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Computing Entity Semantic Similarity by Features Ranking
[article]
2018
Zenodo
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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
2006
Advances in Engineering Software
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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
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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2018 pre-print arXiv:1802.06159v2 fatcat:2pqtgmbtivaztm3p6z7gx6bcwuOther Versions
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
2003
IEEE Transactions on Knowledge and Data Engineering
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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]
2016
Lecture Notes in Computer Science
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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
PivotE
2019
Proceedings of the VLDB Endowment
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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
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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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]
2016
arXiv
pre-print
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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
2008
Semantic Web Applications and Perspectives
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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
2015
International Semantic Web Conference
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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
2013
International Journal of Computer Applications
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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
2016
Text Analysis Conference
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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
2015
Workshop on Making Sense of Microposts
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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
2008
Semantic Web Applications and Perspectives
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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
2017
Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets - DSMM'17
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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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