研究者詳細 - 駒水　孝裕

論文 - 駒水　孝裕

分割表示 83 件中 61 - 80 件目／全件表示 >>

Community Detection and Correlated Attribute Cluster Analysis on Multi-Attributed Graphs 査読有り

Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 2nd International workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP 2018) co-located with the 21st International Conference on Extending Database Technology (EDBT 2018) 2018年3月

Network-Word Embedding for Dynamic Text Attributed Networks 査読有り

Hiroyoshi Ito, Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 6th International Workshop on Semantic Computing for Social Networks and Organization Sciences (SCSN 2018) co-located with the 12th IEEE International Conference on Semantic Computing (ICSC 2018) 2018年1月

Analytical toolbox for smart city applications: Garbage collection log use case 査読有り

Takahiro Komamizu, Jin Nakazawa, Toshiyuki Amagasa, Hiroyuki Kitagawa, Hideyuki Tokuda

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 2018- 巻頁： 4105 - 4110 2018年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

Analyzing and feeding back the results on real-world services are important missions in the Big Data era to realize smart city. However, analyzing real-world data is still challenging because of dirtiness of data and large variety of analytic requirements. To cope with the challenges, this paper proposes and develops an analytical toolbox for smart city applications. The analytical toolbox consists of three phases: preparation, analysis, and visualization. The preparation phase deals with the dirtiness of the data by including fundamental data cleansing techniques and data integration techniques. The analysis phase is responsible for ETL (extract, transform and load) process and analytical query processing from the next phase. The visualization phase deals with analytical requirements from users and visualization of analytical results. This paper showcases a real-world use case of the proposed analytical toolbox. The use case is now open in public with help of Fujisawa city, Japan, and this fact indicates that the proposed analytical toolbox is feasible for real-world data analysis and feeding back to citizens.

DOI： 10.1109/BigData.2017.8258429

Scopus

Implicit order join: Joining log data with property data by discovering implicit order-oriented keys with human assistance 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 2018- 巻頁： 4400 - 4406 2018年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

Data integration is still laboursome task when integrating data are not consistently managed. Such inconsistency can happen easily in real-world situations, such as properties of objects are managed by a central organization and trajectories (or logs) of the objects are recorded by other peripheral organizations. This paper deals with a case of missing ordering information. Integrating property data and log data without ordering information causes duplicated results. In order to solve this problem, this paper proposes a join algorithm, called implicit order join, which discovers implicit ordering information from both property data and log data with help of partial true integrated results from human assistance. With the discovered ordering information, the implicit order join enables to integrate the property data and log data. In order to discover the implicit ordering information, ordering correlation between attribute sequences of property data and log data should be found from comprehensive examination of possible attribute sequence pairs. The potential number of sequence pairs is as high as factorial order of the number of attributes. Therefore, this paper develops a heuristic approach to prune unnecessary examinations based on ordering dependency between attribute sequences. Experimental evaluation in this paper indicates that implicit order join can reduce 77% labouring tasks for integration and the pruning method reduces the number of attribute sequences in orders of magnitude.

DOI： 10.1109/BigData.2017.8258474

Scopus

Japanese Legal Term Correction Using Random Forests 査読有り

Yamakoshi, T; Komamizu, T; Ogawa, Y; Toyama, K

LEGAL KNOWLEDGE AND INFORMATION SYSTEMS (JURIX 2018) 313 巻頁： 161 - 170 2018年

記述言語：英語出版者・発行元：Frontiers in Artificial Intelligence and Applications

We propose a method that assists legislation officers in finding inappropriate Japanese legal terms in Japanese statutory sentences and suggests corrections. In particular, we focus on sets of similar legal terms whose usages are defined in legislation drafting rules. Our method predicts suitable legal terms in statutory sentences using Random Forest classifiers, each of which is optimized for each set of similar legal terms. Our experiment shows that our method outperformed existing modern word prediction methods using neural language models.

DOI： 10.3233/978-1-61499-935-5-161

Web of Science

Scopus

Learning Interpretable Entity Representation in Linked Data

Komamizu, T

DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2018, PT I 11029 巻頁： 153 - 168 2018年

掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Linked Data has become a valuable source of factual records. However, because of its simple representations of records (i.e., a set of triples), learning representations of entities is required for various applications such as information retrieval and data mining. Entity representations can be roughly classified into two categories; (1) interpretable representations, and (2) latent representations. Interpretability of learned representations is important for understanding relationship between two entities, like why they are similar. Therefore, this paper focuses on the former category. Existing methods are based on heuristics which determine relevant fields (i.e., predicates and related entities) to constitute entity representations. Since the heuristics require laboursome human decisions, this paper aims at removing the labours by applying a graph proximity measurement. To this end, this paper proposes RWRDoc, an RWR (random walk with restart)-based representation learning method which learns representations of entities by weighted combinations of minimal representations of whole reachable entities w.r.t. RWR. Comprehensive experiments on diverse applications (such as ad-hoc entity search, recommender system using Linked Data, and entity summarization) indicate that RWRDoc learns proper interpretable entity representations.

DOI： 10.1007/978-3-319-98809-2_10

Web of Science

Scopus

その他リンク： https://dblp.uni-trier.de/db/conf/dexa/dexa2018-1.html#Komamizu18

CROISSANT: Centralized Relational Interface for Web-scale SPARQL Endpoints 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 19th International Conference on Information Integration and Web-based Applications & Services (iiWAS2017) 2017年12月

SOLA: Stream OLAP-based Analytical Framework for Roadway Maintenance 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Salman Ahmed Shaikh, Hiroaki Shiokawa, Hiroyuki Kitagawa

Proc. the 9th International Conference on Management of Digital EcoSystems (MEDES 2017) 2017年11月

GitHubとStack Overflowの開発者の活動記録を併用したリポジトリ推薦査読有り

永野真知, 早瀬康裕, 駒水孝裕, 北川博之

ソフトウェアエンジニアリングシンポジウム 2017論文集 2017年8月

Exploring identical users on GitHub and stack overflow 査読有り

Takahiro Komamizu, Yasuhiro Hayase, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE 頁： 584 - 589 2017年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Knowledge Systems Institute Graduate School

Analyzing behaviours of developers in different platforms (in particular, GitHub and Stack Overflow in this paper) can reveal interesting facts related to development activities. There are only few datasets for analysing crossplatform user behaviours, especially across GitHub and Stack Overflow. Users on GitHub and Stack Overflow are identifiable by equivalences of email addresses. In order to increase the number of identifiable users on these datasets, this paper retrieves potentially identifiable users between GitHub and Stack Overflow not relying only on email addresses. This paper employs a classification-based link prediction, which design the user identification problem as a link prediction problem on the bipartite graph consisting of users of GitHub and those of Stack Overflow. With the identification method, this paper generates a probabilistic dataset containing pairs of users with probabilities (or confidences). This paper, as well, publishes the identification tool in order to enable further data generation on appearing datasets of GitHub, Stack Overflow and others. The generated dataset and tool are highly helpful to accelerate researches on mining software repositories.

DOI： 10.18293/SEKE2017-109

Scopus

FORK: Feedback-Aware ObjectRank-Based Keyword Search over Linked Data 査読有り

Takahiro Komamizu, Sayami Okumura, Toshiyuki Amagasa, Hiroyuki Kitagawa

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10648 巻頁： 58 - 70 2017年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Springer Verlag

Ranking quality for keyword search over Linked Data (LD) is crucial when users look for entities from LD, since datasets in LD have complicated structures as well as much contents. This paper proposes a keyword search method, FORK, which ranks entities in LD by ObjectRank, a well-known link-structure analysis algorithm that can deal with different types of nodes and edges. The first attempt of applying ObjectRank to LD search reveals that ObjectRank with inappropriate settings gives worse ranking results than PageRank which is equivalent to ObjectRank with all the same authority transfer weights. Therefore, deriving appropriate authority transfer weights is the most important issue for encouraging ObjectRank in LD search. FORK involves a relevance feedback algorithm to modify the authority transfer weights according with users’ relevance judgements for ranking results. The experimental evaluation of ranking qualities using an entity search benchmark showcases the effectiveness of FORK, and it proves ObjectRank is more feasible raking method for LD search than PageRank and other comparative baselines including information retrieval techniques and graph analytic methods.

DOI： 10.1007/978-3-319-70145-5_5

Scopus

Towards Real-time Analysis of Smart City Data: A Case Study on City Facility Utilizations 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Salman Ahmed Shaikh, Hiroaki Shiokawa, Hiroyuki Kitagawa

Proc. the 14th IEEE International Conference on Smart City (SmartCity 2016) 頁： 1357-1364 2016年12月

Interleaving Clustering of Classes and Properties for Disambiguating Linked Data 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 18th International Conference on Asia-Pacific Digital Libraries (ICADL 2016) 2016年12月

Visual Spatial-OLAP for Vehicle Recorder Data on Micro-sized Electric Vehicles 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 20th International Database Engineering & Applications Symposium (IDEAS 2016) 頁： 358-363 2016年7月

H-SPOOL: A SPARQL-based ETL Framework for OLAP over Linked Data with Dimension Hierarchy Extraction 招待有り査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

12 巻 ( 3 ) 頁： 359-378 2016年6月

SPOOL: A SPARQL-based ETL Framework for OLAP over Linked Data 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2015) ( 49 ) 頁： 1-10 2015年12月

Facet-value Extraction Scheme from Textual Contents in XML Data 招待有り査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

11 巻 ( 3 ) 頁： 270-290 2015年6月

Extracting Facets from Textual Contents for Faceted Search over XML Data 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 16th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2014) 頁： 420-429 2014年12月

Frequent-Pattern based Facet Extraction from Graph Data 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 17th International Conference on Network-Based Information Systems (NBiS 2014) 頁： 318-323 2014年9月

A Scheme of Automated Object and Facet Extraction for Faceted Search over XML Data 査読有り

Takahiro Komamizu, Toshiyuki Amagasa, Hiroyuki Kitagawa

Proc. the 18th International Database Engineering & Applications Symposium (IDEAS 2014) 頁： 338-341 2014年7月

　前のページ - 次のページ　

<駒水 孝裕>

論文 - 駒水 孝裕

<駒水　孝裕>

論文 - 駒水　孝裕