{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chains-of-reasoning-over-entities-relations","title":"Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks","arxiv_id":"1607.01426","date":"2016-07-05","proceeding":"EACL 2017 4","authors":["Rajarshi Das","Arvind Neelakantan","David Belanger","Andrew McCallum"],"abstract":"Our goal is to combine the rich multistep inference of symbolic logical\nreasoning with the generalization capabilities of neural networks. We are\nparticularly interested in complex reasoning about entities and relations in\ntext and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs\nto compose the distributed semantics of multi-hop paths in KBs; however for\nmultiple reasons, the approach lacks accuracy and practicality. This paper\nproposes three significant modeling advances: (1) we learn to jointly reason\nabout relations, entities, and entity-types; (2) we use neural attention\nmodeling to incorporate multiple paths; (3) we learn to share strength in a\nsingle RNN that represents logical composition across all relations. On a\nlargescale Freebase+ClueWeb prediction task, we achieve 25% error reduction,\nand a 53% error reduction on sparse relations due to shared strength. On chains\nof reasoning in WordNet we reduce error in mean quantile by 84% versus previous\nstate-of-the-art. The code and data are available at\nhttps://rajarshd.github.io/ChainsofReasoning","url_abs":"http://arxiv.org/abs/1607.01426v3","url_pdf":"http://arxiv.org/pdf/1607.01426v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chains-of-reasoning-over-entities-relations","repo_url":"https://github.com/eBay/KPRN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"chains-of-reasoning-over-entities-relations","repo_url":"https://github.com/rajarshd/ChainsofReasoning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"logical-reasoning","task_name":"Logical Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1607.01426","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}