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Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models [article]

Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu, Abhishek Nadgeri, Johannes Hoffart, Jens Lehmann
2020 arXiv   pre-print
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on the state of the art NED model for the Wikipedia knowledge base.  ...  In this paper, we argue that context derived from a knowledge graph (in our case: Wikidata) provides enough signals to inform pretrained transformer models and improve their performance for named entity  ...  The classifier employs the binary cross-entropy loss. APPROACH Knowledge Graph Context: We use a SPARQL endpoint to fetch triples of the identified entity in the sentence.  ... 
arXiv:2008.05190v1 fatcat:rxudma5sgzhixjqpnljhwnnjau

Improving Neural Entity Disambiguation with Graph Embeddings

Özge Sevgili, Alexander Panchenko, Chris Biemann
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop  
Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base.  ...  Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-ofthe-art neural ED  ...  Acknowledgments We thank the SRW mentor Matt Gardner and anonymous reviewers for their most useful feedback on this work.  ... 
doi:10.18653/v1/p19-2044 dblp:conf/acl/SevgiliPB19 fatcat:ujcgwicd4bajbm4zspibqcjmgy

Entity Disambiguation with Web Links

Andrew Chisholm, Ben Hachey
2015 Transactions of the Association for Computational Linguistics  
Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.  ...  We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia.  ...  Ben Hachey is the recipient of an Australian Research Council Discovery Early Career Researcher Award (DE120102900).  ... 
doi:10.1162/tacl_a_00129 fatcat:gsykvrjypzg4fpsnwdq5uew4ma

Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation [article]

Hongzhao Huang and Larry Heck and Heng Ji
2015 arXiv   pre-print
Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia).  ...  This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling  ...  Experiments In this section, we evaluate the performance of various semantic relatedness methods and their impact on the entity disambiguation task.  ... 
arXiv:1504.07678v1 fatcat:ykfpeyk3ujg7vghjeg6krabe2i

OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology

Christos Makris, Michael Angelos Simos
2020 Big Data and Cognitive Computing  
In the context of a frequently changing online world, we introduce and study the domain of online training named entity disambiguation, featuring on-the-fly adaptation to underlying knowledge changes.  ...  Our novel methodology evaluates polysemous anchor mentions with sense compatibility based on thematic segmentation of the Wikipedia knowledge graph representation.  ...  Evaluation Process The evaluation analysis focused on the entity linking disambiguation process, delineating the benefits of our novel methodology.  ... 
doi:10.3390/bdcc4040031 fatcat:vftpewqo7vfzfai4x37bm2xyym

Text Semantic Annotation: A Distributed Methodology Based on Community Coherence

Christos Makris, Georgios Pispirigos, Michael Angelos Simos
2020 Algorithms  
Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology.  ...  Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a13070160 fatcat:znccdtd5wreprdhjlus7tyso3m

Combining Textual and Graph-Based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models [chapter]

Sherzod Hakimov, Hendrik ter Horst, Soufian Jebbara, Matthias Hartung, Philipp Cimiano
2016 Lecture Notes in Computer Science  
Previous work on this task has proven a strong impact of graph-based methods such as PageRank on entity disambiguation.  ...  We analyze the impact of these features and their combination on named entity disambiguation.  ...  Acknowledgements This work was supported by the Cluster of Excellence Cognitive Interaction Technology 'CITEC' (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG)  ... 
doi:10.1007/978-3-319-49004-5_19 fatcat:jb4uobnxkbdo5kbuizesilh7pu

Robust and Collective Entity Disambiguation through Semantic Embeddings

Stefan Zwicklbauer, Christin Seifert, Michael Granitzer
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
Entity disambiguation is the task of mapping ambiguous terms in natural-language text to its entities in a knowledge base.  ...  Moreover, we discuss the influence of the quality of the knowledge base on the disambiguation accuracy and indicate that our algorithm achieves better results than non-publicly available state-of-the-art  ...  Similar to our approach the model can be directly trained on large-scale knowledge graphs.  ... 
doi:10.1145/2911451.2911535 dblp:conf/sigir/ZwicklbauerSG16 fatcat:dzv7i6i4yzg67l3z6mptnf5abi

Linking chemical and disease entities to ontologies by integrating PageRank with extracted relations from literature

Pedro Ruas, Andre Lamurias, Francisco M. Couto
2020 Journal of Cheminformatics  
Models based on the Personalized PageRank (PPR) algorithm are one of the state-of-the-art approaches, but these have low performance when the disambiguation graphs are sparse.  ...  of Named Entity Linking models.  ...  The model was evaluated on the dataset AIDA and achieved a disambiguation accuracy of 91.7%. Guo et al.  ... 
doi:10.1186/s13321-020-00461-4 pmid:33430995 doaj:a2d677fb86d240ed8d56149359214626 fatcat:q6eppbwmjvaerjxnafcxddia5q

Knowledge-Based Biomedical Word Sense Disambiguation: An Evaluation and Application to Clinical Document Classification

Vijay N. Garla, Cynthia Brandt
2012 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology  
Acknowledgements We are especially thankful to the NLM and others who developed the Word Sense Disambiguation benchmarks.  ...  Funding This work was supported in part by NIH grant T15 LM07056 from the National Library of Medicine, CTSA grant number UL1 RR024139 from the NIH National Center for Advancing Translational Sciences  ...  To determine the impact of each parameter, we modeled disambiguation accuracy as a linear function of these parameters for each WSD dataset.  ... 
doi:10.1109/hisb.2012.12 dblp:conf/hisb/GarlaB12 fatcat:rcxw74xrarchvawo62ezccvofa

Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification

Vijay N Garla, Cynthia Brandt
2013 JAMIA Journal of the American Medical Informatics Association  
Acknowledgements We are especially thankful to the NLM and others who developed the Word Sense Disambiguation benchmarks.  ...  Funding This work was supported in part by NIH grant T15 LM07056 from the National Library of Medicine, CTSA grant number UL1 RR024139 from the NIH National Center for Advancing Translational Sciences  ...  To determine the impact of each parameter, we modeled disambiguation accuracy as a linear function of these parameters for each WSD dataset.  ... 
doi:10.1136/amiajnl-2012-001350 pmid:23077130 pmcid:PMC3756260 fatcat:kc4yft6t3zbfpjj2qxqw2ydu64

Collective Entity Linking on Relational Graph Model with Mentions [chapter]

Jing Gong, Chong Feng, Yong Liu, Ge Shi, Heyan Huang
2017 Lecture Notes in Computer Science  
Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.  ...  Graph based linking algorithm is utilized to ensure per mention with only one candidate entity.  ...  Acknowledgement The research of this paper is partially supported by National 863 project 2015AA015404 and open project of State key lab.  ... 
doi:10.1007/978-3-319-69005-6_14 fatcat:xhb4r2pfjbcd5i5adnbtxvjz5u

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation [chapter]

Ioana Hulpuş, Narumol Prangnawarat, Conor Hayes
2015 Lecture Notes in Computer Science  
As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns.  ...  In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base.  ...  Our extensive evaluation sheds light not only on our measures, but also on the general use of path-based relatedness measures on the used knowledge graphs.  ... 
doi:10.1007/978-3-319-25007-6_26 fatcat:uk76uncc2vczfdrrfmgzrvqwji

Neural Collective Entity Linking [article]

Yixin Cao and Lei Hou and Juanzi Li and Zhiyuan Liu
2018 arXiv   pre-print
To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text.  ...  However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information.  ...  Acknowledgments The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007), and THUNUS NExT Co-Lab.  ... 
arXiv:1811.08603v1 fatcat:jrk2sjc6mva4vhvb62tjfz5e4u

Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Albert Weichselbraun, Philipp Kuntschik, Adrian M.P. Braşoveanu
2018 Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics - WIMS '18  
the impact of the suggested methods on its own entity linking performance.  ...  Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating  ...  Acknowledgments The research presented in this paper has been conducted as part of the DISCOVER Project (, funded by the Swiss Commission for Technology and Innovation (CTI).  ... 
doi:10.1145/3227609.3227670 dblp:conf/wims/WeichselbraunKB18 fatcat:jr2drrfsavepzbzgougsnnw6uq
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