First Workshop on Knowledge Base Construction, Mining and Reasoning

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI:10.1145/3159652.3160596

Xiang Ren, Craig A. Knoblock, W. Wang, Yu Su

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引用次数: 1

Abstract

1. Motivation and Goals. e success of data mining and search technologies is largely aributed to the ecient and eective analysis of structured data. e construction of a well-structured, machine-actionable database from raw data sources is oen the premise of consequent applications. Meanwhile, the ability of mining and reasoning over such constructed databases is at the core of powering various downstream applications on web and mobile devices. Recently, we have witnessed a signicant amount of interests in building large-scale knowledge bases (KBs) from massive, unstructured data sources (e.g., Wikipedia-based methods such as DBpedia [9], YAGO [19], Wikidata [22], automated systems like Snowball [1], KnowItAll [5], NELL [4] and DeepDive [15], and opendomain approaches like Open IE [2] and Universal Schema [14]); as well as mining and reasoning over such knowledge bases to empower a wide variety of intelligent services, including question answering [6], recommender systems [3] and semantic search [8]. Automated construction, mining and reasoning of the knowledge bases have become possible as research advances in many related areas such as information extraction, natural language processing, data mining, search, machine learning, databases and data integration. However, there are still substantial scientic and engineering challenges in advancing and integrating such relevant methodologies. e goal of this proposed workshop is to gather together leading experts from industry and academia to share their visions about the eld, discuss latest research results, and exchange exciting ideas. With a focus on invited talks and position papers, the workshop aims to provide a vivid forum of discussion about knowledge base-related research. 2. Relevance to WSDM. Knowledge base construction, mining and reasoning is closely related to a wide variety of applications in WSDM, including web search, question answering, and recommender systems. Building a high-quality knowledge base from

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第一届知识库构建、挖掘和推理研讨会

1. 动机和目标。数据挖掘和搜索技术的成功在很大程度上归功于对结构化数据的高效和有效分析。从原始数据源构建结构良好、机器可操作的数据库是后续应用程序的前提。同时，对这些构建数据库的挖掘和推理能力是支持web和移动设备上的各种下游应用程序的核心。最近，我们见证了从大量非结构化数据源(例如，基于维基百科的方法，如DBpedia [9]， YAGO [19]， Wikidata[22]，自动化系统，如Snowball [1]， KnowItAll [5]， NELL[4]和DeepDive[15]，以及开放领域方法，如Open IE[2]和Universal Schema[14])中构建大规模知识库(KBs)的显著数量;以及对这些知识库的挖掘和推理，以授权各种各样的智能服务，包括问答[6]，推荐系统[3]和语义搜索[8]。随着信息提取、自然语言处理、数据挖掘、搜索、机器学习、数据库和数据集成等相关领域的研究进展，知识库的自动化构建、挖掘和推理已经成为可能。然而，在推进和整合这些相关方法方面，仍然存在大量的科学和工程挑战。本次研讨会的目标是聚集来自工业界和学术界的领先专家，分享他们对该领域的看法，讨论最新的研究成果，并交流令人兴奋的想法。研讨会的重点是邀请演讲和立场文件，旨在为知识库相关研究提供一个生动的讨论论坛。2. 与WSDM的相关性。知识库的构建、挖掘和推理与WSDM中的各种应用程序密切相关，包括web搜索、问题回答和推荐系统。建立一个高质量的知识库

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Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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