Kunal Goyal, Utkarsh Gupta, A. De, Soumen Chakrabarti
{"title":"Deep Neural Matching Models for Graph Retrieval","authors":"Kunal Goyal, Utkarsh Gupta, A. De, Soumen Chakrabarti","doi":"10.1145/3397271.3401216","DOIUrl":null,"url":null,"abstract":"Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3397271.3401216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.