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Computing Entity Semantic Similarity by Features Ranking [article]

Livia Ruback, Claudio Lucchese, Alexander Arturo Mera Caraballo, Grettel Monteagudo García, Marco Antonio Casanova, Chiara Renso
2018 Zenodo  
The similarity between two entities is then esti- mated by comparing their ranked lists of features.  ...  The experiments demonstrate that entity similarity, computed using ranked lists of features, achieves better accuracy than state-of-the-art measures.  ...  The semantic similarity between the two entities was then estimated by comparing their ranked lists of features.  ... 
doi:10.5281/zenodo.3697746 fatcat:lgdgcumcq5dp3gpsffaqoaga5i

ADSS: An approach to determining semantic similarity

Lixin Han, Linping Sun, Guihai Chen, Li Xie
2006 Advances in Engineering Software  
This approach takes into consideration the similarity between two entities and their similarity reflected in context.  ...  Determining the semantic similarity is an important issue in the development of semantic search technology. In this paper, we propose an approach to determining the semantic similarity.  ...  Semantic ranking provided by semantic search engines is harder than the ranking approach provided by a traditional search engine.  ... 
doi:10.1016/j.advengsoft.2005.05.003 fatcat:7kkrlnm5lbgw3d5f3iayxi657u

Ad Hoc Table Retrieval using Semantic Similarity

Shuo Zhang, Krisztian Balog
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model.  ...  We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables.  ...  We compute query-table similarity using all possible combinations of semantic representations and similarity measures, and use the resulting semantic similarity scores as features in a learning-to-rank  ... 
doi:10.1145/3178876.3186067 dblp:conf/www/ZhangB18 fatcat:kioetyupufd3xmb2gq6iqaf3wm

Determining semantic similarity among entity classes from different ontologies

M.A. Rodriguez, M.J. Egenhofer
2003 IEEE Transactions on Knowledge and Data Engineering  
A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and  ...  Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology.  ...  The feature-matching approach uses common and different characteristics between objects or entities to compute semantic similarity.  ... 
doi:10.1109/tkde.2003.1185844 fatcat:qbzrdk4kezecdcprn2qjmm2vja

Gleaning Types for Literals in RDF Triples with Application to Entity Summarization [chapter]

Kalpa Gunaratna, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
2016 Lecture Notes in Computer Science  
We show the usefulness of generated types by utilizing them to group facts on the basis of their semantics in computing diversified entity summaries by extending a state-of-the-art summarization algorithm  ...  In fact, many datatype properties can be analyzed to suggest types selected from a schema similar to object properties, enabling their wider use in applications.  ...  Grouping Datatype Property Features: Grouping of features can be done at two levels: exact/ syntactic similarity and semantic/abstract similarity.  ... 
doi:10.1007/978-3-319-34129-3_6 fatcat:lartcyze4bev5eprshyoab234e


Xueran Han, Jun Chen, Jiaheng Lu, Yueguo Chen, Xiaoyong Du
2019 Proceedings of the VLDB Endowment  
The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers.  ...  In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users  ...  In addition to the investigation process, as a by-product of entity set expansion, the ranked semantic features in the y-axis, provide pointers to other entity types so that a user can apply the browse  ... 
doi:10.14778/3352063.3352111 fatcat:omifatgypnchpbx76sjdsl7nma

Relatedness-based Multi-Entity Summarization

Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse.  ...  We employ a constrained knapsack problem solving approach to efficiently compute entity summaries.  ...  Acknowledgments Research reported in this publication was supported in part by NIMH of the National Institutes of Health (NIH) under award number R01MH105384-01A1.  ... 
doi:10.24963/ijcai.2017/147 pmid:29051696 pmcid:PMC5644492 dblp:conf/ijcai/GunaratnaYTSC17 fatcat:r52oyg3najewbhxiv7322oywle

Ranking Entity Based on Both of Word Frequency and Word Sematic Features [article]

Xiao-Bo Jin and Guang-Gang Geng and Kaizhu Huang and Zhi-Wei Yan
2016 arXiv   pre-print
In this paper, we propose a series of similarity features based on both of the word frequency features and the word semantic features and describe our ranking architecture and experiment details.  ...  Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search.  ...  relevance features as the input instead of directly computing their similarity.  ... 
arXiv:1608.01068v1 fatcat:chuvphnsvbfonc6wstictwaonq

Improving Retrieval Experience Exploiting Semantic Representation of Documents

Pierpaolo Basile, Annalina Caputo, Anna Lisa Gentile, Marco Degemmis, Pasquale Lops, Giovanni Semeraro
2008 Semantic Web Applications and Perspectives  
Relevance computation is primarily driven by a basic string-matching operation.  ...  This paper presents SENSE (SEmantic N-levels Search Engine), an IR system that tries to overcome the limitations of the ranked keyword approach, by introducing semantic levels which integrate (and not  ...  While the Text Operations component provides the features corresponding to the different levels, the N-Levels Indexer computes the local scoring functions defined for assigning weights to features.  ... 
dblp:conf/swap/BasileCGDLS08 fatcat:rqbo5egy75fpvi7n3emiac3agi

Weasel: a Machine Learning Based Approach to Entity Linking combining different features

Felix Tristram, Sebastian Walter, Philipp Cimiano, Christina Unger
2015 International Semantic Web Conference  
The task of entity linking consists in disambiguating named entities occurring in textual data by linking them to an identifier in a knowledge base that represents the real-world entity they denote.  ...  We present Weasel, a novel approach that is based on a combination of different features that is trained using a Support Vector Machine.  ...  Acknowledgment 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).  ... 
dblp:conf/semweb/TristramWCU15 fatcat:badx3ge5xndz3k42lugwnqgrq4

Entity-based Semantic Association Ranking on the Semantic Web

S. Narayana, S. Sivaleela, A. Govardhan, G. P. S. Varma
2013 International Journal of Computer Applications  
User interest is captured by selecting one or more entities from the user interface. The effectiveness of the ranking method is demonstrated using Spearman Foot rule coefficient.  ...  This paper proposes an approach to discover and rank Semantic Associations between two entities based on the user interest.  ...  Ranking Semantic Associations In the second level, the associations are further ranked based on the entities selected by the user.  ... 
doi:10.5120/12091-8339 fatcat:s6knwwogs5cd5pm5bo5uzebumy

BUPTTeam Participation at TAC 2016 Knowledge Base Population

Yongmei Tan, Xiaoguang Li, Di Zheng
2016 Text Analysis Conference  
The Entity Discovery and Linking (EDL) track at NIST TAC-KBP2016 aims to extract named entity mentions from a source collection of textual documents in multiple languages (English, Chinese and Spanish)  ...  The system consists of six components: 1) preprocessing; 2) mention recognition; 3) mention expansion; 4) candidates generation; 5) candidates ranking; 6) clustering.  ...  With the initial vector 𝒔 , the semantic feature of 𝑚 can be computed by using a random walk with restart in the graph. 4) Semantic Relatedness Let 𝑆𝐹(𝑒 𝑖 ) be the semantic feature of entity 𝑒  ... 
dblp:conf/tac/TanLZ16 fatcat:57ujx4wp2vhm7jbe7q66ybxms4

Entity Recognition and Linking on Tweets with Random Walks

Zhaochen Guo, Denilson Barbosa
2015 Workshop on Making Sense of Microposts  
For this task, we developed a method based on a state-of-the-art entity linking system -REL-RW [2], which exploits the entity graph from the knowledge base to compute semantic relatedness between entities  ...  , and use it for entity disambiguation.  ...  For the mention disambiguation, we will explore supervised approaches such as learning to rank to combine the semantic features such as the semantic similarity and lexical features specific to tweets.  ... 
dblp:conf/msm/GuoB15 fatcat:oyryvgebqvcork2nli73gvxmmi

Entity Identification on the Semantic Web

Alexis Morris, Yannis Velegrakis, Paolo Bouquet
2008 Semantic Web Applications and Perspectives  
In this work we survey a number of entity disambiguation and identification techniques and tools that can be used in semantic web applications and more specifically, into an entity management system for  ...  the semantic web.  ...  Acknowledgements: This work has been partially funded by the EU grant ICT-215032.  ... 
dblp:conf/swap/MorrisVB08 fatcat:na3x4ec2qrd2lj6s5amjszaznq

Understanding Relations using Concepts and Semantics

Jouyon Park, Hyunsouk Cho, Seung-won Hwang
2017 Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets - DSMM'17  
such as word2vec (semantic feature).  ...  We overcome each challenge by considering 1) the concepts of words from knowledge bases (Probase) in addition to the words themselves (conceptual feature) and 2) word semantics from distributed representations  ...  Given this concept vector to represent an entity, we can overcome data sparseness, by computing the similarity of words, as the similarity of concept distributions. is enables to compute the word similarity  ... 
doi:10.1145/3077240.3077250 dblp:conf/sigmod/ParkCH17 fatcat:cofxmvr6cbd6nkp6zzi5cbnfim
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