The NICT Science Cloud is one of the science clouds proposed for development of sciences. A variety of science data are collected and stored in the science cloud to be analyzed interdisciplinary. After the Internet is widely used, new concept and information technology haves shown up; semantic web and linked open data (LOD). These technologies enable information on the Internet machine readable. In many science fields, it is pointed out that the semantic web will play an important role for the interdisciplinary research works. However, there have been few ideas to be ever proposed as a methodology or roadmap to the interdisciplinary science using semantic web. Herein we present a concept of professional knowledge and academic knowledge following collective knowledge proposed as a Web 2.0. Based on the concept, we design an application for interdisciplinary science.
This paper proposes a validation system of a real property registration application. In order to show the validity of the data, we construct two ontologies described by OWL and SWRL, and use the ability of reasoning. In addition, we introduce extended rules described by JAVA, and use these reasoning.
When writing SPARQL queries that will access to multiple data sources (i.e., a federated querying), one has to know the corresponding vocabulary where one can name the property URIs to be used in the query. To know the exact URIs for such vocabulary is not easy task since those vocabularies are often separately defined and managed. To avoid such a situation, the techniques to use some ontology mappings in a SPARQL query have been actively developed. In this paper, we present our preliminary approach on SPARQLoid query coding support system that can utilize familiar vocabularies that may be contained in some ontology mappings by using weighted ontology mappings associated to specified keywords in the query.
This paper discusses how to automatically discover "sameAs" and "meaningOf" links from Japanese Wikipedia in order to extend Japanese Wikipedia Ontology. We gathered relevant features such as IDF, string similarity, number of hypernym, and so on. We have identified the link-based score on salient features based on SVM results with 960,000 anchor link pairs. These case studies show us that our link discovery method goes well with more than 70% precision/recall rate..
Over the linked data information retrieval, adaptation of temporal features such as date, time or time of an event, is paid little attention. Therefore, we propose a keywordbased linked data information retrieval framework, called TLDRet, that can incorporate temporal features and able to generate more concise results. Preliminary evaluation of our system shows promising performance.
The objective of this work is to classify poses of idols wearing swimsuits in the photograph. We propose classification system, which takes unannotated idol photos as inputs and classifies their poses based on the spatial layout of idol in the photo. Our system has two phases; the first phase is to estimate the spatial layout of ten body parts (head, torso, upper/lower arms/legs) based on Eichner's Stickman Pose Estimation method. The second phase is to classify the poses of idols in the photo using Bayesian Network classifier. In order to improve the accuracy of classification, we introduce Pose Guide Ontology (PGO). PGO contains useful background knowledge such as semantic hierarchies and constraints related to the orientation and positional relationship between the body parts.