Shusaku Egami

Building Urban LOD for Solving Illegally Parked Bicycles in Tokyo

The illegal parking of bicycles is an urban problem in Tokyo and other urban areas. The purpose of this study was to sustainably build Linked Open Data (LOD) for the illegally parked bicycles and to support the problem solving by raising social awareness, in cooperation with the Bureau of General Affairs of Tokyo. We first extracted information on the problem factors and designed LOD schema for illegally parked bicycles. Then we collected pieces of data from Social Networking Service (SNS) and websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the missing data in the LOD based on the causal relations from the problem factors. As a result, the number of illegally parked bicycles can be inferred with 70.9 % accuracy. Finally, we published the complemented LOD and a Web application to visualize the distribution of illegally parked bicycles in the city. We hope this raises social attention on this issue.

Construction of Linked Urban Problem Data with Causal Relations Using Crowdsourcing

Municipalities in Japan have various urban problems such as traffic accidents, illegally parked bicycles, and noise pollution. However, using these data to solve urban problems is difficult, as these data are not structurally managed. Hence, we aim to construct the Linked Data infrastructure that will facilitate the solving of urban problems. In this paper, we propose a method for semi-automatic construction of Linked Data with the causality of urban problems, based on Web pages and open government data. Specifically, we extracted causal relations using natural language processing and crowdsourcing. As a result, Linked Data with causal relations of noise pollution, illegally parked bicycles, and traffic accidents was constructed.

Science Graph for characterizing the recent scientific landscape using Paragraph Vectors

Maps of science representing the structure of science can help us understand science and technology (S&T) development. Thus, research in scientometrics has developed techniques for analyzing research activities and for measuring their relationships; however, navigating the recent scientific landscape is still challenging, since conventional inter-citation and co-citation analysis has difficulty in applying to ongoing projects and recently published papers. Therefore, in order to characterize what is being attempted in the current scientific landscape, this paper proposes a content-based method of locating research projects in a multi-dimensional space using word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with paragraph vectors, we introduce cluster vectors based on the information entropies of concepts in an S&T thesaurus. In addition, we propose three approaches to find the semantics of project relationships. The experimental results show that the proposed method successfully formed a clustered graph from 25,607 project descriptions from the 7th Framework Programme of EU from 2006 to 2016. Finally, we evaluated the distances and semantics of the project relationships and identified significant relationships from the graph.

Schema Design of Illegally Parked Bicycles LOD.

Illegally parked bicycles are a social problem in Japan and other countries. Illegally parked bicycles obstruct vehicles, cause road accidents, encourage thefts, and disfigure streets. In order to solve the challenge posed by illegally parked bicycles, we realized that it is necessary to collect and republish the data as reusable format. Therefore, we collected the number of illegally parked bicycles, location information, time, and factors. Then, we integrated and republished these data as Linked Open Data (LOD) on the Web. In this paper, we described a schema design of illegally parked bicycles LOD and a methodology of designing LOD schema. Furthermore, we collected data from SNS and website of municipality, and built the LOD of 21,898 triples.

Funding map using paragraph embedding based on semantic diversity

Maps of science representing the structure of science can help us understand science and technology (S&T) development. Studies have thus developed techniques for analyzing research activities’ relationships; however, ongoing research projects and recently published papers have difficulty in applying inter-citation and co-citation analysis. Therefore, in order to characterize what is currently being attempted in the scientific landscape, this paper proposes a new content-based method of locating research projects in a multi-dimensional space using the recent word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with the original paragraph vectors, we introduce paragraph vectors based on the information entropies of concepts in an S&T thesaurus. The experimental results show that the proposed method successfully formed a clustered map from 25,607 project descriptions of the 7th Framework Programme of EU from 2006 to 2016 and 34,192 project descriptions of the National Science Foundation from 2012 to 2016.

Linked Urban Open Data Including Social Problems' Causality and Their Costs.

There are various urban problems, such as suburban crime, dead shopping street, and littering. However, various factors are socially intertwined; thus, structural management of the related data is required for visualizing and solving such problems. Moreover, in order to implement the action plans, local governments first need to grasp the cost-effectiveness. Therefore, this paper aims to construct Linked Open Data (LOD) that include causal relations of urban problems and the related cost information in the budget. We first designed a data schema that represents the urban problems’ causality and extended the schema to include budget information based on QB4OLAP. Next, we semi-automatically enriched instances according to the schema using natural language processing and crowdsourcing. Finally, as use cases of the resulting LOD, we provided example queries to extract the relationships between several problems and the particular cost information. We found several causes that lead to the vicious circle of urban problems and for the solutions of those problems, we suggest to a local government which actions should be addressed.

User Participatory Construction of Open Hazard Data for Preventing Bicycle Accidents

Recently, bicycle-related accidents, e.g., collision accidents at intersection increase and account for approximately 20% of all traffic accidents in Japan; thus, it is regarded as one of the serious social problems. However, the Traffic Accident Occurrence Map released by the Japanese Metropolitan Police Department is currently based on accident information records, and thus there are a number of near-miss events, which are overlooked in the map but will be useful for preventing the possible accidents. Therefore, we detect locations with high possibility of bicycle accidents using user participatory sensing and offer them drivers and government officials as Open Hazard Data (OHD) to prevent future bicycle accident. This paper uses smartphone sensors to obtain data for acceleration, location, and handle rotation information. Then, by classifying those data with convolutional neural networks, it was confirmed that the locations, where sudden braking occurred can be detected with an accuracy of 80%. In addition, we defined an RDF model for OHD that is currently publicly available. In future, we plan to develop applications using OHD, e.g., notifying alerts when users are approaching locations where near-miss events have occurred.

Linked Data Collection and Analysis Platform for Music Information Retrieval

There has been extensive research on music information retrieval (MIR), such as signal processing, pattern mining, and information retrieval. In such studies, audio features extracted from music are commonly used, but there is no open platform for data collection and analysis of audio features. Therefore, we build the platform for the data collection and analysis for MIR research. On the platform, we represent the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other. By adopting the Linked Data, the music data will become easier to publish and share, and there is an advantage that complex music analysis will be facilitated. In this paper, we first investigate the frequency of the audio features used in previous studies on MIR for designing the Linked Data schema. Then, we build a platform, that automatically extracts the audio features and music metadata from YouTube URIs designated by users, and adds them to our Linked Data DB. Finally, the sample queries for music analysis and the current record of music registrations in the DB are presented.

A Solution to Visualize Open Urban Data for Illegally Parked Bicycles

The illegal parking of bicycles is becoming an urban problem in Japan and other countries. We believe the data publication of such urban problems on the Web as Open Data will contribute to solving the problems. However, Open Data sets available for the illegally parked bicycles are coarse and in various formats, and then it is difficult to develop information services using the data. In this study, we thus build an ecosystem that generates Open Urban Data in Link Data format by socially collecting the data, complementing the missing data, and then visualizing the data to facilitate and raise social awareness of the problem. In our experiment, 747 pieces of information on the illegally parked bicycles in Tokyo were collected, and then we estimated the unknown number of the illegally parked bicycles with 64.3i¾?% accuracy. Then, we published the data as the Open Data, and also a web application, which visualizes the distribution of the illegally parked bicycles on a map.

Building of Industrial Parts LOD for EDI - A Case Study -

A wide variety of mechanical parts are used as products in the area of manufacturing. The code systems of product information are necessary for realizing Electronic Data Interchange (EDI) of business-to-business. However, each code systems are designed and maintained by different industry associations. Thus, we built an industrial parts Linked Open Data (LOD), which we called “N-ken LOD” based on a screw product code system (N-ken Code) maintained by Osaka fasteners cooperative association (Daibyokyo). In this paper, we first describe building of N-ken LOD, then how we linked it to external datasets like DBpedia, and built product supplier relations in order to support the EDI.