KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting

• URL: http://arxiv.org/abs/2302.10454v2
• Date: Wed, 22 Feb 2023 21:19:54 GMT
• Title: KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
• Authors: Jinglun Cai, Mingda Li, Ziyan Jiang, Eunah Cho, Zheng Chen, Yang Liu, Xing Fan, Chenlei Guo
• Abstract summary: In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting. To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information. Experimental results show that our approach yields a clear performance gain over two baselines.
• Score: 15.243664083941287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions. When there is an entity error, it imposes extra challenges for a dialogue system to produce satisfactory responses. In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting, an entity correction system with corrupt entity span detection and entity retrieval/re-ranking functionalities. To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information (neighboring entities encoded by graph neural networks) and textual information (KG entity descriptions encoded by RoBERTa). Experimental results show that our approach yields a clear performance gain over two baselines: utterance level QR and entity correction without utilizing KG information. The proposed system is particularly effective for few-shot learning cases where target entities are rarely seen in training or there is a KG relation between the target entity and other contextual entities in the query.

Related papers

• Context Graph [8.02985792541121]
  We present a context graph reasoning textbfCGR$3$ paradigm that leverages large language models (LLMs) to retrieve candidate entities and related contexts. Our experimental results demonstrate that CGR$3$ significantly improves performance on KG completion (KGC) and KG question answering (KGQA) tasks.
  arXiv  Detail & Related papers  (2024-06-17T02:59:19Z)
• Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models [95.31941227776711]
  We propose MPIKGC to compensate for the deficiency of contextualized knowledge and improve KGC by querying large language models (LLMs) We conducted extensive evaluation of our framework based on four description-based KGC models and four datasets, for both link prediction and triplet classification tasks.
  arXiv  Detail & Related papers  (2024-03-04T12:16:15Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Query-Specific Knowledge Graphs for Complex Finance Topics [6.599344783327053]
  We focus on the CODEC dataset, where domain experts create challenging questions. We show that state-of-the-art ranking systems have headroom for improvement. We demonstrate that entity and document relevance are positively correlated.
  arXiv  Detail & Related papers  (2022-11-08T10:21:13Z)
• Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network [111.67744771462873]
  This paper proposes a novel explainable model for sparse Knowledge Graphs (KGs) It combines high-order reasoning into a graph convolutional network, namely HoGRN. It can not only improve the generalization ability to mitigate the information insufficiency issue but also provide interpretability.
  arXiv  Detail & Related papers  (2022-07-14T10:16:56Z)
• Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information [42.88448098873448]
  Conversational recommendation systems (CRS) engage with users by inferring user preferences from dialog history. Previous CRSs use knowledge graph (KG) based recommendation modules and integrate KG with language models for response generation. We propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder.
  arXiv  Detail & Related papers  (2021-12-15T14:12:48Z)
• Adaptive Attentional Network for Few-Shot Knowledge Graph Completion [16.722373937828117]
  Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.
  arXiv  Detail & Related papers  (2020-10-19T16:27:48Z)
• Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction [80.38130122127882]
  We introduce 14 probing tasks targeting linguistic properties relevant to neural relation extraction (RE) We use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance.
  arXiv  Detail & Related papers  (2020-04-17T09:17:40Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.