{"title":"基于文本和知识库的语义搜索","authors":"H. Bast, Björn Buchhold, Elmar Haussmann","doi":"10.1561/1500000032","DOIUrl":null,"url":null,"abstract":"This article provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. In a nutshell, semantic search is \"search with meaning\". This \"meaning\" can refer to various parts of the search process: understanding the query instead of just finding matches of its components in the data, understanding the data instead of just searching it for such matches, or representing knowledge in a way suitable for meaningful retrieval.Semantic search is studied in a variety of different communities with a variety of different views of the problem. In this survey, we classify this work according to two dimensions: the type of data text, knowledge bases, combinations of these and the kind of search keyword, structured, natural language. We consider all nine combinations. The focus is on fundamental techniques, concrete systems, and benchmarks. The survey also considers advanced issues: ranking, indexing, ontology matching and merging, and inference. It also provides a succinct overview of fundamental natural language processing techniques: POS-tagging, named-entity recognition and disambiguation, sentence parsing, and distributional semantics.The survey is as self-contained as possible, and should thus also serve as a good tutorial for newcomers to this fascinating and highly topical field.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"94 1","pages":"119-271"},"PeriodicalIF":8.3000,"publicationDate":"2016-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"149","resultStr":"{\"title\":\"Semantic Search on Text and Knowledge Bases\",\"authors\":\"H. Bast, Björn Buchhold, Elmar Haussmann\",\"doi\":\"10.1561/1500000032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. In a nutshell, semantic search is \\\"search with meaning\\\". This \\\"meaning\\\" can refer to various parts of the search process: understanding the query instead of just finding matches of its components in the data, understanding the data instead of just searching it for such matches, or representing knowledge in a way suitable for meaningful retrieval.Semantic search is studied in a variety of different communities with a variety of different views of the problem. In this survey, we classify this work according to two dimensions: the type of data text, knowledge bases, combinations of these and the kind of search keyword, structured, natural language. We consider all nine combinations. The focus is on fundamental techniques, concrete systems, and benchmarks. The survey also considers advanced issues: ranking, indexing, ontology matching and merging, and inference. It also provides a succinct overview of fundamental natural language processing techniques: POS-tagging, named-entity recognition and disambiguation, sentence parsing, and distributional semantics.The survey is as self-contained as possible, and should thus also serve as a good tutorial for newcomers to this fascinating and highly topical field.\",\"PeriodicalId\":48829,\"journal\":{\"name\":\"Foundations and Trends in Information Retrieval\",\"volume\":\"94 1\",\"pages\":\"119-271\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2016-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"149\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Foundations and Trends in Information Retrieval\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1561/1500000032\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Information Retrieval","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1561/1500000032","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
This article provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. In a nutshell, semantic search is "search with meaning". This "meaning" can refer to various parts of the search process: understanding the query instead of just finding matches of its components in the data, understanding the data instead of just searching it for such matches, or representing knowledge in a way suitable for meaningful retrieval.Semantic search is studied in a variety of different communities with a variety of different views of the problem. In this survey, we classify this work according to two dimensions: the type of data text, knowledge bases, combinations of these and the kind of search keyword, structured, natural language. We consider all nine combinations. The focus is on fundamental techniques, concrete systems, and benchmarks. The survey also considers advanced issues: ranking, indexing, ontology matching and merging, and inference. It also provides a succinct overview of fundamental natural language processing techniques: POS-tagging, named-entity recognition and disambiguation, sentence parsing, and distributional semantics.The survey is as self-contained as possible, and should thus also serve as a good tutorial for newcomers to this fascinating and highly topical field.
期刊介绍:
The surge in research across all domains in the past decade has resulted in a plethora of new publications, causing an exponential growth in published research. Navigating through this extensive literature and staying current has become a time-consuming challenge. While electronic publishing provides instant access to more articles than ever, discerning the essential ones for a comprehensive understanding of any topic remains an issue. To tackle this, Foundations and Trends® in Information Retrieval - FnTIR - addresses the problem by publishing high-quality survey and tutorial monographs in the field.
Each issue of Foundations and Trends® in Information Retrieval - FnT IR features a 50-100 page monograph authored by research leaders, covering tutorial subjects, research retrospectives, and survey papers that provide state-of-the-art reviews within the scope of the journal.