Takafumi Nakanishi

Research Topics

Unique Explainable AI (XAI) Methodology – AIME

Our research introduces a groundbreaking method in explainable AI using Approximate Inverse Model Explanations (AIME). This approach utilizes approximate inverse operators, a novel concept, enabling the application of AIME across various data types in AI and machine learning. It significantly enhances the interpretability of complex models, providing a more intuitive understanding of how data features impact predictions. This versatility and innovation mark a major advancement in making AI more transparent and understandable.

Media Transformation based on Human Sensitivity

Media Transformation based on Human Sensitivity” explores innovative techniques for transforming music, video, and text data based on human emotions and perceptions. This research focuses on extracting features from music and conjecturing the emotional responses it may evoke. It then uses these insights to generate corresponding visual and textual data, creating a multi-sensory experience deeply rooted in human sensitivity. This approach offers a unique blend of technology and art, opening new avenues in understanding and interacting with media.

Publication

Journal

・T. Nakanishi, A. Minematsu, R. Okada, O. Hasegawa, V. Sornlertlamvanich, A New Global Sign Language Recognition System Utilizing the Editable Mediator: Integration with Local Hand Shape Recognition, Information Modelling and Knowledge Bases XXXV, IOS Press, pp.227-238, 2024.
https://doi.org/10.3233/faia231158
・T. Nakanishi, “Approximate Inverse Model Explanations (AIME): Unveiling Local and Global Insights in Machine Learning Models,” in IEEE Access, vol. 11, pp. 101020-101044, 2023.
https://doi.org/10.1109/ACCESS.2023.3314336
・T. Nakanishi, A. Minematsu, R. Okada, O. Hasegawa, V. Sornlertlamvanich, Sign Language Recognition by Similarity Measure with Emotional Expression Specific to Signers, Information Modelling and Knowledge Bases XXXIV, IOS Press, pp.21-37, 2023. https://doi.org/10.3233/FAIA220490
・T. Nitta, S. Hagimoto, A. Yanase, R. Okada, V. Sornlertlamvanich, T. Nakanishi, Realization for Finger Character Recognition Method by Similarity Measure of Finger Features, International Journal of Smart Computing and Artificial Intelligence, Vol. 6 No. 1, 2022. https://doi.org/10.52731/ijscai.v6.i1.684
・T. Nakanishi, R. Okada, R. Nakahodo, Semantic Waveform Measurement Method of Kansei Transition for Time-series Media Contents, International Journal of Smart Computing and Artificial Intelligence,International Institute of Applied Informatics, Vol. 5, No. 1, pp. 51 – 66, 2021. https://doi.org/10.52731/ijscai.v5.i1.631
・S. Kato, T. Nakanishi, B. Ahsan, H. Shimauchi, Time-series topic analysis using singular spectrum transformation for detecting political business cycles, Journal of Cloud Computing: Advances, Systems and Applications, 10, 21, 2021. https://doi.org/10.1186/s13677-020-00197-4
・K. Matsumoto,T. Nakanishi, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, AI-Josyu: Thinking Support System in Class by Real-time Speech Recognition and Keyword Extraction, EMITTER International Journal of Engineering Technology Vol. 7, No. 1, 2019.
・R. Okada, T. Nakanishi, Y. Tanaka, Y. Ogasawara, K. Ohashi, A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions, New Generation Computing,  vol. 37, pp. 113–137, 2019.
・R. Okada, T. Nakanishi, Y. Tanaka,Y. Ogasawara, K. Ohashi, A Visualization Method of Relationships among Topics in a Series of Meetings, Information Engineering Express International Institute of Applied Informatics, Vol.3, No.4, pp.115 – 124, 2017.
・M. Tashiro, Y. Iijima, T. Komatsu, F. Toriumi, T. Nakanishi, K.Eguchi, H. Asako, Construction and verification of a power distribution model that uses graph theory and considers the ease of connection, Journal of transformation of human behavior under the influence of infosocionomics society, Vol.2, pp.15-24, 2017.
・T. Nakanishi, A Feature Selection for Comparison among Each Concept and its Visualization, ACIS International Journal of Computer & Information Science, Vol.17, No.1, 2016.
・F. Toriumi, T. Nakanishi, M. Tashiro, K. Eguchi, Encounters between predators and their targets in private chat, Journal of transformation of human behavior under the influence of infosocionomics society, Vol.1, pp.23-28, 2016.
・T. Nakanishi, R. Okada, F. Triumi, M. Tashiro, K. Eguchi, Simulation for Communication Risks on SNS, Journal of transformation of human behavior under the influence of infosocionomics society, vol.1, pp.15-21, 2016.
・T. Nakanishi, A Data-driven Axes Creation Model for Correlation Measurement on Big Data Analytics,” Information Modelling and Knowledge Bases XXVI, IOS Press, pp.308-323, 2014.
・T. Nakanishi, K. Uchimoto,Y. Kidawara, A Recognition Method of Important Image Regions in Retrieved Image Data Set Corresponding to User’s Given Query, Special Issue in Advances in Intelligent Systems, WIT Transactions on Information and communication Technologies, pp.121-133, 2014.(http://amzn.asia/4tuKOtE)
・T. Nakanishi, K. Uchimoto,Y. Kidawara, A Framework Design of Dataset Relation Discoveryfor Solving Inconsistencies by Connection of Heterogeneity in the Big Data Era, International Journal of Computer & Information Science (IJCIS), Vol.14(1), pp.1-10, 2013.
・K.-S. Kim, T. Nakanishi, H. Homma, K. Zettsu, Y. Kidawara, Y. Kiyoki, A Phenomena-of-Interest Approach for the Interconnection of Sensor Data and Spatiotemporal Web Contents, Information Modelling and Knowledge Bases, (IOS Press), vol. XXII, pp.288-300, 2011.
・T. Nakanishi, H. Homma, K.-S. Kim, K. Zettsu, Y. Kidawara, Y. Kiyoki, A Three-layered Architecture for Event-centric Interconnections among Heterogeneous Data Repositories and its Application to Space Weather, Information Modelling and Knowledge Bases, (IOS Press), vol. XXII, pp.21-36, 2011.
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, Interconnection of heterogeneous knowledge bases and its application on Knowledge Grid, Concurrency and Computation: Practice and Experience, Special Issue: Special Issue: Semantics, Knowledge and Grids, vol.23, issue 9, pp.940-955, 2011.
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, A Context Dependent Dynamic Interconnection Method of Heterogeneous Knowledge Bases by Interrelation Management Function, A Context Dependent Dynamic Interconnection Method of Heterogeneous Knowledge Bases by Interrelation Management Function, Information Modelling and Knowledge Bases(IOS Press), vol. XXI, pp.208-225, 2010
・K. Zettsu, T. Nakanishi, M.Iwazume, Y.Kidawara, Y. Kiyoki, Knowledge Cluster Systems for Knowledge Sharing, Analysis and Delivery among Remote Sites, Information Modelling and Knowledge Bases (IOS Press), Vol. XIX, pp.282-289, 2008.
・H. Homma, T. Nakanishi, T. Kitagawa, A Method of Automatic Metadata Extraction Corresponding to the Impression by Sound of the Words, Information Modelling and Knowledge Bases (IOS Press), Vol. XVIII, pp.206-222, 2007.
・T. Kitagawa, T. Nakanishi, Y. Kiyoki, An Implementation Method of Automatic Metadata Extraction Method for Music Data and Its Application to Semantic Associative Search, Systems and Computers in Japan, Vol.35, No.6, pp59–78, 2004.

International Conference (Peer-reviewed)

・T.Nakanishi, Detection of Latent Gender Biases in Data and Models Using the Approximate Generalized Inverse Method, In Proceedings of 2024 IEEE 18th International Conference on Semantic Computing (ICSC), pp.191-196, 2024.
・Y.Ohkawa, T.Nakanishi, Anomaly Detection Through Graph Autoencoder Based Learning of Screenshot Image Logs, In Proceedings of 2024 IEEE 18th International Conference on Semantic Computing (ICSC), pp.65-68, 2024.
・T. Ohno, Y.Hoshino, T. Nakanishi, Temporal Analysis of Editorial Trends in Major Newspapers Following the Prime Minister’s Speech, In Proceedings of 2024 IEEE 18th International Conference on Semantic Computing (ICSC), pp.148-151, 2024.
・T.Nakanishi, An Inquirer-Responder Architecture Using LLMs: Emulating Virtual Hearing Q&A in Education, In Proceedings of 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), International Workshop on ChatGPT for Business Applications, pp.526–533, 2023.
https://doi.org/10.1109/WI-IAT59888.2023.00088
・F. Cheng and T. Nakanishi, “A Keyword Transition Extraction Method for Time-series Text Data and Its Application to Discovering the Transition of Key Technology Elements in Japan,” 2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), Koriyama, Japan, pp. 100-105, 2023. https://doi.org/10.1109/IIAI-AAI59060.2023.00029
・X. Luo, S. Kato, A. Obata, B. Ahsan, R. Okada, and T. Nakanishi, A Joint Scene Text Recognition and Visual Appearance Model for Protest Issue Classification, In Proceedings of the 4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR ’23). pp.32–36, 2023. https://doi.org/10.1145/3592571.3592971
・R. Ohnishi, Y. Murakami, T. Nakanishi, R. Okada, T. Ozawa, K. Fukushima, T. Miyamae, Y. Ogasawara, K. Akiyama, K. Ohashi, Time-Series Multidimensional Dialogue Feature Visualization Method for Group Work. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2022-Winter. Studies in Computational Intelligence, vol 1086. Springer, Cham., 2023.
https://doi.org/10.1007/978-3-031-26135-0_6
・K. Komiya, R. Okada, A. Minematsu, T. Nakanishi, Automatic Piano Accompaniment Generation Method by Drum Rhythm Features with Selectable Difficulty Level. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2022-Winter. Studies in Computational Intelligence, vol 1086. Springer, Cham., 2023.
https://doi.org/10.1007/978-3-031-26135-0_2
・T. Nitta, S. Hagimoto, K. Miyamura, R. Okada, T. Nakanishi, Time-Series Flexible Resampling for Continuous and Real-Time Finger Character Recognition, 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’22), pp. 357-363, February 2023.
https://doi.org/10.1109/WI-IAT55865.2022.00059.
・Y. Noji, R. Okada, T. Nakanishi, Represent Score as the Measurement of User Influence on Twitter. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2022. Studies in Computational Intelligence, vol 1074. Springer, Cham., 2023.
https://doi.org/10.1007/978-3-031-19604-1_3
・S. Ito, T. Nakanishi, M.Hashimoto, Estimation of Pharmacological Activity by Combining Molecular Fingerprints and Skeletal Formula Images, in Proceedings of 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), pp.45-50, 2022. [acceptance rate:37.3%]
・S. Hagimoto, T. Nitta, R. Okada, T. Nakanishi, A Dynamic Finger Character Recognition Method using Landmark Behavior Rule Base, in Proceedings of 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), pp.189-195, 2022. [acceptance rate:37.3%]
・S. Liu, T. Nakanishi, Obstacles Detection and Motion Estimation by using Multiple Lidar Sensors Data, in Proceedings of 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), pp.196-201, 2022. [acceptance rate:37.3%]
・K. Sano, K. Ojima, R. Okada, T. Nakanishi, A Method for Predicting Sudden/Cyclic Stress using Vital Data and the Realization of a Tracking Mental Management Robot, in Proceedings of 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), pp.202-207, 2022. [acceptance rate:37.3%]
・M. Iwamoto, K. Ojima, R. Okada, T. Nakanishi, Emotion Estimation Method by Convolutional Neural Network for Heartbeat Vital Data, in Proceedings of 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), pp.245-250, 2022. [acceptance rate:37.3%] [Best Student Paper Award]
・Y. Ohkawa, T. Nakanishi, Detection Method of User Behavior Transition on Computer. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science, vol 13726. Springer, Cham., 2022. https://doi.org/10.1007/978-3-031-22137-8_6
・T. Inari, T. Nakanishi, Concentration Patterns Estimation Method in Deskwork by Using Time-series k-means, In Proceedings of 2022 International Electronics Symposium (IES), pp. 576-580, 2022. https://doi.org/10.1109/IES55876.2022.9888313
・S. Tamaru, H. Taki, R. Usuki, T. Nakanishi, Recipe Recommendation Method by Similarity Measure with Food Image Recognition, In Proceedings of 2022 the 6th International Conference on Information System and Data Mining (ICISDM 2022). Association for Computing Machinery, New York, NY, USA, pp.81–88, 2022. https://doi.org/10.1145/3546157.3546170
・A. Ikegami, T. Nakanishi, Interpretable Predictive Results in Classification of Waka Poets, In Proceedings of the 12th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2022), pp.436-442, 2022. https://doi.org/10.1109/IIAIAAI55812.2022.00092
[acceptance rate 33.5%]
・H. Nakata, T. Nakanishi, Music Recommendation Method for Time-Series Emotions from Lyrics using Valence-Arousal-Dominance Model, In Proceedings of the 12th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2022), pp.443-448, 2022.
https://doi.org/10.1109/IIAIAAI55812.2022.00093
[acceptance rate 33.5%] [Honorable Mention Award]
・R. Hirano, R. Okada, T. Nakanishi, Extraction Method for Important Words as a Viewer’s Reaction Arousal Factor from YouTube – Transcription, In Proceedings of the 12th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2022), pp.651-652, 2022. https://doi.org/10.1109/IIAIAAI55812.2022.00129
(Poster Abstract)
・Y. Ishii, T. Nakanishi, R. Okada and A. Minematsu, “Tourist Spots Recommendation Method Corresponding to Place Names Appearing in Novel Contents,” 2022 7th International Conference on Business and Industrial Research (ICBIR), pp. 192-197,2022. https://doi.org/10.1109/ICBIR54589.2022.9786468
・K. Sano, K. Ojima, T. Nakamura, R. Okada, T. Nakanishi, A Method for Estimating Emotions Using HRV for Vital Data and Its Application to Self-mentalcare Management System, in Tokuro Matsuo (editor) Proceedings of 11th International Congress on Advanced Applied Informatics, EPiC Series in Computing, Vol 81, pp. 89–100, 2022. https://doi.org/10.29007/fp2d
・Y. Ishii, A. Ikegami, T. Nakanishi, Realization of Discovery for Burst Topic Transition Using the Topic Change Point Detection Method for Time-Series Text Data, in Tokuro Matsuo (editor) Proceedings of 11th International Congress on Advanced Applied Informatics, EPiC Series in Computing, Vol 81, pp, 362–372, 2022. https://doi.org/10.29007/k8pb
・A. Ikegami, R. Okada, T.Nakanishi, The Discovery of Historical Transition in Aesthetic Notions Through Changes in Co-occurrence Words Mainly Used in Waka Poetry in Three Major Poetry Anthologies. In: Lee R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SNPD 2021. Studies in Computational Intelligence, vol 1012. Springer, Cham, 2022. https://doi.org/10.1007/978-3-030-92317-4_12
・H. Nakata, T. Nakanishi, Music Impression Extraction Method By chord Impressions and Its Application to Music Media Retrieval, in Proceedings of 22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2021-Fall), pp.68-73, 2021.
・K. Miyamura, R. Okada, T. Nakanishi, Mashuprium and Timeline Visualization: Implementation of 3D Space and Timeline Visualization for Automatic Mashup Creation, In Proceedings of the 10th International Congress on Applied Information Technology (IIAI AAI 2021), pp.804-809, 2021.[acceptance rate 33.6%]
・A. Yanase, T. Nakanishi, Musical Impression Extraction Method by Discovering Relationships between Acoustic Features and Impression Terms, In Proceedings of the 10th International Congress on Applied Information Technology (IIAI AAI 2021), pp.810-807, 2021.[acceptance rate 33.6%] [Honorable Mention Award]
・T. Nakanishi, Semantic Waveform Model for Similarity Measure by Time-series Variation in Meaning, In Proceedings of the 10th International Congress on Applied Information Technology (IIAI AAI 2021), pp.382-387, 2021.[acceptance rate 33.6%]
・T. Hakii, K. Shimada, T. Nakanishi, R. Okada, K. Matsuda, R. Onishi, K. Takahashi, Weather Map Prediction Using RGB Metaphorical Feature Extraction for Atmospheric Pressure Patterns, In proceedings of 20th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2021 Summer),pp.21-27, 2021.
・K. Miyamura, R. Okada, T. Nakanishi, Automatic Music Mashup Creation Method by Similarity of Features, In proceedings of 20th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2021 Summer),pp.35-40, 2021.
・S. Hagimoto, T. Nitta, A. Yanase, T. Nakanishi, R. Okada, V. Sornlertlamvanich, Knowledge Base Creation by Reliability of Coordinates Detected from Videos for Finger Character Recognition, In proc. of 19th IADIS International Conference e-Society 2021, FSP 5.1-F144, pp.169-176, 2021.
・T. Nitta, S. Hagimoto, A. Yanase, T. Nakanishi, R. Okada, V. Sornlertlamvanich, Finger Character Recognition in Sign Language Using Finger Feature Knowledge Base for Similarity Measure, In Proceedings of the 3rd IEEE/IIAI International Congress on Applied Information Technology (IEEE/IIAI AIT 2020), 2020. [Best Paper Award]
・A. Iskandar, T. Nakanishi, A. Basuki, R. Okada, T. Kitagawa, Gaze-music Media Transformation by Similarity of Impression Words, 2020 International Electronics Symposium (IES), Surabaya, Indonesia, , pp. 655-661, 2020.
doi: 10.1109/IES50839.2020.9231645.
・T. Nakanishi, R. Okada, R. Nakahodo, Kansei Transition Analysis by Time-series Change of Media Content, In Proceedings of 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI 2020), pp.422-427, 2020.[acceptance rate: 30%]
・R. Okada, T. Nakanishi, A. Kawagoe, H. Saito, H. Saito, M. Shinohara, A Redefinition Method of Extracting Features for Media Content Utilization and Its Application to Kimono Obi Design, in Proceedings of 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI 2020), pp.116-121, 2020.[acceptance rate: 30%]
・S. Kato, T. Nakanishi, H. Shimauchi, B. Ahsan, Topic Variation Detection Method for Detecting Political Business Cycles. In Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT ’19). ACM, New York, NY, USA, pp.85-93, 2019. [acceptance rate: 27.7%]
・T. Nakanishi, K. Matsumoto, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, A Class Content Summary Method based on Media-driven Real-time Content Management Framework, in proceedings of 2019 8th International Congress on Advanced Applied Informatics, pp.795-798, 2019.
・T. Nakanishi, K. Matsumoto, T. Sakawa, K. Onodera, S. Orimo, H. Kobayashi, Media-driven Real-time Content Management Framework and its Application to In-Class Thinking Support System, in proceedings of 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2018.
・K. Matsumoto, T. Nakanishi, T. Kitagawa, Semantic-Dependent Access Log Analysis for Predicting the Demographic Data, In Proceedings of the 28th International Conference on Information Modelling and Knowledge Bases – EJC 2018, Riga, Latvia, pp. 110-129, 2018.
・R. Okada, T. Nakanishi, Y. Tanaka,Y. Ogasawara, K. Ohashi, A Topic Structuration Method on Time Series for a Meeting from Text Data, In: Lee R. (eds) Studies in Computational Intelligence, Springer, Cham, Vol. 721, pp.45-59, 2018.
・T. Nakanishi, K. Tamaru, An Impression Estimation and Visualization Method for TV Commercials, In proceedings of 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 6 Pages, 2017.
・T. Nakanishi, R. Okada, Y. Tanaka,Y. Ogasawara, K. Ohashi, A Topic Extraction Method on the Flow of Conversation in Meetings, In proceedings of 6th International Congress on Advanced Applied Informatics (AAI2017), pp.350—355, 2017.[acceptance rate: 33%] 
・T. Nakanishi, R. Okada, T. Kitagawa, Automatic Media Content Creation System According to an Impression by Recognition-Creation Operators, In proceedings of 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016), pp.175–180, 2016.
・K. Matsumoto, T. Nakanishi, T. Kitagawa, Evaluation Indexes of Customer Journey for Contents of Owned Media, In Proceedings of the 26th International Conference on Information Modelling and Knowledge Bases – EJC 2016,Tampere, Finland, pp. 395-402, June 6-10, 2016.
・K. Matsumoto, R. Okada, T. Nakanishi, T. Kitagawa, The method of image feature selection for integration of image classification by Bag-of-Keypoints, In Proceedings of 2015 International Conference on Computational Science and Computational Intelligence, Las Vegas, Nevada, USA, pp. 589-594, 2015.
[acceptance rate: 19%]
・T. Nakanishi, A Discovery Method of Anteroposterior Correlation for Big Data Era, Software Engineering, In: Lee R. (eds) Studies in Computational Intelligence, Springer, Cham, Vol. 569, pp. 161-177, 2015.
・F. Toriumi, T. Nakanishi, M. Tashiro and K. Eguchi, Analysis of User Behavior on Private Chat System, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI), 2015. doi: 10.1109/WI-IAT.2015.49.
・T. Nakanishi, Data-driven Context Discovery Model for Semantic Computing in the Big Data Era, In Proceedings of SEMAPRO 2015: The Ninth International Conference on Advances in Semantic Processing, pp. 76–81, 2015.
・T. Nakanishi, A Feature Selection Method for Comparison of Each Concept in Big Data, In Proceedings of 4th IEEE/ACIS International Conference on Computer and Information Science, pp. 229-234, 2015.
・T. Nakanishi, Toward a Realization of Knowledge Creation Grid for Big Data Era, In Proceedings of 2014 IIAI 3rd International Conference on Advance Applied Informatics, IEEE, pp.167–172, 2014.
・R. Okada, T. Nakanishi, T. Kitagawa, A Method of Knowledge Creation and Knowledge Utilization by Generalized Inverse Operator, In Proceedings of 2014 IIAI 3rd International Conference on Advance Applied Informatics, IEEE, pp.253–258, 2014.
・T. Nakanishi, Semantic Context-dependent Weighting for Vector Space Model, In proceedings of 2014 IEEE International Conference on Semantic Computing (ICSC2014), pp.262-266, 2014.
[acceptance rate: 35%]
・T. Nakanishi, K. Uchimoto,Y. Kidawara, A Creation Method of Appropriate Axes for Organizing Social Media Contents and Its Application for a Cyber Curator Search, In Proceedings of IADIS International Conference e-Society 2014, Madrid, Spain, 28 February – 2 March, 2014. [acceptance rate: 19%]
・T. Nakanishi, K. Uchimoto, Y. Kidawara, Inconsistencies of Connection for Heterogeneity and a New Relation Discovery Method that Solved them, 12th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2013) Toki Messe, Niigata, Japan, June 16-20, pp.521-528, 2013.[acceptance rate: 38%]
・T. Nakanishi, K. Uchimoto, Y. Kidawara, Toward Correlation Discovery Method in the Open Assumption, The 23rd European Japanese Conference on Information Modelling and Knowledge Bases (EJC2013), Nara, Japan on June 3-7, pp.300-304, 2013.
・T. Nakanishi, K. Uchimoto, Y. Kidawara, Topic-based browsing of tendencies in twitter conversations by using social global information extraction, Proceedings of IADIS International Conference e-Society 2013, Lisbon, Portugal, pp316-325, 2013.[acceptance rate: 19%]
・E. Gonzales, T. Nakanishi, K. Zettsu, Large-Scale Association Rule Discovery from Heterogeneous Databases with Missing Values using Genetic Network Programming, Proceedings of the 1st International Conference on Advances in Information Mining and Management, pp.113-120, 2011.
・P. Rantanen, P. Sillberg, H. Jaakkola, T. Nakanishi, An Asynchronous Message-Based Knowledge Communication, in a Ubiquitous Environment. In: Yoshikawa M., Meng X., Yumoto T., Ma Q., Sun L., Watanabe C. (eds) Database Systems for Advanced Applications. DASFAA 2010 Workshop, Lecture Notes in Computer Science, vol 6193. Springer, 2010. https://doi.org/10.1007/978-3-642-14589-6_44
・R. Zhang, K. Zettsu, T. Nakanishi, Y. Kidawara, Y. Kiyoki, SOBEX: Distributed Service Search Engine that Exploits Service Collaboration Context, Proceedings of the 3rd International Universal Communication Symposium (ISUC’09), pp.261-268, 2009.
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, SAVVY Wiki: A Context-oriented Collaborative Knowledge Management System, Proceedings of ACM Intl. Symp. on Wikis and Open Collaboration (Wikisym2009), P106, 8 pages, 2009.[acceptance rate: 36%]
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, Dynamic Cross-domain Link Creation for Interconnection of Heterogeneous Knowledge Bases, Proceeding of Second International Symposium on Universal Communication (ISUC2008), pp. 374-381, Osaka, Japan, 2008.
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, Approaching to Interconnection of Heterogeneous Knowledge Bases on a Knowledge Grid, Proceeding of The International Conference on Semantics, Knowledge and Grid (SKG 2008), pp.71-78, Beijing, China, 2008. 
[acceptance rate: 25%]
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, A Vector Space Model on Hierarchical Structures with Dynamic Mapping Operator Creation,2008 International Workshop on Web Information Retrieval Support Systems (WIRSS 2008), pp. 45-49, Sydney, Australia, 2008. [acceptance rate: 24%]
・H. Kashioka, S. Akamine, T. Nakanishi, H. Miyamori, K. Zettsu, Y. Kidawara, S. Nakamura, poken Dialog System for Next Generation Knowledge Access, Proceeding of the 9th International Conference on Mobile Data Management (MDM 2008), pp.225-226, 2008 (short paper).
・M. Iwazume, K. Kaneiwa, K. Zettsu, T. Nakanishi, Y. Kidawara, Y. Kiyoki, KC3 browser: semantic mash-up and link-free browsing, Proceeding of the 17th international conference on World Wide Web (WWW 2008), pp.1209-1210, 2008 (short paper).
・T. Nakanishi, K. Zettsu, Y. Kidawara, Y. Kiyoki, Towards Interconnective Knowledge Sharing and Provision for Disaster Information Systems -Approaching to Sidoarjo Mudflow Disaster in Indonesia-, Proceedings of The 3rd Information and Communication Technology Seminar (ICTS2007), p.332-339, 2007.
・H. Homma, T. Nakanishi, T. Kitagawa, An Automatic Creation Method of the Query Vector for the Semantic Associative Search Corresponding to the Impression of Sound of Arbitrary Words, Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM2007), pp.399-402, 2007.
・S. Kishimoto, M. Murakata, T. Nakanishi, T. Sakurai, T. Kitagawa, Problem-Solving Support System for Mathematical Sciences, Proceedings of the Third IEEE International Workshop on Databases for Next-Generation Researchers, pp.79-84, 2007.
・S. Kishimoto, T. Nakanishi, M. Murakata, T. OtsukaT. Sakurai, T. Kitagawa, An Implementation Method of an Integrated Associative Search for Mathematical Expressions, Proceedings of the IASTED International Conference on Databases and Applications, pp.160-167, 2006.
・T. Nakanishi, T. Kitagawa, An Implementation Method of a Heterogeneous Associative Media Data Search for Music Data and Image Data, Proceedings of the IASTED International Conference on Databases and Applications, pp.143-152, 2006.
・T. Nakanishi, T. Kitagawa, Visualization of Music Impression in Facial Expression to Represent Emotion, Proceedings of Third Asia-Pacific Conference on Conceptual Modelling (APCCM2006), Hobart, Australia. CRPIT., Vol.53, ACS., pp.55-64, 2006.
・H. Homma, T. Nakanishi, T. Kitagawa, A Construction Method of a Retrieval Space by an Evaluation of Word Distributions in Documents, Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM ’05), pp.209-212, 2005.
・T. Nakanishi, S. Kishimoto,T. Sakurai, T. Kitagawa, A Construction Method of a Metadata Space Based on Relations Between Words from an Index of a Book, Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM ’05), pp.438-441, 2005.
・T. Nakanishi, T. Kitagawa, Y. Kiyoki, An Implementation Method of Associative Search for Heterogeneous Mediadata Utilizing the Mathematical Model of Meaning and its Application to Image Data and Facial Expression, IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM ’03), pp.613-618, 2003.