Pay More Attention to Relation Exploration for Knowledge Base Question Answering

• URL: http://arxiv.org/abs/2305.02118v2
• Date: Thu, 25 May 2023 10:15:28 GMT
• Title: Pay More Attention to Relation Exploration for Knowledge Base Question Answering
• Authors: Yong Cao, Xianzhi Li, Huiwen Liu, Wen Dai, Shuai Chen, Bin Wang, Min Chen and Daniel Hershcovich
• Abstract summary: We propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation. We show that our framework improves the F1 score by 5.7% from 40.5 to 46.3 on CWQ and 5.8% from 62.8 to 68.5 on WebQSP.
• Score: 17.273836429397203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically determine the reasoning path selection, are not fully considered in recent advancements. In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision. We explore guidance from relations in three aspects, including (1) distinguishing similar entities by employing a variational graph auto-encoder to learn relation importance; (2) exploring extra supervision by predicting relation distributions as soft labels with a multi-task scheme; (3) designing a relation-guided re-ranking algorithm for post-processing. Experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our framework, improving the F1 score by 5.7% from 40.5 to 46.3 on CWQ and 5.8% from 62.8 to 68.5 on WebQSP, better or on par with state-of-the-art methods.

Related papers

• Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education [14.368011453534596]
  We introduce Graphusion, a zero-shot knowledge graph framework from free text. The core fusion module provides a global view of triplets, incorporating entity merging, conflict resolution, and novel triplet discovery. Our evaluation demonstrates that Graphusion surpasses supervised baselines by up to 10% in accuracy on link prediction.
  arXiv  Detail & Related papers  (2024-07-15T15:13:49Z)
• Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering [61.53454387743701]
  We propose CPACE, a concept-centric Prompt-bAsed Contrastive Explanation Generation model. CPACE converts obtained symbolic knowledge into a contrastive explanation for better distinguishing the differences among given candidates. We conduct a series of experiments on three widely-used question-answering datasets: CSQA, QASC, and OBQA.
  arXiv  Detail & Related papers  (2023-05-14T12:12:24Z)
• KGE-CL: Contrastive Learning of Knowledge Graph Embeddings [64.67579344758214]
  We propose a simple yet efficient contrastive learning framework for knowledge graph embeddings. It can shorten the semantic distance of the related entities and entity-relation couples in different triples. It can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.
  arXiv  Detail & Related papers  (2021-12-09T12:45:33Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction [66.09605613944201]
  We quantitatively evaluate the effect of attention and Knowledge Graph on bag-level relation extraction (RE) We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns; and (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior.
  arXiv  Detail & Related papers  (2021-07-26T09:38:28Z)
• Multi-view Inference for Relation Extraction with Uncertain Knowledge [8.064148591925932]
  This paper proposes to exploit uncertain knowledge to improve relation extraction. We introduce ProBase, an uncertain KG that indicates to what extent a target entity belongs to a concept. We then design a novel multi-view inference framework to systematically integrate local context and global knowledge.
  arXiv  Detail & Related papers  (2021-04-28T05:56:33Z)
• A Question-answering Based Framework for Relation Extraction Validation [18.132034601588742]
  We argue that validation is an important and promising direction to further improve the performance of relation extraction. We propose a novel question-answering based framework to validate the results from relation extraction models.
  arXiv  Detail & Related papers  (2021-04-07T06:08:36Z)
• Leveraging Semantic Parsing for Relation Linking over Knowledge Bases [80.99588366232075]
  We present SLING, a relation linking framework which leverages semantic parsing using AMR and distant supervision. SLING integrates multiple relation linking approaches that capture complementary signals such as linguistic cues, rich semantic representation, and information from the knowledgebase. experiments on relation linking using three KBQA datasets; QALD-7, QALD-9, and LC-QuAD 1.0 demonstrate that the proposed approach achieves state-of-the-art performance on all benchmarks.
  arXiv  Detail & Related papers  (2020-09-16T14:56:11Z)
• An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis [73.7488524683061]
  We propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. Our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
  arXiv  Detail & Related papers  (2020-04-04T13:49:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.