{"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/an-adaptive-logical-rule-embedding-model-for","title":"An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs","arxiv_id":null,"date":"2022-12-07","proceeding":"Empirical Methods in Natural Language Processing 2022 12","authors":["Xin Mei","Libin Yang","Zuowei Jiang","Xiaoyan Cai"],"abstract":"Temporal knowledge graphs (TKGs) extrapolation reasoning predicts future events based on historical information, which has great research significance and broad application value. Existing methods can be divided into embeddingbased methods and logical rule-based methods. Embedding-based methods rely on learned entity and relation embeddings to make predictions and thus lack interpretability. Logical rule-based methods bring scalability problems due to being limited by the learned logical rules. We combine the two methods to capture deep causal logic by learning rule embeddings, and propose an interpretable model for temporal knowledge graph reasoning called adaptive logical rule embedding model for inductive reasoning (ALRE-IR). ALRE-IR can adaptively extract and assess reasons contained in historical events, and make predictions based on causal logic. Furthermore, we propose a one-class augmented matching loss for optimization. When evaluated on ICEWS14, ICEWS0515 and ICEWS18 datasets, the performance of ALRE-IR outperforms other stateof-the-art baselines. The results also demonstrate that ALRE-IR still shows outstanding performance when transferred to related dataset with common relation vocabulary, indicating our proposed model has good zero-shot reasoning ability.1","url_abs":"https://aclanthology.org/2022.emnlp-main.493/","url_pdf":"https://aclanthology.org/2022.emnlp-main.493.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":"an-adaptive-logical-rule-embedding-model-for","repo_url":"https://github.com/mxadorable/ALRET-IR","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}