WAN (Wide Area Network) administrators have to use various knowledge in a com- plehensive way. Besides, it is important to clarify message semantics exchanged by interdomain network management applications. There are some standardized form of network knowledge such as MIB, YANG and IODEF, however, they are incompatible and not supposed to be used in a cross-sectional way. In this paper, we propose a network ontology called \\Bonsai"" toward general- purpose network management. Bonsai is a tool to integrate network knowledge in a structured way and designed as a database schema of knowledge base in KANVAS Project, which aims to realize WAN management.

It is important to share data from various system s , such as sensor network, time and motion study data, and text data. To handle and manage the collected data, t he authors have proposed a database framework called COTO database . In this paper, the authors propose a coto ontology as the basis of the COTO database. The coto ontology and COTO DB will be applied as a platform for evaluation of robotic d evices for nu r sing care.

It is required to develop biomimetics database that supports engineers to develop new products inspired by biological functions. Biomimetics ontologies are the foundation of the biomimetics database. There are two methods to expand biomimetics ontologies. We expanded them using technical document analysis so far. In this paper, we especially explain a method using Linked Open Data. We particularly discuss a trial to expand the ontology using DBpedia Japanese.

The potential expectation of companies has been increased for creating businesses by combining data from different domains, organizations, and sections. It is important to consider who stakeholders are and how they are involved in new businesses. However, it is difficult to create a business scenario by taking account of all the stakeholders in various domains, because the combination of stakeholders and their relationship with scenarios depend on the context of scenarios and have enrmous patterns. In this paper, we propose a stakeholder recommender system for supporting scenario generation of data utilization. We implement a system for externalizing relevant stakeholders and estimating stakeholders' relationship with scenarios considering the context of scenarios, using DBpedia and scenarios of data utilization scenario generated in Action Planning as knowledge base.

Ontologies are the basis of the Semantic Web. Owing to the cost of their construction and maintenance, however, there is much interest in automating their construction. Wikipedia is considered a promising source of knowledge because of its own characteristics. DBpedia extracts a large amount of ontological information from Wikipedia. However, DBpedia focuses exclusively on infoboxes (i.e., tables summarizing articles), and several works aim at extending DBpedia by using more information from Wikipedia. This paper builds upon this line of work, and focuses on the section titles and list structure to extend DBpedia. We develop an information extraction system using the list structure and extract more than 20 million triples using section titles as predicates. This suggests that there is ample potential to significantly expand the coverage of DBpedia.

Recently, many and various datasets are published as a Linked Open Data (LOD). SPARQL is an RDF query language and it provides a powerful way to access LOD. However, it is not easy to utilize them because it requires not only techniques of SPARQL but also knowledge of datasets and vocabularies that they used for users. In this paper, we analyze access logs of a SPARQL endpoint to show a difficulty of utilizing LOD and introduce our prototype system for sharing SPARQL query.

This article reports some papers, posters and demos which use DBpedia as a target, training data, and test data. They are all presented in 12th Extended Semantic Web Conference held between 31st May and 4th June 2015.