LODAC Distiller is a platform to transform HTML fles of web sites into RDF fles to be able to provide Linked Data. LODAC Distiller extracts pairs of a key and a value such as a collection's title or an author's name from HTML fles, then it maps the pairs to appropriate RDF properties and objects by using original mapping rules. LODAC Museum uses LODAC Distiller for generating museum collection datasets from various museum web sites.

An information system for biodiversity data using Linked Data technology was developed as a part of LODAC (Linked Open Data for ACademia) project, which aims to construct a framework to share and integrate academic data. As a pilot study, a latest species name list of Japanese butterflies was converted into Linked Data format, integrated with relevant biodiversity data such as museum specimen collections and extended for collection data of bryophyta deposited at National Institute of Polar Research. This integrated dataset is available via our website and a SPARQL endpoint. This system will facilitate use of heterogeneous biodiversity and its relevant resources.

The vast amount of various life sciences data at RIKEN and other institutes including ge-nome, transcriptome, proteome, metabolome, and phenome data are ontologically integrated into a com-mon system. The challenge is to facilitate data retrieval, integration and collaboration. BioLOD.org - the Biological Linked Open Data database (http://biolod.org) - provides over 6,800 downloadable OWL/RDF graph files of mutually linked public biological data organized as a semantic web using standardized for-mats of the World Wide Web Consortium Linking Open Data (W3C LOD) project. BioLOD.org mines numerous semantic links from original databases and re-classifies them into graph files based on ontology classifications. Relationships between the files are mutually and clearly referenced so it is easy to find other files associated by semantic links included in detailed data instances. BioLOD.org intensively sur-veyed both forward and reverse semantic link relationships from 36 databases for humans and mice, 33 databases for plants and 16 databases related to protein experiments and structures. BioLOD summarizes this information as archive files available for download in various useful formats. The BioLOD.org data-base uniquely provides Linked Open Data annotated contextually with biological vocabulary and supports visualization services to browse LOD data through SciNetS.org, repository services to deposit users' LOD through LinkData.org and SPARQL endpoint service for BioLOD data is through BioSPARQL.org.

Recently, home garden and green interior have been receiving attention due to the growing environmental consciousness and macrobiotics. However, it is not very simple to grow the greenery in an urban restricted space, then it occasionally results in overgrowth or extinction. Also, it is important for interior / exterior use to balance the greenery to a user's surrounding, but it's difficult for amateurs to image its grown form in future. Therefore, we propose an Android application to query a plant from the LOD Cloud to fit an environmental condition using sensors on smart phone, and overlay its grown form in the space using AR.

We had transformed English WordNet into RDF according to the guideline from W3C and provided RDF fles and the way of web browsing of WordNet. While Japanized WordNet is recently published, it is not in the LOD style and does not include links to other existing resources of Japanese word collections and dictionaries. Therefore, RDFization and building LOD as a whole of English/Japanese multi-lingual WordNet is an urgent requirement in today's situation. We also have gained several experiences of LOD in other felds, and the experiences are revealing a new question that should be categorized into pragmatics of RDF that teaches how to RDFize resources and data in each particular context and objective. In this report, we discuss how to RDFize English and Japanese WordNet and how to extend RDFized WordNet to LOD with other existing resources of Japanese words.

Japanese Wikipedia Ontology, which we have constructed semi-automatically from Japanese Wikipedie, has problems of lacking upper classes and appropriate definition of properties. The purpose of our research is to complement the upper classes in Japanese Wikipedia Ontology and build up a class hierarchy with properties by integrating Japanese Wikipedia Ontology and Japanese WordNet. In this paper, we propose a method to build up the class hierarchy with properties by lifting up common properties which are defined in sibling classes to more upper classes in Japanese Wikipedia Ontology. We also introduce an attempt to integrate Japanese Wikipedia Ontology and Japanese WordNet.

Recently, various ontologies and applications based-on them are developed. For ontology-based applications, it is very important to acquire appropriate concepts requested by the system developer. In this article, we propose "Multi-Level-Expansion Search" for properly acquisition of concepts from ontologies according to the developer's intentions.

Recently, the internet utilization rate has exceeded 90%, then e-commerce market is accordingly expanding. However, users without any expertise in fashion brands and designs have a problem to search one from enormous items. In this paper, we propose an internet service which picks out the items matching for user's favorite design extracted from the user's browsing items. Specifcally, we introduce a fashion ontology, then estimate a favorite design class using an image recognition method. This paper shows a preliminary experiment and discuss the future work.