整理：AI壹号堂

知识图谱以结构化的方式描述客观世界中的概念、实体及其间的关系，将万维网的信息表达成更接近人类认知世界的形式，提供了一种更好地组织、管理和理解万维网上海量信息的能力。知识图谱核心技术除了大家熟悉的实体识别与关系抽取外，知识融合、本体匹配、及实体对齐更加具有挑战性，本篇文章推荐了本体匹配、实体对齐、及知识融合相关论文，希望能对入门者有一定帮助。前方高能预警哦

背景描述：

    知识图谱以结构化的方式描述客观世界中的概念、实体及其间的关系，将万维网的信息表达成更接近人类认知世界的形式，提供了一种更好地组织、管理和理解万维网上海量信息的能力。知识图谱可以由任何机构和个人自由构建，其背后的数据来源广泛、质量参差不齐，导致它们之间存在多样性和异构性。例如，对于相似领域，通常会存在多个不同的概念或实体指称真实世界中的相同事物。知识图谱核心技术除了大家熟悉的实体识别与关系抽取外，知识融合、本体匹配、及实体对齐更加具有挑战性，本篇文章推荐了本体匹配、实体对齐、及知识融合相关论文，希望能对入门者有一定帮助，其中阅读顺序可以按文中推荐阅读即可，其中红色标注的是强烈建议大家阅读的，其他的可以根据时间进行安排。

本体匹配

• Anchor-PROMPT: Using non-local context for semantic matching. IJCAI, 2001

• Learning to map between ontologies on the semantic web. WWW, 2002

• Similarity flooding: a versatile graph matching algorithm and its application to schema matching. ICDE, 2002

• Constructing virtual documents for ontology matching. WWW, 2006

• Using Google distance to weight approximate ontology matches. WWW, 2007

• A survey of exploiting WordNet in ontology matching. IFIP AI, 2008

• Falcon-AO: A practical ontology matching system. JWS, 2008

• Matching large ontologies: A divide-and-conquer approach. DKE, 2008

• RiMOM: A dynamic multistrategy ontology alignment framework. TKDE, 2009

• LogMap: Logic-based and scalable ontology matching. ISWC, 2011

• An extensible linear approach for holistic ontology matching. ISWC, 2016

• Cross-lingual infobox alignment in Wikipedia using entity-attribute factor graph. ISWC, 2017

实体对齐

• Entity resolution with Markov logic. ICDM, 2006

• A self-training approach for resolving object coreference on the semantic web. WWW, 2011

• Data matching. Principles of data integration, 2012

• PARIS: Probabilistic alignment of relations, instances, and schema. VLDB, 2012

• SiGMa: Simple greedy matching for aligning large knowledge bases. KDD, 2013

• Translating embeddings for modeling multi-relational data. NIPS, 2013

• Progressive approach to relational entity resolution. VLDB, 2014

• Cross-lingual entity alignment via joint knowledge graph embedding. ISWC, 2017

• Falcon: Scaling up hands-off crowdsourced entity matching to build cloud services. SIGMOD, 2017

• Knowledge graph embedding: A survey of approaches and applications. TKDE, 2017

• Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. IJCAI, 2017

• Bootstrapping entity alignment with knowledge graph embedding. IJCAI, 2018

• Cross-lingual knowledge graph alignment via graph convolutional networks. EMNLP, 2018

• Deep learning for entity matching: A design space exploration. SIGMOD, 2018

• Entity alignment between knowledge graph using attribute embeddings. AAAI, 2018

• Learning to exploit long-term relational dependencies in knowledge graphs. ICML, 2019

• Multi-channel graph neural network for entity alignment. ACL, 2019

• Multi-view knowledge graph embedding for entity alignment. IJCAI, 2019

• Relation-aware entity alignment for heterogeneous knowledge graphs. IJCAI, 2019

知识融合

• Truth discovery with multiple conflicting information providers on the web. TKDE, 2008

• Integrating conflicting data: The role of source dependence. VLDB, 2009

• A Bayesian approach to discovering truth from conflicting sources for data integration. VLDB, 2012

• Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. SIGMOD, 2014

• SLiMFast: Guaranteed results for data fusion and source reliability. SIGMOD, 2017

• Fact checking: Theory and practice. KDD, 2018