In system development, it is an important issue how to reuse modules of existing systems. However, in a situation where systems similar to each other but customized differently are required continually, reuse of the modules becomes more difficult because relations among their functions are complicated. To this problem, we aim to establish a technology for extracting a model of functional structure in design documents of the existing system and utilizing it for development of other new systems. In this paper, we introduce our trial approach to extract the model and also discuss how to use a domain ontology in that process.

Knowledge graphs are useful for many artificial intelligence tasks. However, knowledge graphs often have missing facts. To populate knowledge graphs, the graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidates triples. Translation-based models are part of knowledge graph embedding models and they employ the translation-based principle. The principle can efficiently capture the rules of a knowledge graph, however TransE, the original translation-based model, has some problems. To solve them many extensions of TransE have been proposed. In this paper, we discuss such problems and models.

In this paper, we proposed methods that develop knowledge graph using ontology matching. Wikipedia, DBpedia, and other Linked Data resources are almost clustered by systematic ontologies, but some resource does not have ontologies it should be linked. "Structuring Wikipedia" project categorizes Wikipedia resources using Extended Named Entity (ENE). Since, DBpedia resources are based on Wikipedia, we use ENE for categorizing DBpedia resources.

Urban areas have many problems such as homelessness, illegally parked bicycles, and littering. These problems are influenced by various factors and are linked to each other; thus, an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles. Therefore, we propose constructing an urban problem linked open data (LOD) system that would include urban problems' causality. In addition, we propose a method for detecting vicious cycles of urban problems using inferences from the LOD. We first designed a Linked Data schema that represents urban problems' causality. Next, we instantiated actual causes and effects using crowdsourcing, supported with techniques based on natural language processing. In addition, we complemented the constructed LOD by drawing inferences using Semantic Web Rule Language (SWRL) rules. Finally, using SPARQL queries, we detected several root problems that led to vicious cycles, then urban-problem experts evaluated the extracted causal relations.

This paper proposes Crop Vocabulary(CVO) as a basis of core vocabulary of crop names that becomes the guidelines for data interoperability between agricultural ICT systems on the food chain. Since a single species is treated in different ways, there are many different types of crop names. So, we organize the crop name discriminated by properties such as scienti c name, planting method, edible part and registered cultivar name. CVO is also linked to existing vocabularies issued by Japanese government agency and international organization. It is expected to use in the data format in the agricultural ICT system.

We have been building structured manuals for care processes. It is useful to formalize them in a standard form which is defined by W3C because we can re-use some of the semantic technologies to process the structured manuals. This manuscript reports a trial to RDFize the structured manuals for care processes.

We have been prototyping a regional understanding support system by local food from 2016. In version 3, the mochi map becomes Linked Open Data,This paper discusses our perspective about this system and utilizing data in the future.