Toru Ishida (Kyoto University)
Invited Talk (one hour)
Jim Hendler (Rensselaer Polytechnic Institute)
Hideaki Takeda (NII)Title: Design Process of Agriculture Ontologies
In this talk, I will talk about the experience of designing and developing the agriculture ontologies, in particular, how the structure of the ontologies were defined as the agreement. We are currently developing two ontologies: the ontology for agriculture activity (AAO) and the ontology for crop. Since they are domain ontologies, there needs the collaboration between computer scientists and domain experts. We learnt the importance of iterative interaction between two sides. Furthermore we have two parties in domain experts. One is the member of the developing team who know computer science and agriculture both and the other is the experts who only know agriculture. The former is important to bridge the gap between knowledge about computer science and agriculture.
Hideaki Takeda, Prof. Dr.Eng,
Hideaki Takeda is a professor at National Institute of Informatics (NII) Japan, and a professor at the Graduate University for Advanced Studies (Sokendai). He received B. Eng., M. Eng. and Dr. Eng. degrees in precision machinery engineering from the University of Tokyo, Japan, in 1986, 1988 and 1991, respectively. He worked at Norwegian Institute of Technology and Nara Institute of Technology prior to joining the current institution. He has been the Sumitomo endowed professor in the University of Tokyo between 2005 and 2010. He has also been the director of Research and Development Center for Scientific Information Resources in NII between 2006 and 2010. His research interests include Semantic Web, knowledge sharing systems and scholarly communication management.
Hiroshi Masuya (RIKEN BioResource Center)|
Title: RDF based integration of biological phenotype data produced in Japan
In the life science, bio-resource as an experimental material is one of the most important fundamentals to secure reproducibility of the experimental results. Phenotype data is also crucial for researchers to choose appropriate experimental materials for their studies. Recently sharing of phenotype data of experimental animals are performed various databases using phenotype ontologies as standardized vocabularies. Using Resource Description Framework (RDF) related technologies, candidates of disease model can be shown through the disease-phenotype relationship between across human, rodents, fish, worms and flies. Therefore, dissemination of phenotype-related information, RDF technologies becomes one of the best solutions. For wider data dissemination of experimental animals produced in Japan, we worked out a data integration project, "J-Phenome" (http://jphenome.info/), to develop RDF data of Japanese bio-resources. I would like to overview the development of RDF datasets and discuss advantage of RDF for sharing phenotype data in Japan and worldwide.
Hiroshi Masuya received PhD in School of Life Science, The Graduate University for Advanced Studies (National Institute Genetics) in 1996. He is currently unit leader of the RIKEN BioResource center. His research interest is development of integrated database of biological phenotype revealing relationships between phenotypes and human diseases using Semantic Web technologies.
Sadao Kurohashi (Kyoto University)|
Title: Knowledge-Intensive Structural Natural Language Processing
There have been two obstacles to make machines understand language: lack of knowledge and lack of methodology to utilize knowledge. The former was basically solved by the emergence of the web. A huge volume of texts on the web including Wikipedia have become a resource for knowledge extraction. The latter hard problem also has started to be alleviated by Deep Learning recently. In this talk, I will present our recent research achievements related to knowledge-intensive structural natural language processing.
Sadao Kurohashi received the B.S., M.S., and PhD in Electrical Engineering from Kyoto University in 1989, 1991 and 1994, respectively. He is currently a professor of the Graduate School of Informatics at Kyoto University. His research interests include natural language processing, knowledge acquisition/representation, and information retrieval. He received the 10th and 20th anniversary best paper awards from Journal of Natural Language Processing in 2004 and 2014 respectively, 2009 Funai IT promotion award, and 2009 IBM faculty award.
Yohei Murakami (Kyoto University)|
Title: The Language Grid: Towards an Interoperable Language Service Infrastructure Fragmentation and recombination is the key to creating a full range of customized language environments for different types of user activities. Fragmentation provides various components for the customized language environments and recombination builds each language environment according to user's request by combining these components. To realize the fragmentation and recombination process, we proposed the Language Grid by introducing the service-oriented approach to language resource domain. This infrastructure wraps existing language ron.
Yohei Murakami has been an associate professor of Unit of Design at Kyoto University since 2014. He received his Ph.D. degree in informatics from Kyoto University in 2006. His research interests lie in services computing and multi agent systems. He founded the Technical Committee on Services Computing in the Institute of Electronics, Information and Communication Engineers in 2009. He has also started International Workshop on Worldwide Language Service Infrastructure (WLSI) and served as a workshop chair of WLSI 2013-2016. Since 2006, he has been working on the Language Grid for almost ten years, and established NPO Language Grid.esources as atomic language services and enables users to compose new services by combining them. This talk explains the design concept and service architecture of the Language Grid, and introduces the concept of Open Language Grid, which aims at providing a worldwide language service infrastructure for language resource sharing through global collaborati
Makoto Nakatsuji (NTT Resonant Inc.)|
Title: Semantic sensitive simultaneous tensor factorization and related applications
The semantics distributed over large-scale knowledge bases can be used to intermediate heterogeneous users' activity logs created in services; such information can be used to improve applications that can help users to decide the next activities/services. Since user activities can be represented in terms of relationships involving three or more things (e.g. a user tags movie items on a webpage), tensors are an attractive approach to represent them. The recently introduced Semantic Sensitive Tensor Factorization (SSTF) is promising as it achieves high accuracy in predicting users' activities by basing tensor factorization on the semantics behind objects (e.g. item categories). However, SSTF currently focuses on the factorization of a tensor for a single service and thus has two problems: (1) the balance problem occurs when handling heterogeneous datasets simultaneously, and (2) the sparsity problem triggered by insufficient observations within a single service. Our solution, Semantic Sensitive Simultaneous Tensor Factorization (S3TF), tackles the problems by: (1) Creating tensors for individual services and factorizing them simultaneously; it does not force the creation of a tensor from multiple services and factorize the single tensor. This avoids the low prediction accuracy caused by the balance problem. (2) Utilizing shared semantics behind distributed activity logs and assigning semantic bias to each tensor factorization. This avoids the sparsity problem by sharing semantics among services. Experiments using real-world datasets show that S3TF achieves higher accuracy in rating prediction than the current best tensor method. It also extracts implicit relationships across services in the feature spaces by simultaneous factorization with shared semantics. The presentation also introduces the question and answering system that creates answers for non-factoid questions using the related ideas with the above S3TF. It is practical and has already been applied to the love advice service in Oshiete goo.
Makoto Nakatsuji is a Manager in NTT Resonant Inc. He completed his Ph.D. in Social Informatics at Kyoto University Graduate School of Informatics in 2010. He was a visiting scholar in The Tetherless World Constellation at Rensselaer Polytechnic Institute in 2013.His research interests include semantic data mining, recommendation, deep learning, Question Answering systems, and graph analysis.
Kazuo Kajimoto (Panasonic Corporation)|
Title: Semantics description lifecycle in IoT eco-system according to business flow
(Semantics description lifecycle in IoT eco-system according to business flow)
Koiti Hasida (The University of Tokyo)|
Title: Linguistic Annotation Diagram: An ISO Standard Candidate for Semantic Authoring
Tatsuya Kushida (National Bioscience Database Center)|
Title: Developing a new life science ontology from JST thesaurus
Takeshi Morita (Keio University)|
Title: PRINTEPS: A Total Intelligent Application Development Platform
Naoaki Okazaki (Tohoku University)|
Title: Knowledge acquisition using deep neural network
Makoto Nakatsuji (NTT Resonant Inc.)|
Title: CAN AI GENERATE LOVE ADVICE?: TOWARD NEURAL ANSWER GENERATION FOR NON-FACTOID QUESTIONS
Shusaku Egami (The University of Electro-Communications)|
Title: IPBLOD: Linked Open Data for Solving Illegally Parked Bicycles
Dan Fujimura (Kyoto University) |
Title: Matching Service using SPARQL
Closing Takahiro Kawamura (JST)
Poster session and Reception 17:00-19:30 (food will be available from 18:00)