
Abstract
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two enti- ties connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering
Item Type: | Conference or Workshop Item (Paper) |
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EU Funded Grant Agreement Number: | 740516 |
EU Projects: | Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement |
Research Centers: | Center for Information and Language Processing (CIS) |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems 000 Computer science, information and general works > 004 Data processing computer science 400 Language > 400 Language 400 Language > 410 Linguistics |
URN: | urn:nbn:de:bvb:19-epub-61860-0 |
Place of Publication: | Stroudsburg, PA |
Annotation: | ISBN 978-1-948087-64-3 |
Language: | English |
Item ID: | 61860 |
Date Deposited: | 13. May 2019, 13:02 |
Last Modified: | 04. Nov 2020, 13:39 |