Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases
Open accessAbstract
Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO.While these KBs are very large, they are still very incomplete, necessitating the use of inference to fill in gaps.Prior work has shown how to make use of a large text corpus to augment random walk inference over KBs.We present two improvements to the use of such large corpora to augment KB inference.First, we present a new technique for combining KB relations and surface text into a single graph representation that is much more compact than graphs used in prior work.Second, we describe how to incorporate vector space similarity into random walk inference over KBs, reducing the feature sparsity inherent in using surface text.This allows us to combine distributional similarity with symbolic logical inference in novel and effective ways.With experiments on many relations from two separate KBs, we show that our methods significantly outperform prior work on KB inference, both in the size of problem our methods can handle and in the quality of predictions made.
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Unlike prior path ranking studies for KG completion that leverage entities and relations in the KG for path reasoning, our path ranking model additionally considers the relation phrase in the question for ranking.
“Different from those studies using path ranking algorithm for KG completion (Lao et al., 2011;Gardner et al., 2014;Mazumder and Liu, 2017;Das et al., 2018), which leverage the entities and relations in the KG for path reasoning, our path ranking model additionally considers the relation phrase in the question for ranking.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Gardner et al., 2014
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Modeling Paths for Explainable Knowledge Base Completion
Stadelmaier, J., & Padó, S. (2019) · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w19-4816 · Cited by 12 · see citing works
Differentiable Reasoning on Large Knowledge Bases and Natural Language
Minervini, P., Bošnjak, M., Rocktäschel, T., Riedel, S., & Grefenstette, E. (2020) · Studies on the semantic web · doi:10.3233/ssw200015 · Cited by 12 · see citing works
Research progress of large-scale knowledge graph completion technology
Du, Z., Meng, X., & Wang, S. (2020) · Scientia Sinica Informationis · doi:10.1360/n112018-00225 · Cited by 12 · see citing works
Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base
Balogh, V., Berend, G., Diochnos, D. I., & Turán, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6235 · Cited by 11 · see citing works
Zhang, M., Geng, G., Zeng, S., & Jia, H. (2020) · Entropy · doi:10.3390/e22101168 · Cited by 12 · see citing works
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge
Gardner, M., & Krishnamurthy, J. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10980 · Cited by 4 · see citing works
Self-citationKnowledge Graph Reasoning via Dynamic Subgraph Attention with Low Resource Computation
Wang, Y., Xia, N., Yu, H., & Luo, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127866 · Cited by 10 · see citing works
Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
Martires, P. Z. D., Kumar, N. D., Persson, A., Loutfi, A., & Raedt, L. D. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00100 · Cited by 10 · see citing works
Mitchell, T. M., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Mishra, B. D., Gardner, M., Kisiel, B., Krishnamurthy, J., et al. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1037/e660332010-001 · Cited by 10 · see citing works
Self-citationHOPLoP: multi-hop link prediction over knowledge graph embeddings
Ranganathan, V., & Barbosa, D. (2021) · World Wide Web · doi:10.1007/s11280-021-00972-6 · Cited by 10 · see citing works
Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design
Ke, Q., & Lin, J. (2022) · Applied Sciences · doi:10.3390/app122111001 · Cited by 10 · see citing works
Knowledge Base Completion Using Matrix Factorization
He, W., Feng, Y., Zou, L., & Zhao, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25255-1_21 · Cited by 10 · see citing works
Automated Rule Base Completion as Bayesian Concept Induction
Bouraoui, Z., & Schockaert, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016228 · Cited by 10 · see citing works
Knowledge graph reasoning: Mainstream methods, applications and prospects
Liu, H., Yang, S., Shi, G., & Miao, Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111625 · Cited by 12 · see citing works
A review of background, methods, limitations and opportunities of knowledge graph completion
Li, D., Liang, Y., Qian, S., Wu, H., Jia, W., Fu, Y., & Sun, Y. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100809 · Cited by 9 · see citing works
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
Zhou, Y., Schockaert, S., & Shah, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313486 · Cited by 9 · see citing works
- Authors
- Gardner, M., Talukdar, P., Krishnamurthy, J., & Mitchell, T.
- Affiliations
- Carnegie Mellon University (US)
- Indian Institute of Science Bangalore (IN)
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 397-406
- OpenAlex
- W175897666
- Cited by
- 189
- References
- 19
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 18 · 2023: 9 · 2022: 19 · 2021: 17 · 2020: 18 · 2019: 28 · 2018: 19 · 2017: 14 · 2016: 14 · 2015: 15 · 2014: 2
- FWCI
- 8.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository (submittedVersion)
- KiltHub Repository
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency (FA8750-13-2-0005, FA8750)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- knowledge base inference, random walks, knowledge base completion, graph representation, feature sparsity, distributional semantics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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