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Never-ending learning
Open accessAbstract
Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set. We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time. In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner. NELL has been learning to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits) ), while learning thousands of interrelated functions that continually improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs. We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL.
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Cited by
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
Adversarial NLI: A New Benchmark for Natural Language Understanding
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HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Dasgupta, S. S., Ray, S. N., & Talukdar, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1225 · Cited by 406
Self-citationA survey on data‐efficient algorithms in big data era
Adadi, A. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00419-9 · Cited by 331
Dynabench: Rethinking Benchmarking in NLP
Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.324 · Cited by 254
Metadata
- Authors
- Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Yang, B., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M., & Welling, J.
- Venue
- Communications of the ACM
- Published
- 2018-04-24
- Volume
- 61
- Issue
- 5
- Pages
- 103-115
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W277886906
- Cited by
- 507
- References
- 6
- FWCI
- 33.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- National Science Foundation (IIS-1065251)
- Defense Advanced Research Projects Agency (FA8750-13-2-0005)
- Air Force Office of Scientific Research (FA9550-17-1-0218)
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