Knowledge graphs enable data integration, knowledge discovery and knowledge processing. Knowledge graphs have various methods for building and have various characteristics depending on the purpose of use. This paper discusses about the building model for the knowledge graph and the process of building the knowledge graph and the points to be noted.

In the initial stage of system development, the system engineer plans the budget, and period of time, and define the system requirements. In the case of a system that utilizes a knowledge graph, it is necessary to define the requirements of the knowledge graph and estimate the building cost. It is useful for system engineers, in particular, who do not have much experience in the building of knowledge graphs, to understand how much they need to build a knowledge graph for what they want to achieve and how much it costs to build it. However, it is not clear how much the knowledge graph should be constructed in order to realize what it wants to realize, and what requirements the knowledge graph should meet and the building costs for "What you want to achieve". In this paper, as a first step to improve the efficiency of system development utilizing knowledge graphs, we define the building level of knowledge graph and clarify "what it realizes" and "difficulty" for each level of knowledge graph. At first, we investigate the procedure of building a knowledge graph. After defining the level of knowledge graph, "what it realizes" and "difficulty" of the actual knowledge graph is shown.

The International Association for Ontology and its Application has hosted the Joint Ontology Workshops (JOWO) since 2015. The aim of this workshops is to gather and address a wide spectrum of topics related to ontology research, ranging from cognitive science to knowledge representation, natural language processing, artificial intelligence, logic, philosophy, and linguistics. I report about JOWO from the viewpoint of one participant of the conference in this paper.

Occupational information includes job definitions and various job and work information. By analyzing the vocabulary of job information, we can understand the characteristics of the job, and by analyzing the relationship between the vocabulary, we can also see the relationship between the jobs. This paper constructs thesaurus by analyzing the vocabulary of job information and defining the relationship between vocabulary. The thesaurus of job-related words will be applied to the job search system to enable efficient and useful job search.

We have developed MochiMap, a web map of local foods to help people understand their local foods. In this paper, we designed and prototyped a website for exploratorily browsing of local foods because the exploratory browsing is suitable for non-experts of local foods. Concretely, we use the faceted navigation mechanism to make it easier for first-time users to understand. We also described the properties defined in Wikidata that are needed for each facet. As a future work, we will conduct an evaluation experiment for verifying our proposed system.

In this work, we present a methodology that measures the customer service expertise and performance in video by applying several rules and metrics on knowledge graphs (KGs). In our approach, the KGs represent human behavior performed in the video through conversations and actions which are described from the knowledge base (KB). The definition of rules, baselines, and metrics are written by specific notations (Allen's for representing time interval relations and RCC8 for defining location relations). The methodology is composed of four stages: in 1) "behavior pattern definition" the rules, baselines, and metrics/scores are defined for assessment of behaviors in customer service. The 2) "knowledge graph constructions" process video files, extracts, and represent human activities and interactions with objects in video. During the 3) "knowledge graph retrieval" the user behavior is retrieved from a knowledge graph by means of SPARQL queries. Finally, in 4) "knowledge graph analysis" the rules and scores are applied. In order to measure the expertise following certain rules, the methodology implements inferences, queries, filters, and temporal processing on the knowledge graphs. The purpose of this step is to measure expertise in customer service. Consecutively, the user performance in the video is compared with other baselines (user expert and average). As a case study, the work was applied to elderly care customer service using public videos from the elderly behavior library.

Knowledge Graph embedding (KGEmbedding) is an expected technology to retrieve a new knowledge with completion of knowledge graph, combining of knowledge graphs and so on. However most of KG Embedding models don't consider ontology part of the knowledge graph. Especially the relationship between properties. We propose a model for knowledge graph with well-described ontology in this article. And we show some results of experiments with basic dataset and our own practical dataset.

Necessity of General Design Theory of Classification Retrieval System is discussed, comparing Classification retrieval system (ex. Fterm retrieval system) and Keyword retrieval system (ex. Google, Yahoo), in order to search for the most necessary information rapidly and correctly in the internet society. Currently there is Fterm retrieval system in which the classifications are given beforehand, differently from Keyword retrieval system. The characteristic of this Fterm retrieval system lies in excluding the ambiguity of the keywords. Meanwhile the characteristic of Keyword retrieval system such as google or yahoo lies in thoroughly excluding giving the classifications beforehand. So Classification retrieval system (ex. Fterm retrieval system) and Keyword retrieval system (ex. Google, Yahoo) have utterly different design theory in 'giving the classifications beforehand' and 'keyword.' Especially the possibility for applying graph-theory is discussed in Necessity of General Design Theory of Classification Retrieval System in this paper.