Ikki Ohmukai

Web content summarization using social bookmarks: a new approach for social summarization

An increasing number of Web applications are allowing users to play more active roles for enriching the source content. The enriched data can be used for various applications such as text summarization, opinion mining and ontology creation. In this paper, we propose a novel Web content summarization method that creates a text summary by exploiting user feedback (comments and tags) in a social bookmarking service. We had manually analyzed user feedback in several representative social services including del.icio.us, Digg, YouTube, and Amazon.com. We found that (1) user comments in each social service have its own characteristics with respect to summarization, and (2) a tag frequency rank does not necessarily represent its usefulness for summarization. Based on these observations, we conjecture that user feedback in social bookmarking services is more suitable for summarization than other type of social services. We implemented prototype system called SSNote that analyzes tags and user comments in del.icio.us, and extracts summaries. Performance evaluations of the system were conducted by comparing its output summary with manual summaries generated by human evaluators. Experimental results show that our approach highlights the potential benefits of user feedback in social bookmarking services.

Study Support and Integration of Cultural Information Resources with Linked Data

A museum collection search system called Linked Open Data for Academia (LODAC) Museum has been developed that uses Linked Data. The LODAC Museum identifies and associates artists, artworks, and museum information from some different museums to provide integrated data that are published as Linked Data with the SPARQL endpoint. This projects purpose is to provide an information distribution system that can share and publish a wide range of data as Linked Data, especially in the artistic and cultural fields in Japan. Different types of data are currently being integrated, and new approaches and support for studying these fields are being investigated.

Towards a Data Hub for Biodiversity with LOD

Because of a huge variety of biological studies focused on different targets, i.e., from molecules to ecosystem, data produced and used in each field is also managed independently so that it is difficult to know the relationship among them. We aim to build a data hub with LOD to connect data in different biological fields to enhance search and use of data across the fields. We build a prototype data hub on taxonomic information on species, which is a key to retrieve data and link to databases in different fields. We also demonstrate how the data hub can be used with an application to assist search on other database.

Social scheduler: a proposal of collaborative personal task management

We propose a collaborative approach for personal task management which is modeled as an integration of alliance and human-in-the-loop model. Alliance model is based on information sharing and collaboration of several persons. They disclose their task condition and maintain to be updatable by their friends. To avoid privacy issues we propose emergent group discovery algorithm to control the level of disclosure. Human-in-the-loop model consists of three subsystems to support decision-making activities. Visualizer indicates the attributes associated with each task such as the deadline, the subjective priority, and the workload, which are determined by the user. Optimizer generates executable schedules from these tasks by active scheduler and multiobjective genetic algorithm. Recommender evaluates these alternatives by analytic hierarchy process. We implement client/server system called social scheduler on cell-phones environment. We remark the advantages of our approach with an experiment.

From one star to three stars: Upgrading legacy open data using crowdsourcing

Despite recent open data initiatives in many countries, a significant percentage of the data provided is in non-machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. This paper describes the first unified framework for converting legacy open data in image format into a machine-readable and reusable format by using crowdsourcing. Crowd workers are asked not only to extract data from an image of a chart but also to reproduce the chart objects in spreadsheets. The properties of the reconstructed chart objects give their data structures including series names and values, which are useful for automatic processing of data by computer. Since results produced by crowdsourcing inherently contain errors, a quality control mechanism was developed that improves the accuracy of extracted tables by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Experimental results demonstrated that the proposed framework and mechanism are effective.

A proposal of the person-centered approach for personal task management

This paper proposes a human-centered approach for personal task management in which people can decide management of their tasks according to their environments, including their subjective and multivalent judgement and human relationships. In our approach task management is modeled as a decision-making process on their own resources. The human decision-making process consists of three types of activity, i.e., the intelligence activity, design activity, and choice activity. The proposed system assists each activity by three sub-systems, i.e., visualizer, optimizer and recommender respectively. At first, visualizer indicates the attributes associated with each task such as deadline, subjective priority, and workload, which are determined by the user. The optimizer generates executable schedules from these tasks using an active scheduler and multi-objective genetic algorithm. Finally, the recommender evaluates these alternatives using an analytic hierarchy process. The system is also able to analyze the human relationships of the user group using the PageRank algorithm, and this result is utilized to improve the performance of the task scheduler. We implement a client/server system which uses mobile phones and verify the function of the proposed system along the lines of two scenarios.

QueReSeek: Community-Based Web Navigation by Reverse Lookup of Search History

In this paper, we propose a system called QueReSeek that realizes Web navigation by using search queries in a community. Web navigation is realized as follows: when a user browsing some Web content, if the Web content is included in the list of results of past search by people in the community, query strings used in the search are shown to the user. To realize this navigation, the system collects queries to search engines and their results, and builds the search query-URL index. It shows relevant queries from the URL of Web content which is browsed by users based on this index. By looking up this database reversely, it can show related query strings to Web contents. Since the search queries in the community are keywords related to information and knowledge of interest within the community, this navigation reflects implicit knowledge in the community. It is useful especially for community members who are not proficient in search. Such users can learn search expertise by following search strings provided by the system. We implemented this proposed method in two ways. We could display relevant queries for approximately 20% of the browsed Web content in this experiment.

Constructing a Site for Publishing Open Data of the Ministry of Economy, Trade, and Industry - A Practice for 5-Star Open Data -

We describe a procedure for constructing a website for publishing open data by focusing on the case of Open DATA METI, a website of the Ministry of Economy, Trade, and Industry of Japan. We developed two sites for publishing open data: a data catalog site and one for searching linked open data (LOD). The former allows users to find relevant data they want to use, and the latter allows them to utilize the found data by connecting them. To implement the data catalog site, we constructed a site tailored to the needs of the organization. Then we extracted a large amount of metadata from the individual open data and put it on the site. These activities would have taken a lot of time if we had used the existing methods, so we devised our own solutions for them. To implement the LOD searching site, we converted the data into LOD in the Resource Description Framework (RDF). We focused on converting statistical data into tables, which are widely used. Regarding the conversion, there were several kinds of missing information that we needed to associate with the data in the tables. We created a template for incorporating the necessary information for LOD in the original table. The conversion into LOD was automatically done using the template.

Crowdsourcing chart digitizer: task design and quality control for making legacy open data machine-readable

Despite recent open data initiatives in many countries, a significant percentage of the data provided is in non-machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. Various types of software for digitizing data chart images have been developed. However, such software is designed for manual use and thus requires human intervention, making it unsuitable for automatically extracting data from a large number of chart images. This paper describes the first unified framework for converting legacy open data in chart images into a machine-readable and reusable format by using crowdsourcing. Crowd workers are asked not only to extract data from an image of a chart but also to reproduce the chart objects in a spreadsheet. The properties of the reproduced chart objects give their data structures, including series names and values, which are useful for automatic processing of data by computer. Since results produced by crowdsourcing inherently contain errors, a quality control mechanism was developed that improves accuracy by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Experimental results demonstrated that the proposed framework and mechanism are effective. The proposed framework is not intended to compete with chart digitizing software, and workers can use it if they feel it is useful for extracting data from charts. Experiments in which workers were encouraged to use such software showed that even if workers used it, the extracted data still contained errors. This indicates that quality control is necessary even if workers use software to extract data from chart images.

Specification Patent Management for Web Application Platform Ecosystem

Diversified usage of web applications has encouraged disintegration of web platform into management of identification and applications. Users make use of various kinds of data linked to their identity with multiple applications on certain social web platforms such as Facebook or MySpace. There has emerged competition among web application platforms. Platformers can design relationship with developers by controlling patent of their own specification and adopt open technologies developed external organizations. Platformers choose a way to open according to feature of the specification and their position. Patent management of specification come to be a key success factor to build competitive web application platforms. Each way to attract external developers such as standardization, open source has not discussed and analyzed all together.