筑波大学計算科学研究センター 平成28年度 年次報告書

まえがき　……　21 センター組織と構成員　……　32 平成 28 年度の活動状況　……　73 各研究部門の報告　……　10I. 素粒子物理研究部門　……　10II. 宇宙物理研究部門　……　36III. 原子核物理研究部門　……　64IV. 量子物性研究部門　……　88V. 生命科学研究部門　……　106 V-1. 生命機能情報分野　……　106 V-2. 分子進化分野　……　122VI. 地球環境研究部門　……　140VII. 高性能計算システム研究部門　……　154VIII. 計算情報学研究部門　……　205 Ⅷ-1. データ基盤分野　……　205 Ⅷ-2. 計算メディア分野　……　22

Enhancing Recommendations in Specialist Search Through Semantic-based Techniques and Multiple Resources

Information resources abound on the Internet, but mining these resources is a non-trivial task. Such abundance has raised the need to enhance services provided to users, such as recommendations. The purpose of this work is to explore how better recommendations can be provided to specialists in specific domains such as bioinformatics by introducing semantic techniques that reason through different resources and using specialist search techniques. Such techniques exploit semantic relations and hidden associations that occur as a result of the information overlapping among various concepts in multiple bioinformatics resources such as ontologies, websites and corpora. Thus, this work introduces a new method that reasons over different bioinformatics resources and then discovers and exploits different relations and information that may not exist in the original resources. Such relations may be discovered as a consequence of the information overlapping, such as the sibling and semantic similarity relations, to enhance the accuracy of the recommendations provided on bioinformatics content (e.g. articles). In addition, this research introduces a set of semantic rules that are able to extract different semantic information and relations inferred among various bioinformatics resources. This project introduces these semantic-based methods as part of a recommendation service within a content-based system. Moreover, it uses specialists' interests to enhance the provided recommendations by employing a method that is collecting user data implicitly. Then, it represents the data as adaptive ontological user profiles for each user based on his/her preferences, which contributes to more accurate recommendations provided to each specialist in the field of bioinformatics