{"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/time-aware-multiway-adaptive-fusion-network","title":"Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering","arxiv_id":"2302.12529","date":"2023-02-24","proceeding":null,"authors":["Yonghao Liu","Di Liang","Fang Fang","Sirui Wang","Wei Wu","Rui Jiang"],"abstract":"Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \\emph{temporal-specific} presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel \\textbf{T}ime-aware \\textbf{M}ultiway \\textbf{A}daptive (\\textbf{TMA}) fusion network. Inspired by the step-by-step reasoning behavior of humans. For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \\emph{temporal-specific} representation of the question. This representation can be incorporated with the pre-trained KG embedding to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of TMA on the CronQuestions dataset's complex questions are absolutely improved by 24\\% and 10\\% compared to the best-performing baseline. Furthermore, we also show that TMA employing an adaptive fusion mechanism can provide interpretability by analyzing the proportion of information in question representations.","url_abs":"https://arxiv.org/abs/2302.12529v2","url_pdf":"https://arxiv.org/pdf/2302.12529v2.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":[],"tasks":[{"task_slug":"graph-question-answering","task_name":"Graph Question Answering"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-cronquestions","task":"Question Answering","dataset":"CronQuestions","model":"TMA","rank_in_archive_order":15,"of":29,"metrics":{"Hits@1":"78.4"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-timequestions","task":"Question Answering","dataset":"TimeQuestions","model":"TMA","rank_in_archive_order":13,"of":21,"metrics":{"P@1":"43.6"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.12529","atlas_url":"https://app.syntology.ai/?focus=2302.12529","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}