In this paper, we propose a standpoint-based Linked Data visualization approach, with the main goal of developing a Linked Data browser that receives an entity and visualizes the data related to it in various ways based on multiple "standpoints". Here, a standpoint means the data generated from a user's perspective of interest in an entity and it's visualization. For example, "volcano" and "World Heritage Site" are standpoints with respect to Mt. Fuji. Considering these standpoints, a user interested in "volcano" in our browser is shown types of volcanoes and the history of volcanic eruptions, while a user interested in "World Heritage Site" is shown a list of viewing spots and the list of visitor numbers per year. This feature is not found in existing Linked Data browsers that can be defined as "single" standpoint browsers. In this paper we present our proposed approach and describe a prototype system based on this approach.

セマンティックWebでは，RDFで記述されたリンクトデータの規模が拡大しており，その活用が重要となっている．通常，RDFデータの検索はクエリ言語SPARQLを用いて詳細な問い合わせを実現する．しかし，SPARQLによる検索は，クエリの記法に加え多くのデータセット固有の知識が利用者に要求され，このことがリンクトデータを統合して有効活用することを難しくしている．そこで本研究では，複数のキーワード間の関係性を導く検索と，複数のRDFデータセットを統合して容易に検索するための同値関係プロパティの推論方法を提案する．

本研究では，Wikipedia記事本文を構造化データとして活用するため，係り受け関係と述語項構造に基づく中間RDFグラフを提案する．また，その中間RDFグラフ上の構造からDBpedia上のプロパティに対応付けたRDFトリプルを抽出する．

Semantic labeling for numerical attributes is a process of matching numerical at- tributes in tabular resources to properties and classes in knowledge bases. It can be used in many applications such as table search, table extension, and knowledge augmentation. One of the chal- lenges of this tasks is to distinguish numerical attributes expressed in various scales or units of measurement. Indeed, how to distinguish the similar attributes of "human height - centimeters" and "human height - feet" and the dissimilar attribute "population - million". Previous studies assume the similar attributes expressed in the same scale. In fact, the similar attributes could be expressed differently since the data resource is constructed by different people in different back- ground and context. In this paper, we propose a novel method to improve the performance of semantic labeling for numerical attributes in various scales. We use an external knowledge about unit conversion taken from Wikidata to generate more data resources for the numerical background knowledge bases (WBKB). Our empirical experiments show that using the WBKB can improve the performance of semantic labeling expressed in various scales.

This paper describes graphic representation of the process of action based on guitar rendition ontology. We have systematized the knowledge of classical guitar for learning and teaching support, and developed guitar rendition ontology. The ontology defines action process of each rendition by using several properties. However, it is difficult to understand intuitively for players because the ontology presents specific description form. Therefore, in this study, we extracted the knowledge relevant to action from guitar rendition ontology and described action processes by graphical representation.

Wikipedia is a sort of online encyclopedia on the Internet. The Wikimedia Foundation, Inc, which is an American non-profit and charitable organization, manages and runs every language website of wikipedias. Although they have become huge and popular for humans, they also have some shortages for machines in the viewpoint of Semantic Webs and LODs. Aiming to propel the reuse of wikipedias for Semantic Webs and LODs, this paper discusses the pros and cons of Wikipedia, the current status of research and development from Semantic Web views, and forecasts the future in the direction of Wikipedia for humans to Wikipedia for machines.