Knowledge Fusion: Introduction of Concepts and Techniques

The Internet contains a huge amount of data information, relying only on human resources can no longer obtain the complete and useful information from it. The research of knowledge graph provides a means to obtain large amounts of knowledge from massive texts and images, which plays an important role in people's daily life, such as Q&A, search, recommendation. The effective organization and integration of knowledge obtained from different sources into a knowledge base is the researching difficulty and hot spot to be solved in the knowledge graph construction in currently.

[1]  Peter Christen,et al.  A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication , 2012, IEEE Transactions on Knowledge and Data Engineering.

[2]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[3]  Wei Hu,et al.  Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding , 2017, SEMWEB.

[4]  Wei Shen,et al.  Linking named entities in Tweets with knowledge base via user interest modeling , 2013, KDD.

[5]  Yixin Zhong,et al.  A Probabilistic Feature Based Maximum Entropy Model for Chinese Named Entity Recognition , 2006, ICCPOL.

[6]  Eric Nichols,et al.  Named Entity Recognition with Bidirectional LSTM-CNNs , 2015, TACL.

[7]  Jiawei Han,et al.  Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions , 2015, IEEE Transactions on Knowledge and Data Engineering.

[8]  Vasudeva Varma,et al.  IIIT Hyderabad in Guided Summarization and Knowledge Base Population , 2010, TAC.

[9]  Yu Luo,et al.  A Joint Decoding Algorithm for Named Entity Recognition , 2018, 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC).

[10]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[11]  吴天星,et al.  Cross-Lingual Taxonomy Alignment with Bilingual Biterm Topic Model , 2016 .

[12]  Hans-Peter Kriegel,et al.  Extraction of semantic biomedical relations from text using conditional random fields , 2008, BMC Bioinformatics.

[13]  Jiali Wang,et al.  An approach on Chinese microblog entity linking combining baidu encyclopaedia and word2vec , 2017 .

[14]  Wei Zhang,et al.  From Data Fusion to Knowledge Fusion , 2014, Proc. VLDB Endow..

[15]  Surajit Chaudhuri,et al.  A framework for robust discovery of entity synonyms , 2012, KDD.

[16]  Giuseppe Ottaviano,et al.  Fast and Space-Efficient Entity Linking for Queries , 2015, WSDM.

[17]  Guoliang Li,et al.  Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases , 2017, CIKM.

[18]  Chen Zhang,et al.  Chinese Named Entity Recognition Based on Rules and Conditional Random Field , 2018, CSAI '18.

[19]  Jing Jiang,et al.  Linking Entities to a Knowledge Base with Query Expansion , 2011, EMNLP.

[20]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[21]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[22]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[23]  Wei Wu,et al.  Glyce: Glyph-vectors for Chinese Character Representations , 2019, NeurIPS.

[24]  James Hammerton,et al.  Named Entity Recognition with Long Short-Term Memory , 2003, CoNLL.

[25]  Li Yang,et al.  Graph-Based Collective Chinese Entity Linking Algorithm , 2016 .

[26]  Yi Li,et al.  RiMOM: A Dynamic Multistrategy Ontology Alignment Framework , 2009, IEEE Transactions on Knowledge and Data Engineering.

[27]  Rung Ching Chen,et al.  Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques , 2011, Appl. Soft Comput..

[28]  Tom M. Mitchell,et al.  Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.

[29]  Silviu Cucerzan,et al.  TAC Entity Linking by Performing Full-document Entity Extraction and Disambiguation , 2011, TAC.

[30]  Larry P. Heck,et al.  Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation , 2015, ArXiv.

[31]  Hermann Ney,et al.  Maximum Entropy Models for Named Entity Recognition , 2003, CoNLL.

[32]  Carlo Zaniolo,et al.  Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment , 2016, IJCAI.

[33]  Zhiyuan Liu,et al.  Iterative Entity Alignment via Joint Knowledge Embeddings , 2017, IJCAI.

[34]  John F. Sowa,et al.  Principles of semantic networks , 1991 .

[35]  Divesh Srivastava,et al.  Knowledge Curation and Knowledge Fusion: Challenges, Models and Applications , 2015, SIGMOD Conference.

[36]  Zhaochen Guo,et al.  Robust Entity Linking via Random Walks , 2014, CIKM.

[37]  Yuejie Zhang,et al.  Fusion of Multiple Features for Chinese Named Entity Recognition Based on CRF Model , 2008, AIRS.

[38]  Yue Zhang,et al.  Chinese NER Using Lattice LSTM , 2018, ACL.

[39]  Zhou Fang,et al.  Multi-source knowledge fusion algorithm , 2013 .

[40]  Razvan C. Bunescu,et al.  Using Encyclopedic Knowledge for Named entity Disambiguation , 2006, EACL.

[41]  Xianpei Han,et al.  A Generative Entity-Mention Model for Linking Entities with Knowledge Base , 2011, ACL.

[42]  Zhuang Yan,et al.  A Survey on Entity Alignment of Knowledge Base , 2016 .

[43]  Houfeng Wang,et al.  Learning Entity Representation for Entity Disambiguation , 2013, ACL.

[44]  Robert J. Gaizauskas,et al.  Graph Ranking for Collective Named Entity Disambiguation , 2014, ACL.

[45]  Jian Su,et al.  Entity Linking with Effective Acronym Expansion, Instance Selection, and Topic Modeling , 2011, IJCAI.

[46]  Mohammed Maree,et al.  On the Merging of Domain-Specific Heterogeneous Ontologies Using WordNet and Web Pattern-Based Queries , 2011, J. Inf. Knowl. Manag..

[47]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[48]  Yang Li,et al.  Mining evidences for named entity disambiguation , 2013, KDD.

[49]  Victor Guimar Boosting Named Entity Recognition with Neural Character Embeddings , 2015 .

[50]  Jian Su,et al.  Enhancing HMM-based biomedical named entity recognition by studying special phenomena , 2004, J. Biomed. Informatics.

[51]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[52]  Jun Zhao,et al.  Collective entity linking in web text: a graph-based method , 2011, SIGIR.

[53]  Gerhard Weikum,et al.  Robust Disambiguation of Named Entities in Text , 2011, EMNLP.

[54]  Shaojun Zhao,et al.  Named Entity Recognition in Biomedical Texts using an HMM Model , 2004, NLPBA/BioNLP.

[55]  Olivier Teste,et al.  An Extensible Linear Approach for Holistic Ontology Matching , 2016, International Semantic Web Conference.

[56]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[57]  Liu Qi,et al.  BTM Topic Modeling Approach to Named Entity Linking , 2018 .

[58]  Lin Yao,et al.  CRF-based active learning for Chinese named entity recognition , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[59]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.