skip to main content
10.1145/3477495.3531751acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article

Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?

Published: 07 July 2022 Publication History

Abstract

Existing approaches on Question Answering over Knowledge Graphs (KGQA) have weak generalizability. That is often due to the standard i.i.d. assumption on the underlying dataset. Recently, three levels of generalization for KGQA were defined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQA datasets for 5 different Knowledge Graphs (KGs). We show that according to this definition many existing and online available KGQA datasets are either not suited to train a generalizable KGQA system or that the datasets are based on discontinued and out-dated KGs. Generating new datasets is a costly process and, thus, is not an alternative to smaller research groups and companies. In this work, we propose a mitigation method for re-splitting available KGQA datasets to enable their applicability to evaluate generalization, without any cost and manual effort. We test our hypothesis on three KGQA datasets, i.e., LC-QuAD, LC-QuAD 2.0 and QALD-9). Experiments on re-splitted KGQA datasets demonstrate its effectiveness towards generalizability. The code and a unified way to access 18 available datasets is online at https://github.com/semantic-systems/KGQA-datasets as well as https://github.com/semantic-systems/KGQA-datasets-generalization.

Supplementary Material

MP4 File (SIGIR22-rs2245.mp4)
Presentation video

References

[1]
Katrin Affolter, Kurt Stockinger, and Abraham Bernstein. 2019. A comparative survey of recent natural language interfaces for databases. VLDB J. 28, 5 (2019), 793--819. https://doi.org/10.1007/s00778-019-00567--8
[2]
Michael Azmy, Peng Shi, Jimmy Lin, and Ihab F. Ilyas. 2018. Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia. In Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20--26, 2018, Emily M. Bender, Leon Derczynski, and Pierre Isabelle (Eds.). Association for Computational Linguistics, 2093--2103. https://aclanthology.org/C18--1178/
[3]
Junwei Bao, Nan Duan, Zhao Yan, Ming Zhou, and Tiejun Zhao. 2016. ConstraintBased Question Answering with Knowledge Graph. In COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11--16, 2016, Osaka, Japan, Nicoletta Calzolari, Yuji Matsumoto, and Rashmi Prasad (Eds.). ACL, 2503--2514. https://aclanthology.org/C16- 1236/
[4]
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic Parsing on Freebase from Question-Answer Pairs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18--21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 1533--1544. https://aclanthology. org/D13--1160/
[5]
Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015. Largescale Simple Question Answering with Memory Networks. CoRR abs/1506.02075 (2015). arXiv:1506.02075 http://arxiv.org/abs/1506.02075
[6]
Abdelghani Bouziane, Djelloul Bouchiha, Noureddine Doumi, and Mimoun Malki. 2015. Question Answering Systems: Survey and Trends. Procedia Computer Science 73 (2015), 366--375. https://doi.org/10.1016/j.procs.2015.12.005 International Conference on Advanced Wireless Information and Communication Technologies (AWICT 2015).
[7]
Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, and Asja Fischer. 2021. Introduction to neural network-based question answering over knowledge graphs. WIREs Data Mining Knowl. Discov. 11, 3 (2021). https://doi.org/10.1002/widm.1389
[8]
Yongrui Chen, Huiying Li, Guilin Qi, Tianxing Wu, and Tenggou Wang. 2021. Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graph. CoRR abs/2111.00732 (2021). arXiv:2111.00732 https://arxiv.org/abs/2111.00732
[9]
Tarcísio Souza Costa, Simon Gottschalk, and Elena Demidova. 2020. Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020, Mathieu d'Aquin, Stefan Dietze, Claudia Hauff, Edward Curry, and Philippe Cudré-Mauroux (Eds.). ACM, 3157--3164. https://doi.org/10.1145/3340531.3412760
[10]
Ruixiang Cui, Rahul Aralikatte, Heather C. Lent, and Daniel Hershcovich. 2021. Multilingual Compositional Wikidata Questions. CoRR abs/2108.03509 (2021). arXiv:2108.03509 https://arxiv.org/abs/2108.03509
[11]
Dennis Diefenbach, Kamal Deep Singh, and Pierre Maret. 2017. WDAqua-core0: A Question Answering Component for the Research Community. In Semantic Web Challenges - 4th SemWebEval Challenge at ESWC 2017, Portoroz, Slovenia, May 28 - June 1, 2017, Revised Selected Papers (Communications in Computer and Information Science, Vol. 769), Mauro Dragoni, Monika Solanki, and Eva Blomqvist (Eds.). Springer, 84--89. https://doi.org/10.1007/978--3--319--69146--6_8
[12]
Jiwei Ding, Wei Hu, Qixin Xu, and Yuzhong Qu. 2019. Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, 2614--2622.
[13]
Mohnish Dubey, Debayan Banerjee, Abdelrahman Abdelkawi, and Jens Lehmann. 2019. LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia. In The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26--30, 2019, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 11779), Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel F. Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, and Fabien Gandon (Eds.). Springer, 69--78. https://doi.org/10.1007/ 978--3-030--30796--7_5
[14]
Yu Gu, Sue Kase, Michelle Vanni, Brian M. Sadler, Percy Liang, Xifeng Yan, and Yu Su. 2021. Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases. In WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19--23, 2021, Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.). ACM / IW3C2, 3477--3488. https: //doi.org/10.1145/3442381.3449992
[15]
Ria Hari Gusmita, Rricha Jalota, Daniel Vollmers, Jan Reineke, AxelCyrille Ngonga Ngomo, and Ricardo Usbeck. 2019. QUANT - Question Answering Benchmark Curator. In Semantic Systems. The Power of AI and Knowledge Graphs - 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9--12, 2019, Proceedings (Lecture Notes in Computer Science, Vol. 11702), Maribel Acosta, Philippe Cudré-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald Sack, and York Sure-Vetter (Eds.). Springer, 343--358. https://doi.org/10.1007/978--3-030--33220--4_25
[16]
Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Strötgen, and Gerhard Weikum. 2018. TempQuestions: A Benchmark for Temporal Question Answering. In Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, April 23--27, 2018, Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis (Eds.). ACM, 1057--1062. https: //doi.org/10.1145/3184558.3191536
[17]
Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy, and Gerhard Weikum. 2021. Complex Temporal Question Answering on Knowledge Graphs. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 792--802. https://doi.org/10.1145/3459637.3482416
[18]
Kelvin Jiang, Dekun Wu, and Hui Jiang. 2019. FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 318--323. https://doi.org/10.18653/v1/n19--1028
[19]
Lucie-Aimée Kaffee, Kemele M. Endris, Elena Simperl, and Maria-Esther Vidal. 2019. Ranking Knowledge Graphs By Capturing Knowledge about Languages and Labels. In Proceedings of the 10th International Conference on Knowledge Capture, K-CAP 2019, Marina Del Rey, CA, USA, November 19--21, 2019, Mayank Kejriwal, Pedro A. Szekely, and Raphaël Troncy (Eds.). ACM, 21--28. https: //doi.org/10.1145/3360901.3364443
[20]
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020. Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https://openreview. net/forum?id=SygcCnNKwr
[21]
Vladislav Korablinov and Pavel Braslavski. 2020. RuBQ: A Russian Dataset for Question Answering over Wikidata. In The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, Athens, Greece, November 2--6, 2020, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 12507), Jeff Z. Pan, Valentina A. M. Tamma, Claudia d'Amato, Krzysztof Janowicz, Bo Fu, Axel Polleres, Oshani Seneviratne, and Lalana Kagal (Eds.). Springer, 97--110. https: //doi.org/10.1007/978--3-030--62466--8_7
[22]
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5--10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 7871--7880. https://doi.org/10.18653/v1/2020.aclmain.703
[23]
Ivan Rybin, Vladislav Korablinov, Pavel Efimov, and Pavel Braslavski. 2021. RuBQ 2.0: An Innovated Russian Question Answering Dataset. In The Semantic Web - 18th International Conference, ESWC 2021, Virtual Event, June 6--10, 2021, Proceedings (Lecture Notes in Computer Science, Vol. 12731), Ruben Verborgh, Katja Hose, Heiko Paulheim, Pierre-Antoine Champin, Maria Maleshkova, Óscar Corcho, Petar Ristoski, and Mehwish Alam (Eds.). Springer, 532--547. https://doi.org/10.1007/978--3-030--77385--4_32
[24]
Muhammad Saleem, Samaneh Nazari Dastjerdi, Ricardo Usbeck, and AxelCyrille Ngonga Ngomo. 2017. Question Answering Over Linked Data: What is Difficult to Answer? What Affects the F scores?. In Joint Proceedings of BLINK2017: 2nd International Workshop on Benchmarking Linked Data and NLIWoD3: Natural Language Interfaces for the Web of Data co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 21st - to - 22nd, 2017 (CEUR Workshop Proceedings, Vol. 1932), Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo, Jin-Dong Kim, Key-Sun Choi, Philipp Cimiano, Irini Fundulaki, and Anastasia Krithara (Eds.). CEUR-WS.org. http://ceur-ws.org/Vol-1932/paper-02.pdf
[25]
Apoorv Saxena, Soumen Chakrabarti, and Partha P. Talukdar. 2021. Question Answering Over Temporal Knowledge Graphs. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1--6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 6663--6676. https://doi.org/10.18653/v1/2021.acl-long.520
[26]
Iulian Vlad Serban, Alberto García-Durán, Çaglar Gülçehre, Sungjin Ahn, Sarath Chandar, Aaron C. Courville, and Yoshua Bengio. 2016. Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus. (2016). https://doi.org/10.18653/v1/p16--1056
[27]
Lucia Siciliani, Pierpaolo Basile, Pasquale Lops, and Giovanni Semeraro. 2021. MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs. Semantic Web Preprint (2021), 1--17.
[28]
Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Saeedeh Shekarpour, Maria-Esther Vidal, and Jens Lehmann. 2020. No one is perfect: Analysing the performance of question answering components over the DBpedia knowledge graph. J. Web Semant. 65 (2020), 100594. https://doi.org/10.1016/j.websem.2020. 100594
[29]
Nadine Steinmetz and Kai-Uwe Sattler. 2021. What is in the KGQA Benchmark Datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs. J. Data Semant. 10, 3--4 (2021), 241--265. https://doi.org/10.1007/ s13740-021-00128--9
[30]
Yu Su, Huan Sun, Brian M. Sadler, Mudhakar Srivatsa, Izzeddin Gur, Zenghui Yan, and Xifeng Yan. 2016. On Generating Characteristic-rich Question Sets for QA Evaluation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1--4, 2016, Jian Su, Xavier Carreras, and Kevin Duh (Eds.). The Association for Computational Linguistics, 562--572. https://doi.org/10.18653/v1/d16--1054
[31]
Alon Talmor and Jonathan Berant. 2018. The Web as a Knowledge-Base for Answering Complex Questions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1--6, 2018, Volume 1 (Long Papers), Marilyn A. Walker, Heng Ji, and Amanda Stent (Eds.). Association for Computational Linguistics, 641--651. https://doi.org/10. 18653/v1/n18--1059
[32]
Priyansh Trivedi, Gaurav Maheshwari, Mohnish Dubey, and Jens Lehmann. 2017. LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs. In The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21--25, 2017, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 10588), Claudia d'Amato, Miriam Fernández, Valentina A. M. Tamma, Freddy Lécué, Philippe Cudré-Mauroux, Juan F. Sequeda, Christoph Lange, and Jeff Heflin (Eds.). Springer, 210--218. https://doi.org/10.1007/978--3--319--68204- 4_22
[33]
Christina Unger, Axel-Cyrille Ngonga Ngomo, and Elena Cabrio. 2016. 6th Open Challenge on Question Answering over Linked Data (QALD-6). In Semantic Web Challenges - Third SemWebEval Challenge at ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Revised Selected Papers (Communications in Computer and Information Science, Vol. 641), Harald Sack, Stefan Dietze, Anna Tordai, and Christoph Lange (Eds.). Springer, 171--177. https://doi.org/10.1007/978--3--319- 46565--4_13
[34]
Ricardo Usbeck, Ria Hari Gusmita, Axel-Cyrille Ngonga Ngomo, and Muhammad Saleem. 2018. 9th Challenge on Question Answering over Linked Data (QALD-9) (invited paper). In Joint proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4) and NLIWoD4: Natural Language Interfaces for the Web of Data (NLIWOD-4) and 9th Question Answering over Linked Data challenge (QALD-9) co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, California, United States of America, October 8th - 9th, 2018 (CEUR Workshop Proceedings, Vol. 2241), Key-Sun Choi, Luis Espinosa Anke, Thierry Declerck, Dagmar Gromann, Jin-Dong Kim, Axel-Cyrille Ngonga Ngomo, Muhammad Saleem, and Ricardo Usbeck (Eds.). CEUR-WS.org, 58--64. http://ceur-ws.org/Vol2241/paper-06.pdf
[35]
Ricardo Usbeck, Michael Röder, Michael Hoffmann, Felix Conrads, Jonathan Huthmann, Axel-Cyrille Ngonga Ngomo, Christian Demmler, and Christina Unger. 2019. Benchmarking question answering systems. Semantic Web 10, 2 (2019), 293--304. https://doi.org/10.3233/SW-180312
[36]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4- 9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 5998--6008. https://proceedings.neurips.cc/paper/2017/hash/ 3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
[37]
Daniel Vollmers, Rricha Jalota, Diego Moussallem, Hardik Topiwala, AxelCyrille Ngonga Ngomo, and Ricardo Usbeck. 2021. Knowledge Graph Question Answering Using Graph-Pattern Isomorphism. In Further with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6--9, 2021 (Studies on the Semantic Web, Vol. 53), Mehwish Alam, Paul Groth, Victor de Boer, Tassilo Pellegrini, Harshvardhan J. Pandit, Elena Montiel-Ponsoda, Víctor Rodríguez-Doncel, Barbara McGillivray, and Albert Meroño-Peñuela (Eds.). IOS Press, 103--117. https://doi.org/10.3233/SSW210038
[38]
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh. 2016. The Value of Semantic Parse Labeling for Knowledge Base Question Answering. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7--12, 2016, Berlin, Germany, Volume 2: Short Papers. The Association for Computer Linguistics. https://doi.org/10. 18653/v1/p16--2033
[39]
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, and Le Song. 2018. Variational Reasoning for Question Answering With Knowledge Graph. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 6069--6076. https://www.aaai.org/ocs/index.php/ AAAI/AAAI18/paper/view/16983
[40]
Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2018. An Interpretable Reasoning Network for Multi-Relation Question Answering. In Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20--26, 2018, Emily M. Bender, Leon Derczynski, and Pierre Isabelle (Eds.). Association for Computational Linguistics, 2010--2022. https://aclanthology.org/C18--1171/

Cited By

View all
  • (2025)RP-KGC: A Knowledge Graph Completion Model Integrating Rule-Based Knowledge for Pretraining and InferenceBig Data Mining and Analytics10.26599/BDMA.2024.90200638:1(18-30)Online publication date: Feb-2025
  • (2025)The Generalization and Error Detection in LLM-based Text-to-SQL SystemsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3707416(1077-1079)Online publication date: 10-Mar-2025
  • (2025)Logic-infused knowledge graph QA: Enhancing large language models for specialized domains through Prolog integrationData & Knowledge Engineering10.1016/j.datak.2025.102406157(102406)Online publication date: May-2025
  • Show More Cited By

Index Terms

  1. Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
      July 2022
      3569 pages
      ISBN:9781450387323
      DOI:10.1145/3477495
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 July 2022

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. benchmark
      2. evaluation
      3. generalizability
      4. generalization
      5. kgqa
      6. question answering

      Qualifiers

      • Research-article

      Funding Sources

      • Federal Ministry for Economic Affairs and Climate Action of Germany

      Conference

      SIGIR '22
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 792 of 3,983 submissions, 20%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)96
      • Downloads (Last 6 weeks)16
      Reflects downloads up to 28 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2025)RP-KGC: A Knowledge Graph Completion Model Integrating Rule-Based Knowledge for Pretraining and InferenceBig Data Mining and Analytics10.26599/BDMA.2024.90200638:1(18-30)Online publication date: Feb-2025
      • (2025)The Generalization and Error Detection in LLM-based Text-to-SQL SystemsProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3707416(1077-1079)Online publication date: 10-Mar-2025
      • (2025)Logic-infused knowledge graph QA: Enhancing large language models for specialized domains through Prolog integrationData & Knowledge Engineering10.1016/j.datak.2025.102406157(102406)Online publication date: May-2025
      • (2024)Aligning Large Language Models to a Domain-specific Graph Database for NL2GQLProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679713(1367-1377)Online publication date: 21-Oct-2024
      • (2024)A survey on augmenting knowledge graphs (KGs) with large language models (LLMs): models, evaluation metrics, benchmarks, and challengesDiscover Artificial Intelligence10.1007/s44163-024-00175-84:1Online publication date: 4-Nov-2024
      • (2023)QALD-10 – The 10th challenge on question answering over linked dataSemantic Web10.3233/SW-233471(1-15)Online publication date: 28-Nov-2023
      • (2023)A Fine Granular Relational Reasoning KGQA model Based on Weak Supervised Learning2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507374(2198-2206)Online publication date: 8-Dec-2023
      • (2023)Knowledge Graph Completion via Subgraph Topology AugmentationSocial Media Processing10.1007/978-981-99-7596-9_2(14-29)Online publication date: 15-Nov-2023
      • (2023)Parameter-Lite Adapter for Dynamic Entity AlignmentPRICAI 2023: Trends in Artificial Intelligence10.1007/978-981-99-7019-3_36(389-400)Online publication date: 15-Nov-2023
      • (2022)OntologueProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601902(22463-22476)Online publication date: 28-Nov-2022
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media