Here is discussed how to build up Japanese Wikipedia Ontology and its use. Japanese Wikipedia Ontology has a lot of relations extracted from Japanese Wikipedia. It can be used as a hub on Japanese Linked Open Data cloud like DBpedia.

With the current semantic technology, Linked Data are generated with a set of triples, each of which consists of a subject, a predicate, and an object. Although it is possible to encode any piece of information with a set of triples, it is not easy to encode complex information with triples. This paper proposes a method for generating Linked Data from sentences generated according to an abstract grammar developed based on the grammar of natural languages. This method enables generation of Linked Information as well as Linked Data.

Web of data called Linked Data came to be published more and more. OpenRefine is a tool for working messy data, and RDF Refine is a OpenRefine extension for exporting RDF. So, we can transform data which is written in CSV file into RDF data easily with OpenRefine and RDF Refine. The other side, we have a problem that it's difficult to reuse metadata schema when we create new metadata. RDF Refine has a function to import another project of RDF Refine. In this paper, I propose the way to reuse a metadata schema with RDF Refine and Description Set Profile which is proposed by DCMI, when we want to transform metadata into RDF data.

This paper describes our RDF data set on local event information and access interface to it. The data set is automatically generated from a CSV file by applying conversion rules. The access interface enables users to search the data set by location, date and keyword through SPARQL server. Finally, the paper raises issues concerning dissemination of public information within RDF/LOD .

This paper describes an instance of Industrial, Academic and Government Collaborations in the context of open data. We have collected various sort of RDF datasets related to Sabae city in Fukui to provide our Sabae SPARQL Endpoint. Our smartphone application called "Sabae Burari" sends a SPARQL query dynamically to show POIs in Sabae on an illustrated map.

Suggested by Ramified Types by Bertrand Russell in his famous book "Principia Mathematica", we previously proposed the meta-modeling criteria in OWL Full descriptions in order to enable meta-modeling in RDF/OWL, but we did not address the formulation of RDF/OWL semantics yet according to the criteria. In this paper, we have introduced the orders of classes into RDF/OWL semantics and then re-formulated the RDF semantics and OWL semiFull semantics, which inhibits OWL inference on properties as resources. Thus, the ambiguous wording "punning" by W3C is fixed on the ground of meta-modeling theory by Ramified Types and it helps us to deeply understand the semantics and the mechanism of RDF/OWL systems that allow us ontological meta-modeling.

Quality of ontology is important because it is connected directly with the performance of an application system using the ontology. However ontology refinement to improve its quality needs knowledge and experiments in ontology development. Therefore, ontology refinement task is too difficult especially for beginners in ontology building. In order to solve this problem this article proposes an ontology refinement support system based on similarity among is-a hierarchies and an evaluation of it. The system can support content refinements for ontologies.

Open Data is now collecting attention for innovative service creation, mainly in the area of government, bioscience, and smart X project. However, to promote its application more for consumer services, a search engine for Open Data to know what kind of data are there would be of help. This paper presents a voice assistant which uses Open Data as its knowledge source. It is featured by improvement of accuracy according to the user feedbacks, and acquisition of unregistered data by the user participation. We also show an application to support for a field-work and confirm its effectiveness.

Recently, there are various semantic data such as linked data, knowledge graph, ontologies and so on. How to explore them and get appropriate information are very important techniques for intelligent application systems based on them. In this article, we focus on intelligent exploration of ontologies since ontologies provide systematized knowledge to understand target domain and contribute to deep understanding of semantic data. Although concept search is very important and fundamental technique for ontology exploration, there are some rooms to improve it in order to deal with conceptual structure of ontologies more efficiently. The authors propose a novel conceptual search method called "Multistep Expansion based Concept Search" to get appropriate concepts from ontologies according to the user's intentions and purpose.

In research of drug design, bioremediation, etc., it is an overriding theme to get to know a proteinic structure and function. System for research is first extracted from databases by ontology, and cost mitigation of a biological experiment can be expected by next performing machine learning and a simulation. This research showed utilizing LOD as a technique for construction of the database of a "Protein-Ligand Binding Site Pair" by the proteomics in agriculture life science and the drug design field. Data of interatomic distance were able to be annotated by RDF of protein structure and a small molecular compound database. Moreover, in order to add a protein sequence database to LOD, the index for employing large-scale RDF by performance measurement of a triple store in the cloud environment was able to be obtained. This will be useful when developing the application of life science.