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Fuzzy-Aggregation-Semantic-Similarity 100%

A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures Jorge Martinez-Gil, Software Competence Center Hagenberg (Austria) email:


Word-Co-Occurrence-Literature 99%

Analysis of word co-occurrence in human literature for supporting semantic correspondence discovery Jorge Martinez-Gil Mario Pichler Software Competence Center Hagenberg Softwarepark 21, 4232 Hagenberg, Austria Software Competence Center Hagenberg Softwarepark 21, 4232 Hagenberg, Austria ABSTRACT Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea.


Biomedical-Semantic-Similarity 99%

Evolutionary algorithm based on different semantic similarity functions for synonym recognition in the biomedical domain José M.


semantic-similarity 99%

Annotated Bibliography on Semantic Similarity Jorge Martinez-Gil Software Competence Center Hagenberg GmbH Softwarepark 21, 4232 Hagenberg, Austria jorge.


Semantic-Similarity-Web-Intelligence 98%

(will be inserted by the editor) An Overview of Textual Semantic Similarity Measures Based on Web Intelligence Jorge Martinez-Gil Received:


Semantic-Similarity-Using-Google 98%

(will be inserted by the editor) Semantic Similarity Measurement Using Historical Google Search Patterns Jorge Martinez-Gil and Jose F.


Tag-Cloud-Refactoring 97%

A Customized Similarity Measure and Its Application for Tag Cloud Refactoring David Urdiales-Nieto, Jorge Martinez-Gil, and Jos´e F.


ResearchPaper 97%

Learning Ticket Similarity with Context-sensitive Deep Neural Networks Iheb Ben Abdallah Durga Prasad Muni, Suman Roy, Yeung Tack Yan John John Lew Chiang, Navin Budhiraja Computer Science and Electrical Engineering, Ecole CentraleSupelec Grande Voie des Vignes, Chˆatenay-Malabry Paris, France 92290 Infosys Limited #44 Electronic City, Hosur Road Bangalore, India 560100 {DurgaPrasad Muni,Suman Roy,Yeung Chiang,Navin.


V9I5-5 96%

Similarity Measure:Similarity is numeric considering the large size of internet, its users and measure of the degree to which two objects are variety of applications being used;


Ontology-Matching-Heuristic 96%

Although automatic matching is perhaps the most appropriate way to align ontologies, it has the disadvantage that finding good similarity functions is, data, context, and sometimes even user-dependent, and needs to be reconsidered every time new data or a new task is inspected (Kiefer et al, 2007).


Semantic-Similarity-Using-Search-Engines 96%

Smart Combination of Web Measures for Solving Semantic Similarity Problems Jorge Martinez-Gil, Jose F.


Ontology-Matching-Genetic-Algorithms 95%

Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method is expected to scale better for a high number of measures.


Semantic-Similarity-Human-Literature 94%

Looking for the Best Historical Window for Assessing Semantic Similarity Using Human Literature Jorge Martinez-Gil Mario Pichler Lorena Paoletti Software Competence Center Hagenberg Softwarepark 21 4232, Austria Software Competence Center Hagenberg Softwarepark 21 4232, Austria Software Competence Center Hagenberg Softwarepark 21 4232, Austria ABSTRACT We describe the way to get benefit from broad cultural trends through the quantitative analysis of a vast digital book collection representing the digested history of humanity.


Validation-Semantic-Correspondences 94%

On the other hand, the Google Similarity Distance has appeared recently.


Biomedical-Fuzzy-Logics 93%

Talk on “Accurate semantic similarity measurement of biomedical nomenclature by means of fuzzy logic” Jorge Martinez-Gil Software Competence Center Hagenberg GmbH Softwarepark 21, 4232 Hagenberg, Austria Abstract.


fdata-03-00012 92%

As a result, FoodKG can enhance knowledge graphs with semantic similarity scores and relations between different classes, classify the existing entities, and allow FEW experts and researchers to use scientific terms for describing FEW concepts.


Collaborative recommendation 91%

-1- Agenda  Collaborative Filtering (CF) – – – – – – – – – – – Pure CF approaches User-based nearest-neighbor The Pearson Correlation similarity measure Memory-based and model-based approaches Item-based nearest-neighbor The cosine similarity measure Data sparsity problems Recent methods (SVD, Association Rule Mining, Slope One, RF-Rec, …) The Google News personalization engine Discussion and summary Literature -2- Collaborative Filtering (CF)  The most prominent approach to generate recommendations – used by large, commercial e-commerce sites – well-understood, various algorithms and variations exist – applicable in many domains (book, movies, DVDs, ..)  Approach – use the "wisdom of the crowd"


supplementary 90%

 a= 1 λ k  ∑ ∑  f(mi , µc ) + wml  c=1 mi ∈M  ∑ (mi ,mj )∈ML li ̸=lj f(mi , mj ) − wcl ∑ (mi ,mj )∈CL li =lj (3)  f(mi , mj )  end while Feature Entity Heads Arguments Predicates or 2nd Order Similarity of Mention Words Number;


Textual-Renderings-Ontologies 90%

As result, we got some evidences that this technique gives us a good measure of the similarity of ontologies and, therefore can allow us to improve the effectiveness of the alignment process.


KMeansRE 89%

similarity) to the same nearest mean, then the actual distance between these items also tends to the minima.


SAWSDL-Web-Services 86%

String normalization, String similarity, Data Type Comparison, Linguistic methods, Inheritance analysis, Data analysis, Graph-Mapping, Statistical analysis and Taxonomy analysis.


introduction to chemical engineering ch (10) 86%

(Incidentally, the similarity implies that the physical processes that cause the transfer are also similar.) Chapter 10 – Answer Key, Introduction to Chemical Engineering: