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An Efficient Approach For Semantically-Enhanced Document Clustering By Using Wikipedia Link Structure

Iyad AlAgha, Rami Nafee
2014 International Journal of Artificial Intelligence & Applications  
We first map terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of terms is calculated by using the Wikipedia's link structure.  ...  This paper presents a new approach to enhance document clustering by exploiting the semantic knowledge contained in Wikipedia.  ...  Our approach is distinguished over similar approaches in terms of the way we used to efficiently map the document content to Wikipedia concepts and the low-cost measure we adapted to determine semantic  ... 
doi:10.5121/ijaia.2014.5605 fatcat:adhak2f6gbgcnf22wfkflijaom

Semantic Information Retrieval based on Wikipedia Taxonomy

May Sabai Han
2012 International Journal of Computer Applications Technology and Research  
The utility of the proposed system is evaluated by using the ta xonomy of Wikipedia categories.  ...  The proposed method uses Wikipedia as an ontology and spreading activation strategy to compute semantic similarity.  ...  [3] build a network of concepts from Wikipedia documents using a random walk approach to compute distances between documents.  ... 
doi:10.7753/ijcatr0201.1016 fatcat:tbteoangtfgrphg4pmdvz6rdbq

Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis

Evgeniy Gabrilovich, Shaul Markovitch
2007 International Joint Conference on Artificial Intelligence  
We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia.  ...  We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts.  ...  This work was partially supported by funding from the EC-sponsored MUSCLE Network of Excellence.  ... 
dblp:conf/ijcai/GabrilovichM07 fatcat:ke3csg5wtfhxflhzvrxbnfczrq

Towards optimize-ESA for text semantic similarity: A case study of biomedical text

Khaoula Mrhar, Mounia Abik
2020 International Journal of Electrical and Computer Engineering (IJECE)  
Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia.  ...  However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector.  ...  Second layer: Build domain index UiAfter the filtering of the Wikipedia articles related to a specific domain Di, we build an inverted index domain Di which maps each word into a list of concept in which  ... 
doi:10.11591/ijece.v10i3.pp2934-2943 fatcat:3wiiprpkhrh5zfzlvcejiymhzi

Cross Domain Search by Exploiting Wikipedia

Chen Liu, Sai Wu, Shouxu Jiang, Anthony K.H. Tung
2012 2012 IEEE 28th International Conference on Data Engineering  
In this paper, we propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years.  ...  Compared to the tags, the concepts are therefore of higher quality. We develop effective methods for cross-modal search based on the concepts associated with resources.  ...  ACKNOWLEDGMENT The research and system development reported in this paper was supported by Singapore MDA grant R-252-000-376-27.  ... 
doi:10.1109/icde.2012.13 dblp:conf/icde/LiuWJT12 fatcat:qrlwyc2jhjfptbr4lf7sbhjzly

Theme Based Clustering of Tweets

Rudra M. Tripathy, Shashank Sharma, Sachindra Joshi, Sameep Mehta, Amitabha Bagchi
2014 Proceedings of the 1st IKDD Conference on Data Sciences - CoDS '14  
We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering.  ...  We show some of our initial results to demonstrate the effectiveness of our approach.  ...  So it is better to use the concept of the tweets rather than words. One of the solution to this problem is to map each word to a concept by leveraging the Wikipedia as a knowledge based.  ... 
doi:10.1145/2567688.2567694 dblp:conf/cods/TripathySJMB14 fatcat:l3egbby5bbai7ng2fzpt3dmsbi

Visual Reranking for Image Retrieval over the Wikipedia Corpus

Débora Myoupo, Adrian Popescu, Hervé Le Borgne, Pierre-Alain Moëllic
2009 Conference and Labs of the Evaluation Forum  
This paper describes the approach we developed for the WikipediaMM task on 2009 [4] , which builds on our last year contribution.  ...  Our main purpose was to test whether combining textual and content based retrieval improves over purely textual search and the results we report here confirm that combining modalities results in a significant  ...  Conceptual neighbourhood building Wikipedia images are accompanied by brief textual descriptions and query expansion is an appealing way to improve recall and, if performed in a judicious way, to also  ... 
dblp:conf/clef/MyoupoPBM09a fatcat:fdv6yi3d5rgx5my7tku7pwdsuq

MMIS at ImageCLEF 2009: Non-parametric Density Estimation Algorithms

Ainhoa Llorente, Suzanne Little, Stefan M. Rüger
2009 Conference and Labs of the Evaluation Forum  
The second approach uses keyword correlation to compute semantic similarity measures using several knowledge sources.  ...  Evaluation of results is done under two different metrics, one based on ROC curves and the other in a hierarchical measure proposed by the organisers.  ...  This work was partially funded by the EU Pharos project (IST-FP6-45035) and by Santander Corporation.  ... 
dblp:conf/clef/LlorenteLR09 fatcat:2bfvvrxwvje47fwgubjb64xuji

Multimodal Image Retrieval over a Large Database [chapter]

Débora Myoupo, Adrian Popescu, Hervé Le Borgne, Pierre-Alain Moëllic
2010 Lecture Notes in Computer Science  
Textual queries are reformulated using Wikipedia knowledge and results are then reordered using a k-NN based reranking method.  ...  We introduce a new multimodal retrieval technique which combines query reformulation and visual image reranking in order to deal with results sparsity and imprecision, respectively.  ...  Similarly to [4] or [6] our method finds semantically similar concepts from Wikipedia for an input text.  ... 
doi:10.1007/978-3-642-15751-6_20 fatcat:4aowzdwztrbaphhrbgudfyyksy

Constructing Concept Space from Social Collaborative Editing

Cheng-Hung Tsai, Tsun Ku, Liang-Pu Chen, Ping-che Yang
2015 Procedia Manufacturing  
In order to utilize the information from social network, we need a concept space that can alter with application domain.  ...  We describe how this framework in detail and proposed method for each stage, and the metrics in the previous studies and the one we used for evaluation.  ...  Acknowledgements This study is conducted under the Online and Offline integrated Smart Commerce Platform (2/4) of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs  ... 
doi:10.1016/j.promfg.2015.07.676 fatcat:c4kkcnaaqvfqpckugrye7qfwca

Construction of Semantic Metric for Measuring the Distance between Ontology Concepts

Viktor Hryhorovych
2021 International Conference on Computational Linguistics and Intelligent Systems  
To assess the relationship between concepts, a metric for non-taxonomic ontology (for an arbitrary semantic network of concepts) is constructed.  ...  The analysis and reasoning of the offered metric is carried out.  ...  In [21] the systematics from Wikipedia is estimated based on maps of relations of tokens and concepts.  ... 
dblp:conf/colins/Hryhorovych21 fatcat:ppdk5ghg75bntcdqt5zcfbic4y

Harnessing different knowledge sources to measure semantic relatedness under a uniform model

Ziqi Zhang, Anna Lisa Gentile, Fabio Ciravegna
2011 Conference on Empirical Methods in Natural Language Processing  
Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks.  ...  This paper introduces a method of harnessing different knowledge sources under a uniform model for measuring semantic relatedness between words or concepts.  ...  To do so, we apply a simple maximum set overlap metric to their feature values.  ... 
dblp:conf/emnlp/ZhangGC11 fatcat:2u7rwdv35bghfc24vbdv2xjihi

From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations

P. Du, G. Feng, J. Flatow, J. Song, M. Holko, W. A. Kibbe, S. M. Lin
2009 Bioinformatics  
Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms.  ...  To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results.  ...  Therefore, we define the distance metrics of DO terms based on gene-to-DO mapping profiles.  ... 
doi:10.1093/bioinformatics/btp193 pmid:19478018 pmcid:PMC2687947 fatcat:qyi4xidpdnbdjbl4ez7vw2htby

User Interests Identification on Twitter Using a Hierarchical Knowledge Base [chapter]

Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit Sheth
2014 Lecture Notes in Computer Science  
We argue that the hierarchical semantics of concepts can enhance existing systems to personalize or recommend items based on a varied level of conceptual abstractness.  ...  These approaches typically use available public knowledge-bases (such as Wikipedia) to spot entities and create entity-based user profiles.  ...  Conclusion and Future Work In this paper, we have presented an approach that generates Hierarchical Interest Graph for Twitter users by leveraging Wikipedia Category Graph.  ... 
doi:10.1007/978-3-319-07443-6_8 fatcat:v57lgrxgl5g6tku67plcflinhy

Building a relatedness graph from Linked Open Data: A case study in the IT domain

Tommaso Di Noia, Vito Claudio Ostuni, Jessica Rosati, Paolo Tomeo, Eugenio Di Sciascio, Roberto Mirizzi, Claudio Bartolini
2016 Expert systems with applications  
In this paper we present an approach to build a relatedness graph among resources in the DBpedia dataset that refer to the IT domain.  ...  The availability of encyclopedic Linked Open Data (LOD) paves the way to a new generation of knowledge-intensive applications able to exploit the information encoded in the semantically-enriched datasets  ...  DBpedia also maps hypertextual links between Wikipedia pages by the property dbpedia-owl:wikiPageWikiLink.  ... 
doi:10.1016/j.eswa.2015.08.038 fatcat:mq7ara4eifbujae3jajq2jasje
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