This page lists the books authored by Professor Kaneiwa, the systems that implement the research results of the Kaneiwa Laboratory, and the research presentations given by its students.
Description Logics and Web Ontologies (in Japanese)
Ken Kaneiwa Ohmsha, Ltd. 2009
Workshop Papers (Most Recent)
Hayate Takahashi, Ken Kaneiwa: Two-Stage Ontology Completion Using Candidate Reduction by Knowledge Graph Embedding and Definition-Based Language Models, JSAI Special Interest Group on Knowledge-Based Systems (SIG-KBS), December 2025 (in Japanese). [PDF]
Yuto Hino, Ken Kaneiwa: Ensemble Learning of Contrastive Learning Models for Node Classification on Noisy Graphs, JSAI Special Interest Group on Knowledge-Based Systems (SIG-KBS), December 2025 (in Japanese). [PDF]
Ziqi Tian, Ken Kaneiwa: Few-Shot Textual Entailment via Prototype Augmentation Using Competing Contexts, JSAI Special Interest Group on Artificial General Intelligence (SIG-AGI), December 2025 (in Japanese). [PDF]
Yukihiro Shiraishi, Ken Kaneiwa: A self-matching training method with annotation embedding models for ontology subsumption prediction, International Journal of Data Science and Analytics, Vol.22, article number 86, 2026. [Link, Preprint]
Yuki Iwamoto, Ken Kaneiwa, Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion, Proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024), pp. 299-304, 2024. [Link, Preprint]
Yukihiro Shiraishi, Ken Kaneiwa: Embedding Models with Inverted-index and Co-occurrence Matrices for Ontology Subsumption Prediction, Proceedings of the 12th International Joint Conference on Knowledge Graphs (IJCKG 2023) [PDF]
Ken Kaneiwa, Yota Minami: Feature Selection Based on the Complexity of Structural Patterns in RDF Graphs, International Journal of Data Science and Analytics, 2023. Springer [Link, Preprint]
Yuki Odaka, Ken Kaneiwa:
Block-Segmentation Vectors for Arousal Prediction using Semi-supervised Learning, Applied Soft Computing, Vol.142, 2023. Elsevier [Link, Preprint]
Yuga Oishi, Ken Kaneiwa, Multi-Duplicated Characterization of Graph Structures Using Information Gain Ratio for Graph Neural Networks, IEEE Access, Vol.11, pp.34421-34430, 2023 [PDF]
Yuga Oishi, Ken Kaneiwa, Hierarchical Model Selection for Graph Neural Networks, IEEE Access, Vol.11, pp.16974-16983, 2023 [PDF]
Daichi Arai, Ken Kaneiwa: General Kernel Functions for the Diversity of RDF Graphs, Transactions of the Japanese Society for Artificial Intelligence, Vol. 33, No. 5, pp. B-I12_1-14, 2018 (in Japanese). [PDF]
Daichi Arai, Ken Kaneiwa: Kernel Functions for Redundant Feature Representations of RDF Graphs and Their Fast Computation, Transactions of the Japanese Society for Artificial Intelligence, Vol. 32, No. 1, pp. B-G34_1-12, 2017 (in Japanese). [PDF]
Research Papers (Semantic Web)
Koji Fujiwara, Ken Kaneiwa, Efficient Index Structures for Query Solutions on RDF Graphs, Proceedings of the 26th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2024-Summer III), 2024. [Link]
Ken Kaneiwa, Daiki Takahashi:
The Completeness of Reasoning Algorithms for Clause Sets in Description Logic ALC, Knowledge-Based Systems, 2024. Elsevier [Link, Preprint]
Ken Kaneiwa, Yuki Yamanaka: Converging-Path Search via Equivalence Relations from Multiple Large-Scale RDF Datasets, Transactions of the Japanese Society for Artificial Intelligence, Vol. 38, No. 2, pp. D-M53_1-9, 2023 (in Japanese). [PDF]
Ken Kaneiwa, Kenta Hirayama: Equivalent Transformations and Computational Complexity of SPARQL Queries with Duplicate Elimination, DBSJ Japanese Journal, Vol.21-J, Article No.1, 2023 (in Japanese). [PDF]
Ken Kaneiwa, Takuma Nagai: Concept Generation in the Description Logic SROIQ Based on Minimal RDF Reasoning, Transactions of the Japanese Society for Artificial Intelligence, Vol. 35, No. 1, pp. B-J62_1-13, 2020 (in Japanese). [PDF]
Miki Hirohashi, Ken Kaneiwa: Graph Pattern Mining for RDF Data, DBSJ Japanese Journal, Vol.17-J, Article No.1, 2019 (in Japanese). [PDF]
Ken Kaneiwa, Koji Fujiwara: Indexed Data Compression and Fast Search for Large-Scale RDF Graphs, Transactions of the Japanese Society for Artificial Intelligence, Vol. 33, No. 2, pp. E-H43_1-10, 2018 (in Japanese). [PDF]
Daichi Arai, Ken Kaneiwa: Kernel Functions for Redundant Feature Representations of RDF Graphs and Their Fast Computation, Transactions of the Japanese Society for Artificial Intelligence, Vol. 32, No. 1, pp. B-G34_1-12, 2017 (in Japanese). [PDF]
Awards
Excellence Award, JSAI Special Interest Group on Knowledge-Based Systems: Yuga Oishi, Ken Kaneiwa, “Graph Neural Networks Based on Multi-Duplicated Characterization of Graph Structures Using the Information Gain Ratio (IGR),” 2023.
Research Encouragement Award, IEICE Technical Committee on Artificial Intelligence and Knowledge-Based Processing: Yukihiro Shiraishi, Ken Kaneiwa, “Embedding Models for Logical Reasoning on Individuals and Subsumption Relations,” 2021.
Excellence Award, JSAI Special Interest Group on Semantic Web and Ontology: Koji Fujiwara, Ken Kaneiwa, “Achieving Both Fast Search and Data Compression for Large-Scale RDF Graphs,” 2014. [PDF]
Publicly Available Systems
SPARQL search engine: FROST [related to journal papers 4 and 6]
Description logic query system for RDF data: Picker [related to journal papers 1 and 7]
Corrected Paper
Because the experimental data contained errors, we have released a corrected version in which the experiments were repeated with the correct data.
Yota Minami, Ken Kaneiwa: Supervised Learning with Diverse Feature Vectors Extracted from RDF Graphs, The 49th Meeting of the JSAI Special Interest Group on Semantic Web and Ontology, SIG-SWO-049-04, 2019 (in Japanese). [Corrected PDF] (Changes: all values (accuracy) in Tables 2 and 3, and the descriptions of the experiments in Section 3.3 and Section 4)
