Filters








20,600 Hits in 1.5 sec

Contextual Ontology Learning

Dr. Lobna KAROUI
2018 Zenodo  
The unsupervised hierarchical clustering divides recursively each cluster based on an automatic context definition.  ...  Keywords Context, machine learning, ontology, concept, clustering, learning, semantic  ... 
doi:10.5281/zenodo.1184949 fatcat:crdivt2tcjb7ffv6lmdxtkzily

An Approach of Context Ontology for Robust Face Recognition Against Illumination Variations

M.Rezaul Bashar, Yan Li, Phill Kyu Rhee
2007 2007 International Conference on Information and Communication Technology  
Context ontology is built using context acquisition, context learning and context categorization.  ...  Our proposed system works on two phases: environmental context ontology building (modelling) and recognition using context ontology.  ...  Context ontology is used because the learning process is done in a hierarchical way using ontological tree structure.  ... 
doi:10.1109/icict.2007.375351 fatcat:2x6cd2zg4vfu7hqjgdbkxdkvre

Semantic HMC for Big Data Analysis [article]

Thomas Hassan, Aurlie Bertaux, Nuno Silva
2014 arXiv   pre-print
In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process.  ...  We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.  ...  ACKNOWLEDGMENT This project is founded by the company Actualis SARL, the French agency ANRT and through the Portuguese COMPETE Program under the project AAL4ALL (QREN13852).  ... 
arXiv:1412.0854v1 fatcat:4dvddvrtjfhj5jcpagvxuq4in4

Semantic HMC for big data analysis

Thomas Hassan, Rafael Peixoto, Christophe Cruz, Aurlie Bertaux, Nuno Silva
2014 2014 IEEE International Conference on Big Data (Big Data)  
In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a nonsupervised Ontology learning process.  ...  We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.  ...  ACKNOWLEDGMENT This project is founded by the company Actualis SARL, the French agency ANRT and through the Portuguese COMPETE Program under the project AAL4ALL (QREN13852).  ... 
doi:10.1109/bigdata.2014.7004482 dblp:conf/bigdataconf/HassanPCBS14 fatcat:3unhht3lz5abnbftvyb4tbbae4

Automatic Extraction of Structurally Coherent Mini-Taxonomies [chapter]

Khalid Saleem, Zohra Bellahsene
2008 Lecture Notes in Computer Science  
Our experiments show that these hierarchical patterns are good enough to represent and describe the concepts of the domain ontology.  ...  A very important characteristic of an ontology is its hierarchical structure of concepts. Semantic web is heavily dependent on the XML paradigm, which inherently follows the hierarchical structure.  ...  Another issue for the future is a benchmark for automatic ontology learning tools in a large scale scenario.  ... 
doi:10.1007/978-3-540-87877-3_25 fatcat:mowyc77zizh6nmpkyvkq3xnbae

Exploiting ontologies for automatic image annotation

Munirathnam Srikanth, Joshua Varner, Mitchell Bowden, Dan Moldovan
2005 Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05  
Specifically, the hierarchy is used in the context of generating a visual vocabulary for representing images and as a framework for the proposed hierarchical classification approach for automatic image  ...  Machine learning approaches have been explored to model the association between words and images from an annotated set of images and generate annotations for a test image.  ...  Based on the above results, the annotation word hierarchy seems is effective as a framework for the hierarchical classification model as well as a source for selecting the initial clusters for the visual  ... 
doi:10.1145/1076034.1076128 dblp:conf/sigir/SrikanthVBM05 fatcat:7eoobgq4ebc75hfole66r67hre

Fuzzy clustering-based approach to derive hierarchical structures from folksonomies

Marouf Zahia, Benslimane Sidi Mohamed
2013 2013 ACS International Conference on Computer Systems and Applications (AICCSA)  
In this paper we propose an approach for extracting ontological structures from folksonomies that exploits the power of fuzzy clustering using new similarity and generality measure.  ...  Our experimental results on real world data sets show that our method can effectively learn the ontology structure from the folksonomies.  ...  Fig. 7 . 7 F-measure based comparison between the learned hierarchical structures and the reference ontologies from WordNet and Wikipedia.  ... 
doi:10.1109/aiccsa.2013.6616455 dblp:conf/aiccsa/ZahiaM13 fatcat:c3hob4b3rzaavljg2xbxjn5wsq

Learning a taxonomy from a set of text documents

Mari-Sanna Paukkeri, Alberto Pérez García-Plaza, Víctor Fresno, Raquel Martínez Unanue, Timo Honkela
2012 Applied Soft Computing  
The taxonomy is obtained by a hierarchical approach of Self-Organizing Map clustering of the concept definition documents.  ...  This article presents a methodology for learning taxonomy from a set of documents that each explain one concept.  ...  Hierarchical clustering The taxonomy generation process is based on unsupervised learning.  ... 
doi:10.1016/j.asoc.2011.11.009 fatcat:qxond2lfpzhehm3nt7ugcsqsge

Ontology Learning Part One — on Discovering Taxonomic Relations from the Web [chapter]

Alexander Maedche, Viktor Pekar, Steffen Staab
2003 Web Intelligence  
Thus, in the context of the Semantic Web, ontologies describe domain theories for the explicit representation of the semantics of the data.  ...  Hierarchical clustering algorithms are preferable for detailed data analysis. They produce hierarchies of clusters, and therefore contain more information than non-hierarchical algorithms.  ... 
doi:10.1007/978-3-662-05320-1_14 fatcat:xlwrtgwwlvhy5mzmwhui4wdmja

Semantic Image Clustering Using Object Relation Network [chapter]

Na Chen, Viktor K. Prasanna
2012 Lecture Notes in Computer Science  
With a series of coarse-to-fine lenses, images are clustered in a top-down hierarchical manner.  ...  We adopt the class hierarchies in a guide ontology as different levels of lenses to view the bag-of-semantics models.  ...  For web images, textual context is believed to be a useful addition to the visual features.  ... 
doi:10.1007/978-3-642-34263-9_8 fatcat:mj5iad73xnbbfksij32arhxvzy

Taxonomy Extraction from Automotive Natural Language Requirements Using Unsupervised Learning

Martin Ringsquand, Mathias Schraps
2014 International Journal on Natural Language Computing  
The approach is based on the distributional hypothesis and the special characteristics of domain-specific German compounds.  ...  Evaluation shows that this taxonomy extraction approach outperforms common hierarchical clustering techniques.  ...  [3] introduced formal concept analysis to learn taxonomies and compared its performance to hierarchical clustering.  ... 
doi:10.5121/ijnlc.2014.3403 fatcat:aukdfc5ibbbatgwxtvn4bldgeu

Ontology Knowledge Mining for Ontology Alignment

Rihab Idoudi, Karim Saheb Ettabaa, Basel Solaiman, Kamel Hamrouni
2016 International Journal of Computational Intelligence Systems  
The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously.  ...  ontology alignment based on the ontology knowledge mining.  ...  Note that, for ontologies of this benchmark, the first step of the hierarchical fuzzy clustering phase has been performed with (c=2).  ... 
doi:10.1080/18756891.2016.1237187 fatcat:3n7usmjidzdjzesxw6xx37cj34

Ontology Learning from Text [chapter]

Alexander Maedche, Steffen Staab
2001 Lecture Notes in Computer Science  
KM is one of the main areas for ontology use and therefore gives input for various ontology learning aspects Well-established knowledge life cycle inspires ontology life cycle (→ ontology evolution/ management  ...  /negotiation) with ontology learning as important component © Paul Buitelaar, Philipp Cimiano, Marko Grobelnik, Michael Sintek: Ontology Learning from Text.  ...  Some Current Work on Ontology Learning from Text Economic Univ., Prague (Kavalec and Svatek, 2005) Relation Label Extraction Extension of Association Rules Algorithm Free Univ.  ... 
doi:10.1007/3-540-45399-7_30 fatcat:4mevea3rwrecncb4haxw5uemni

Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population

Vindula Jayawardana, Dimuthu Lakmal, Nisansa De Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha, Madhavi Perera
2018 The International Journal on Advances in ICT for Emerging Regions  
Ontology population, on the other hand, is an inherently problematic process, as it needs manual intervention to prevent the conceptual drift.  ...  With the arising fields such as information extraction and knowledge management, the role of ontology has become a driving factor of many modern day systems.  ...  Sri Lanka, for the immense assistance that they provided in preparing this work.  ... 
doi:10.4038/icter.v11i1.7191 fatcat:lyiwreubpvfwvp2cr7lfmholf4

Semi-Supervised Instance Population of an Ontology using Word Vector Embeddings [article]

Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha, Madhavi Perera
2017 arXiv   pre-print
In many modern day systems such as information extraction and knowledge management agents, ontologies play a vital role in maintaining the concept hierarchies of the selected domain.  ...  Hence, in this study, we propose a novel way of semi-supervised ontology population through word embeddings as the basis.  ...  Semi-Supervised Hierarchical Clustering Based Model (M 5 ): The next model being used is a semi-supervised method based on hierarchical clustering.  ... 
arXiv:1709.02911v1 fatcat:mqum3xkkrvbrtelnrwimm4jgai
« Previous Showing results 1 — 15 out of 20,600 results