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Entity disambiguation in anonymized graphs using graph kernels

Linus Hermansson, Tommi Kerola, Fredrik Johansson, Vinay Jethava, Devdatt Dubhashi
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In recent years, the explosion of available online information has brought forth new data mining applications into the spotlight, such as automated querying about realworld entities.  ...  Both individuals work in overlapping fields, and deciding whom is referred to could be a difficult task, even when considering context.  ...  A word of thanks goes to Devdatt Dubhashi for providing helpful comments and curiosity in the work.  ... 
doi:10.1145/2505515.2505565 dblp:conf/cikm/HermanssonKJJD13 fatcat:vazawm2ix5b5zbw2hvkzdvzcju

Name Disambiguation from link data in a collaboration graph using temporal and topological features [article]

Baichuan Zhang, Tanay Kumar Saha, Mohammad Al Hasan
2016 arXiv   pre-print
Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network.  ...  In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network.  ...  Their kernels use only the graph topology, such as, graphlet counts and shortest paths, so they can be used in an anonymized network for entity disambiguation.  ... 
arXiv:1406.5162v3 fatcat:7e7ybiuf6vd7zdu57w2bakff7q

Name disambiguation from link data in a collaboration graph using temporal and topological features

Tanay Kumar Saha, Baichuan Zhang, Mohammad Al Hasan
2015 Social Network Analysis and Mining  
Our method is non-intrusive of privacy as it uses only the time-stamped graph topology of an anonymized network.  ...  In this work, we propose a method for solving entity disambiguation task from link information obtained from a collaboration network.  ...  We extend the journal version by adding centrality based graph topological features in methodology section. See Section 4.5 for more details. 2.  ... 
doi:10.1007/s13278-015-0249-1 fatcat:xiebconhknerbnpauf7p3mskmy

Name Disambiguation in Anonymized Graphs using Network Embedding [article]

Baichuan Zhang, Mohammad Al Hasan
2017 arXiv   pre-print
Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs.  ...  In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical  ...  Graphlet based graph kernel methods (GL3, GL4) are existing state-of-the-art approaches for name disambigua- tion in anonymized graphs.  ... 
arXiv:1702.02287v4 fatcat:wzzuhqlrvbaine5ajd3uhm2c5q

Name Disambiguation in Anonymized Graphs using Network Embedding

Baichuan Zhang, Mohammad Al Hasan
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Our proposed method is non-intrusive of privacy because instead of using a ributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs.  ...  In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical  ...  Graphlet based graph kernel methods (GL3, GL4) are existing state-of-the-art approaches for name disambigua- tion in anonymized graphs.  ... 
doi:10.1145/3132847.3132873 dblp:conf/cikm/ZhangH17 fatcat:qzft4eecobhrjhjvh7sijcfqiu

Privacy Preserving Multiview Point Based BAT Clustering Algorithm and Graph Kernel Method for Data Disambiguation on Horizontally Partitioned Data

J. Anitha, R. Rangarajan
2015 Research Journal of Applied Sciences Engineering and Technology  
Prior to solving that data disambiguation problem using Ramon-Gartner subtree graph kernel (RGSGK), the weight values are assigned for determining the kernel value for disambiguated data.  ...  In case of the clustering approaches based on multi-view point clustering, multiple sensitive attributes in a tuple has to be considered which remains inattentive.  ...  Ramon-Gartner subtree graph kernel(RGSGK) method is used for solving the data disambiguation problems.  ... 
doi:10.19026/rjaset.10.2473 fatcat:wbzcax2dwzhudi4nbryucvizcy

Unsupervised Techniques for Extracting and Clustering Complex Events in News

Delia Rusu, James Hodson, Anthony Kimball
2014 Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation  
In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases.  ...  These concepts are further used as features in a clustering application. Two evaluation settings highlight the advantages and shortcomings of the proposed approach.  ...  Acknowledgments We would like to thank Pierre Brunelle and Konstantine Arkoudas as well as the anonymous reviewers for their helpful comments.  ... 
doi:10.3115/v1/w14-2905 dblp:conf/aclevents/RusuHK14 fatcat:xrfsdk4jqjfmbcovkvezkbvfcm

JEDI: Joint Entity and Relation Detection using Type Inference

Johannes Kirschnick, Holmer Hemsen, Volker Markl
2016 Proceedings of ACL-2016 System Demonstrations  
An innovative method for constraint solving on entity types of multiple relations is used to disambiguate pattern.  ...  The high precision in the evaluation supports our claim that we can detect entities and relations together, alleviating the need to train a custom classifier for an entity type 1 .  ...  Acknowledgments We would like to thank the anonymous reviewers for their helpful comments.  ... 
doi:10.18653/v1/p16-4011 dblp:conf/acl/KirschnickHM16 fatcat:nefavzsmjjclfbkbfddscv2k2i

Predicting Quality of Crowdsourced Annotations Using Graph Kernels [chapter]

Archana Nottamkandath, Jasper Oosterman, Davide Ceolin, Gerben Klaas Dirk de Vries, Wan Fokkink
2015 IFIP Advances in Information and Communication Technology  
Machine learning using graph kernels is an effective technique to use structural information in datasets to make predictions.  ...  We employ the Weisfeiler-Lehman graph kernel for RDF to make predictions about the quality of crowdsourced annotations in Steve.museum dataset, which is modelled and enriched as RDF.  ...  In this paper we will use the Weisfeiler-Lehman [22] graph kernel for RDF (WLRDF), introduced in [6] .  ... 
doi:10.1007/978-3-319-18491-3_10 fatcat:bj56gvkkd5fzxodqo2qdf6cs74

LHD 2.0: A Text Mining Approach to Typing Entities in Knowledge Graphs

Tomas Kliegr, Onddej Zamazal
2016 Social Science Research Network  
The type of the entity being described is one of the key pieces of information in linked data knowledge graphs.  ...  For evaluation we created a gold-standard dataset covering over 2,000 DBpedia entities using a commercial crowdsourcing service.  ...  Acknowledgements The authors wish to thank the three anonymous reviewers for their very helpful comments.  ... 
doi:10.2139/ssrn.3199238 fatcat:mmw5m2b55veknc5kaspfmpam5m

Abstract Meaning Representation for Paraphrase Detection

Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances  ...  Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F 1 measure.  ...  Acknowledgments The authors would like to thank the three anonymous reviewers for their helpful comments.  ... 
doi:10.18653/v1/n18-1041 dblp:conf/naacl/IssaDCYC18 fatcat:dhelema64faxpb66beequnn7lq

NLP Research and Resources at DaSciM, Ecole Polytechnique [article]

Hadi Abdine, Yanzhu Guo, Moussa Kamal Eddine, Giannis Nikolentzos, Stamatis Outsios, Guokan Shang, Christos Xypolopoulos, Michalis Vazirgiannis
2021 arXiv   pre-print
DaSciM (Data Science and Mining) part of LIX at Ecole Polytechnique, established in 2013 and since then producing research results in the area of large scale data analysis via methods of machine and deep  ...  The group has been specifically active in the area of NLP and text mining with interesting results at methodological and resources level.  ...  Graph based representations for NLP and Text Mining In recent years, graphs have become a widely used tool for modeling structured data.  ... 
arXiv:2112.00566v1 fatcat:dcmwpwdwc5emti6jcflqq5ib4a

DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants [article]

Deepak Muralidharan, Joel Ruben Antony Moniz, Weicheng Zhang, Stephen Pulman, Lin Li, Megan Barnes, Jingjing Pan, Jason Williams, Alex Acero
2021 arXiv   pre-print
In applications, entity labels may change frequently, and non-textual properties like topicality or popularity may be needed to choose among alternatives.  ...  Named entity recognition (NER) is usually developed and tested on text from well-written sources.  ...  In the CNN layer used to capture contextual similarity, we use 32 convolutional kernels of width 7.  ... 
arXiv:2108.06633v1 fatcat:2swvlqrqzjhmblsbrv7v6fo76m

Learning Graph Embeddings from

Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, Alexander Panchenko
2019 Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*  
Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNetbased similarity measures, show that our approach yields competitive results, outperforming  ...  The model learns representations for nodes in a dense space that approximate a given userdefined graph distance measure, such as e.g. the shortest path distance or distance measures that take information  ...  We thank three anonymous reviewers for their most useful feedback. Last but not least, we are grateful to Sarah Kohail who helped with computing the first version of the node2vec baselines.  ... 
doi:10.18653/v1/s19-1014 dblp:conf/starsem/KutuzovDOBP19 fatcat:uzgozi2dnbhyjgasx2i4256fge

Learning Graph Embeddings from WordNet-based Similarity Measures [article]

Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, Alexander Panchenko
2019 arXiv   pre-print
Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming  ...  The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information  ...  We thank three anonymous reviewers for their most useful feedback. Last but not least, we are grateful to Sarah Kohail who helped with computing the first version of the node2vec baselines.  ... 
arXiv:1808.05611v4 fatcat:mrb36kdyabapzgnl4r3wtmcmt4
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