{"title":"利用图神经网络和相关性评分提高基于知识图谱的问题解答系统性能的新技术","authors":"Sincy V. Thambi, P. C. Reghu Raj","doi":"10.1007/s10844-023-00839-4","DOIUrl":null,"url":null,"abstract":"<p>A Knowledge Graph-based Question Answering (KGQA) system attempts to answer a given natural language question using a knowledge graph (KG) rather than from text data. The current KGQA methods attempt to determine whether there is an explicit relationship between the entities in the question and a well-structured relationship between them in the KG. However, such strategies are difficult to build and train, limiting their consistency and versatility. The use of language models such as BERT has aided in the advancement of natural language question answering. In this paper, we present a novel Graph Neural Network(GNN) based approach with relevance scoring for improving KGQA. GNNs use the weight of nodes and edges to influence the information propagation while updating the node features in the network. The suggested method comprises subgraph construction, weighing of nodes and edges, and pruning processes to obtain meaningful answers. BERT-based GNN is used to build subgraph node embeddings. We tested the influence of weighting for both nodes and edges and observed that the system performs better for weighted graphs than unweighted graphs. Additionally, we experimented with several GNN convolutional layers and obtainined improved results by combining GENeralised Graph Convolution (GENConv) with node weights for simple questions. Extensive testing on benchmark datasets confirmed the effectiveness of the proposed model in comparison to state-of-the-art KGQA systems.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"17 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel technique using graph neural networks and relevance scoring to improve the performance of knowledge graph-based question answering systems\",\"authors\":\"Sincy V. Thambi, P. C. Reghu Raj\",\"doi\":\"10.1007/s10844-023-00839-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A Knowledge Graph-based Question Answering (KGQA) system attempts to answer a given natural language question using a knowledge graph (KG) rather than from text data. The current KGQA methods attempt to determine whether there is an explicit relationship between the entities in the question and a well-structured relationship between them in the KG. However, such strategies are difficult to build and train, limiting their consistency and versatility. The use of language models such as BERT has aided in the advancement of natural language question answering. In this paper, we present a novel Graph Neural Network(GNN) based approach with relevance scoring for improving KGQA. GNNs use the weight of nodes and edges to influence the information propagation while updating the node features in the network. The suggested method comprises subgraph construction, weighing of nodes and edges, and pruning processes to obtain meaningful answers. BERT-based GNN is used to build subgraph node embeddings. We tested the influence of weighting for both nodes and edges and observed that the system performs better for weighted graphs than unweighted graphs. Additionally, we experimented with several GNN convolutional layers and obtainined improved results by combining GENeralised Graph Convolution (GENConv) with node weights for simple questions. Extensive testing on benchmark datasets confirmed the effectiveness of the proposed model in comparison to state-of-the-art KGQA systems.</p>\",\"PeriodicalId\":56119,\"journal\":{\"name\":\"Journal of Intelligent Information Systems\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10844-023-00839-4\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-023-00839-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A novel technique using graph neural networks and relevance scoring to improve the performance of knowledge graph-based question answering systems
A Knowledge Graph-based Question Answering (KGQA) system attempts to answer a given natural language question using a knowledge graph (KG) rather than from text data. The current KGQA methods attempt to determine whether there is an explicit relationship between the entities in the question and a well-structured relationship between them in the KG. However, such strategies are difficult to build and train, limiting their consistency and versatility. The use of language models such as BERT has aided in the advancement of natural language question answering. In this paper, we present a novel Graph Neural Network(GNN) based approach with relevance scoring for improving KGQA. GNNs use the weight of nodes and edges to influence the information propagation while updating the node features in the network. The suggested method comprises subgraph construction, weighing of nodes and edges, and pruning processes to obtain meaningful answers. BERT-based GNN is used to build subgraph node embeddings. We tested the influence of weighting for both nodes and edges and observed that the system performs better for weighted graphs than unweighted graphs. Additionally, we experimented with several GNN convolutional layers and obtainined improved results by combining GENeralised Graph Convolution (GENConv) with node weights for simple questions. Extensive testing on benchmark datasets confirmed the effectiveness of the proposed model in comparison to state-of-the-art KGQA systems.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.