Ontologies and Information Systems for the Semantic Web最新文献

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Incorporation of corpus-specific semantic information into question answering context 将语料库特定的语义信息整合到问答上下文中

Ontologies and Information Systems for the Semantic Web Pub Date : 2008-10-30 DOI: 10.1145/1458484.1458497

Protima Banerjee, Hyoil Han

{"title":"Incorporation of corpus-specific semantic information into question answering context","authors":"Protima Banerjee, Hyoil Han","doi":"10.1145/1458484.1458497","DOIUrl":"https://doi.org/10.1145/1458484.1458497","url":null,"abstract":"In today's environment of information overload, Question Answering (QA) is a critically important research area for the Semantic Web. In order for humans to make effective use of the expansive information sources available to us, we require automated tools to help us make sense of large amounts of data. Within this framework, Question Context plays an important role. We define Question Context to be an semantic structure that can be used to enrich queries so that the user's information need is better represented. This paper describes the theoretical foundations of a novel approach that uses statistical language modeling techniques to create Question Context and to then integrate it into the Information Retrieval stage of QA. We base our approach on two established language modeling methods - the Aspect Model, which is the basis of Probabilistic Latent Semantic Analysis (PLSA) and Relevance-Based Language Models. Our approach proposes an Aspect-Based Relevance Language Model as the Question Context Model, and our methodology incorporates corpus-specific semantic concepts into the QA process. Words from the most heavily relevant aspects are then incorporated into the query. We present some interesting preliminary qualitative results that show the potential usefulness of the Question Context Model to both the first (IR) and second (Intelligent Information Processing) stages of QA.","PeriodicalId":363359,"journal":{"name":"Ontologies and Information Systems for the Semantic Web","volume":"390 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134479499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Personalized cluster-based semantically enriched web search for e-learning 面向电子学习的个性化基于聚类的语义丰富网络搜索

Ontologies and Information Systems for the Semantic Web Pub Date : 2008-10-30 DOI: 10.1145/1458484.1458498

Leyla Zhuhadar, O. Nasraoui

{"title":"Personalized cluster-based semantically enriched web search for e-learning","authors":"Leyla Zhuhadar, O. Nasraoui","doi":"10.1145/1458484.1458498","DOIUrl":"https://doi.org/10.1145/1458484.1458498","url":null,"abstract":"We present an approach for personalized search in an e-learning platform, that takes advantage of semantic Web standards (RDF and OWL) to represent the content and the user profiles. Personalizing the finding of needed information in an e-learning environment based on context requires intelligent methods for representing and matching the learning needs and the variety of learning contexts. Our framework consists of the following phases: (1) building the semantic e-learning domain using the known college and course information as concepts and sub-concepts in a lecture ontology, (2) generating the semantic learner's profile as an ontology from navigation logs that record which lectures have been accessed, (3) clustering the documents to discover more refined sub-concepts (top terms in each cluster) than provided by the available college and course taxonomy, (4) re-ranking the learner's search results based on the matching concepts in the learning content and the user profile, and (5) providing the learner with semantic recommendations during the search process, in the form of terms from the closest matching clusters of their profile. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner's context can be effectively used for improving the precision and recall in e-learning search, particularly by re-ranking the search results based on the learner's past activities.","PeriodicalId":363359,"journal":{"name":"Ontologies and Information Systems for the Semantic Web","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121419471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

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