a programming course within the first 3-5 results, with some improvement in the outcomes when the social notion of authority in exploited

    Fine Grained Approach for Domain Specific Seed URL Extraction

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    Domain Specific Search Engines are expected to provide relevant search results. Availability of enormous number of URLs across subdomains improves relevance of domain specific search engines. The current methods for seed URLs can be systematic ensuring representation of subdomains. We propose a fine grained approach for automatic extraction of seed URLs at subdomain level using Wikipedia and Twitter as repositories. A SeedRel metric and a Diversity Index for seed URL relevance are proposed to measure subdomain coverage. We implemented our approach for \u27Security - Information and Cyber\u27 domain and identified 34,007 Seed URLs and 400,726 URLs across subdomains. The measured Diversity index value of 2.10 conforms that all subdomains are represented, hence, a relevant \u27Security Search Engine\u27 can be built. Our approach also extracted more URLs (seed and child) as compared to existing approaches for URL extraction

    A domain-specific search engine for the construction sector

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    Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

    Learning Classical Planning Strategies with Policy Gradient

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    A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the approaches. This enables using policy gradient to learn search strategies tailored to a specific distributions of planning problems and a selected performance metric, e.g. the IPC score. We instantiate the framework by constructing a policy space consisting of five search approaches and a two-dimensional representation of the planner's state. Then, we train the system on randomly generated problems from five IPC domains using three different performance metrics. Our experimental results show that the learner is able to discover domain-specific search strategies, improving the planner's performance relative to the baselines of plain best-first search and a uniform policy.Comment: Accepted for ICAPS 201
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