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Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
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Falcon and SLING perform poorly for other kinds of implicit relations and cannot handle relation linking through relation paths.
“However, they perform poorly for other kinds of implicit relations, and cannot handle RL through relation paths either.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Mihindukulasooriya et al., 2020
The Qualitative Analysis (p. 10) supports a limitation in SLING: for a grandchildren question requiring two child relations, it did not decompose the question into two triples. But the Results (p. 9) say SLING identified a relation implicit in the question, and the cited passages do not establish that Falcon and SLING generally perform poorly on other implicit relations or cannot handle relation paths as a class. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDTQA uses SLING, the state-of-the-art REL system for KBQA.
“For this task, DTQA uses SLING (Mihindukulasooriya et al. 2020); the state-of-the-art REL system for KBQA.”
From A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering (2021) · cites this paper as Mihindukulasooriya et al. 2020
SLING relies on an ensemble of complementary relation linking approaches to produce a top-k list of KB relations for each AMR predicate.
“It relies on an ensemble of complementary relation linking approaches to produce a top-k list of KB relations for each AMR predicate.”
From A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering (2021) · cites this paper as Mihindukulasooriya et al. 2020
SLING's most relevant and novel approach is statistical mapping of an AMR predicate to its semantically equivalent relation from the underlying KB using a distant supervision dataset.
“The most relevant and novel approach in SLING is its statistical mapping of an AMR predicate to its semantically equivalent relation from the underlying KB using a distant supervision dataset.”
From A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering (2021) · cites this paper as Mihindukulasooriya et al. 2020
AMR can effectively capture semantic information at the sentence level.
“according to (Mihindukulasooriya et al., 2020), AMR can effectively capture the semantic information at the sentence level”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Mihindukulasooriya et al., 2020
SLING leverages AMR for preprocessing, trains a Transformer-based model based on distant supervision, and integrates several complementary signals for relation linking.
“SLING (Mihindukulasooriya et al., 2020) leverages AMR for preprocessing, trains a Transformer-based model based on distant supervision, and integrates several complementary signals for RL.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Mihindukulasooriya et al., 2020
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
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Research works printed but not linked: 23
- Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52 · DOI · Paper page
- Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedi, P., Lehmann, J., Fischer, A.: Introduction to neural network based approaches for question answering over knowledge graphs. CoRR abs/1907.09361 (2019) · arXiv
- Dubey, M., Banerjee, D., Chaudhuri, D., Lehmann, J.: EARL: joint entity and relation linking for question answering over knowledge graphs. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11136, pp. 108–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00671-6_7 · DOI · Paper page
- Höffner, K., Walter, S., Marx, E., Usbeck, R., Lehmann, J., Ngomo, A.N.: Survey on challenges of question answering in the semantic web. Semant. Web 8(6), 895–920 (2017) · DOI
- Hu, S., Zou, L., Yu, J.X., Wang, H., Zhao, D.: Answering natural language questions by subgraph matching over knowledge graphs. IEEE Trans. Knowl. Data Eng. 30(5), 824–837 (2018) · DOI
- Kingsbury, P., Palmer, M.: PropBank: the next level of treebank. In: Proceedings of Treebanks and lexical Theories, vol. 3. Citeseer (2003)
- Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: Zero-shot entity linking with dense entity retrieval. arXiv:1911.03814 (2019) · DOI
- Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015) · DOI
- Lukovnikov, D., Fischer, A., Lehmann, J.: Pretrained transformers for simple question answering over knowledge graphs. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 470–486. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_27 · DOI
- Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: ACL 2009, pp. 1003–1011 (2009) · DOI
- Mulang, I.O., Singh, K., Orlandi, F.: Matching natural language relations to knowledge graph properties for question answering. In: SEMANTiCS 2017, pp. 89–96 (2017) · DOI
- Naseem, T., Shah, A., Wan, H., Florian, R., Roukos, S., Ballesteros, M.: Rewarding smatch: Transition-based AMR parsing with reinforcement learning. arXiv preprint arXiv:1905.13370 (2019) · DOI
- Pan, J.Z., Zhang, M., Singh, K., Harmelen, F., Gu, J., Zhang, Z.: Entity enabled relation linking. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11778, pp. 523–538. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30793-6_30 · DOI · Paper page
- Rossiello, G., Gliozzo, A., Farrell, R., Fauceglia, N.R., Glass, M.R.: Learning relational representations by analogy using hierarchical siamese networks. In: NAACL-HLT (1), pp. 3235–3245. Association for Computational Linguistics (2019) · DOI
- Sakor, A., et al.: Old is gold: linguistic driven approach for entity and relation linking of short text. In: NAACL: HLT 2019, pp. 2336–2346 (2019) · DOI · Paper page
- Singh, K., Both, A., Sethupat, A., Shekarpour, S.: Frankenstein: a platform enabling reuse of question answering components. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 624–638. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_40 · DOI
- Singh, K., et al.: Capturing knowledge in semantically-typed relational patterns to enhance relation linking. In: K-CAP 2017, pp. 1–8 (2017) · DOI · Paper page
- Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: distributional similarity for relation learning. In: ACL 2019, pp. 2895–2905 (2019)
- Trivedi, P., Maheshwari, G., Dubey, M., Lehmann, J.: LC-QuAD: a corpus for complex question answering over knowledge graphs. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 210–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_22 · DOI · Paper page
- Usbeck, R., Gusmita, R.H., Ngomo, A.N., Saleem, M.: 9th challenge on question answering over linked data (QALD-9) (invited paper). In: Semdeep/NLIWoD@ISWC. CEUR Workshop Proceedings, vol. 2241, pp. 58–64. CEUR-WS.org (2018)
- Usbeck, R., Ngomo, A.-C.N., Haarmann, B., Krithara, A., Röder, M., Napolitano, G.: 7th open challenge on question answering over linked data (QALD-7). In: Dragoni, M., Solanki, M., Blomqvist, E. (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 59–69. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6_6 · DOI
- Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
- Yu, M., Yin, W., Hasan, K.S., dos Santos, C.N., Xiang, B., Zhou, B.: Improved neural relation detection for knowledge base question answering. In: ACL 2017, pp. 571–581 (2017) · DOI
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Sorted by citations
A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering
Abdelaziz, I., Ravishankar, S., Kapanipathi, P., Roukos, S., & Gray, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.17988 · Cited by 26
“Apart from this statistical co-occurrence information, the rich lexical information in the AMR predicate and question text is also leveraged by SLING.”
MethodInfluentialSelf-citationImplicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5
Metadata
- Authors
- Mihindukulasooriya, N., Rossiello, G., Kapanipathi, P., Abdelaziz, I., Ravishankar, S., Yu, M., Gliozzo, A., Roukos, S., & Gray, A.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Corresponding author
- Mihindukulasooriya, Nandana
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 402-419
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3085243036
- Cited by
- 24
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 5 · 2022: 6 · 2021: 6 · 2020: 2
- FWCI
- 1.81
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- semantic parsing, Abstract Meaning Representation, distant supervision, knowledge base question answering, relation extraction, sling, LC-QuAD
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