We proposed the VirtualHome2KG framework to generate synthetic KGs of daily life activities in virtual space. We also demonstrated the utility and potential of the VirtualHome2KG through several use cases, including the analysis of daily activities by querying, embedding, and clustering, and fall risk detection among older adults based on expert knowledge.

We constructed an ontology, CIRO, which can infer the risk of COVID-19 infection from the action history for the actual operation of tracking and screening of close contacts at public health centers.

We extracted urban problem causality from various documents and structured the data as a KG. Then we detected vicious cycles and root problems using SPARQL and SWRL. Furthermore, urban-problem experts evaluated the extracted causal relations.

We collected unstructured data from a university campus and integrated it as a knowledge graph based on the proposed ontology.

We developed a reference ontology that enables common situational awareness of spatiotemporal concepts for semantic interoperability in air traffic information management.

We proposed a method for creating word and paragraph vectors corresponding to bilingual textual information in the same multidimensional space, aiming to construct a bilingual map of science.

We extracted causal relations using natural language processing and crowdsourcing to construct urban problem Linked Data.

We proposed a new content-based method of locating research projects in a multi-dimensional space using the word/paragraph embedding techniques.

We complemented temporal and spatial missing data of the Linked Open Data (LOD) of the problem of illegally parked bicycles using bayesian networks and computational fluid dynamics.

We proposed a schema of illegally parked bicycle LOD (IPBLOD) and a methodology of designing LOD schema.

We purposed eco-cycle for solving illegally parked bicycles using linked open data.

We built an ecosystem that generates Open Urban Data in Link Data format while complementing missing attribute values.

We constructed a linked open data of industrial parts (screw LOD) to realize a business support agent that applies the screw LOD to the bill of materials (BOM).

Comparison and discussion of daily living activity datasets in real and virtual spaces.

We developed refinement methods for the actual use of the knowledge graphs for inference and machine learning and released refined scene knowledge graphs as open data.

We proposed a criminal detection method from mystery novels using the principal components regression analysis of word vectors considering cooccurrence.

We developed the ontologies of actions and objects in the home environment, so-called Primitive Action ontology, and Home Object ontology.

This study proposed a framework for constructing and augmenting knowledge graphs (KGs) based on simulation results of daily living activities. Furthermore, we present several use cases using SPARQL queries and a KG embedding method.

We proposed a method to construct and augment knowledge graphs (KGs) based on the simulation results of daily living activities.

We carried out flight experiments using Aeronautical Mobile Airport Communications System (AeroMACS) and System Wide Information Management (SWIM) prototype system.

The development of a practical validation system of ground taxiing experiment for A/G SWIM integration.

We focused on a university campus as an example of a small organization and propose an ontology that enables the cross-sectional analysis of various data.

We construct domain ontologies based on flight, aeronautical, and weather information exchange models. Furthermore, we confirmed the applicability of flexible searching in heterogeneous ATM data using SPARQL and ontology reasoning.

We organized a challenge calling for techniques that reason and/or estimate which characters are criminals while providing a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story.

We proposed a method for extending the geospatial representation of the existing aviation ontology.

We compared a graph-based method using knowledge graph embedding and an image-based method using convolutional neural networks (CNN) to predict urban problems such as littering.

We developed a content-based map, which converts text information, such as US-Japan funding project descriptions and paper abstracts, into multi-dimensional vectors and calculates content similarities.

We proposed a method to detect root problems that lead to the vicious cycles of urban problems using SPARQL and SWRL.

We proposed a new content-based method of locating research projects in a multi-dimensional space using the word/paragraph embedding techniques.

We constructed Linked Open Data (LOD) that include causal relations of urban problems and the related cost information in the budget.

We detected locations with high possibility of bicycle accidents using user participatory sensing and published as Open Hazard Data (OHD).

We proposed a new content-based method of locating research projects in a multi-dimensional space using the word/paragraph embedding techniques.

We proposed a method for semi-automatic construction of Linked Data with the causality of urban problems, based on web pages and open government data.

We proposed and evaluated a method for estimating spatially missing data using computational fluid dynamics (CFD) for expanding urban LOD.

We represented the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other.

We proposed and evaluated a method for estimating spatially missing data using computational fluid dynamics (CFD) for expanding urban LOD.

We represented the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other.

We proposed a method for sustainably building urban LOD to solve the illegally parked bicycle problem and applied them to Tokyo and other urban areas.

We proposed a schema design of illegally parked bicycles LOD and a methodology of designing LOD schema.

We built an ecosystem that generates Open Urban Data in Link Data format while complementing missing attribute values.

We built industrial parts Linked Open Data (LOD), which we called "N-ken LOD" based on a screw product code system (N-ken Code). Then we linked it to external datasets like DBpedia and built product supplier relations to support the EDI.

We discussed perspective in utilizing LOD for variety of EDI requirements.

This article proposes a content-based method of locating research articles/projects in a multi-dimensional space using word/paragraph embedding.