Results 251 to 260 of about 476,390 (273)
Some of the next articles are maybe not open access.
Semantic Social Networks Analysis
2014International ...
Thovex, Christophe +2 more
openaire +2 more sources
SENECA (Semantic Networks for Conceptual Analysis)
ACM SIGART Bulletin, 1982SENECA (2) is a representation based on semantic networks, allowing the description of objects, their characteristics and relationships. The processes that can be performed in the domain of discourse, modifying the information, can also be represented.
E. G. Camarero +2 more
openaire +1 more source
Semantic Social Networks Analysis
2017International ...
Thovex, Christophe +4 more
openaire +1 more source
Pattern discovery using semantic network analysis
2012 3rd International Workshop on Cognitive Information Processing (CIP), 2012Cognitive information processing at higher conceptual levels requires a computational approach to knowledge representation and analysis. Semantic network analysis bridges the gap between probabilistic pattern recognition techniques and symbolic representations by replacing cumbersome and computationally complex forms of logic-based semantic inference ...
Robin Burk +4 more
openaire +1 more source
Neural networks for latent semantic analysis
2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies and Industrial Opportunities (Cat. No.00CH37141), 2002Textual data can usually be represented by a large-dimensional matrix. For tasks such as information retrieval or information filtering, it is therefore necessary to reduce the dimensionality. A statistical method called latent semantic analysis (LSA) can be used not only for dimensional reduction but also for transformation of the original space into ...
R. Thawonmas +3 more
openaire +1 more source
Coloured semantic networks for content analysis
Quality & Quantity, 1994This paper adapts a widespread formalism of Knowledge Representation known in the AI literature as J. Sowa'sConceptual Graphs to the purposes of Content Analysis. It is proposed that instead of nested contexts, negation and modalities could be represented by colouring the links and the nodes of the graphs.
openaire +2 more sources
Towards Semantic-Based Social Network Analysis
2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2018We propose a semantic-based methodology for Social Network Analysis (SNA). This methodology addresses computations needed for SNA in a declarative way -in contrast to traditional SNA where computations are procedural. Our ingredients are semantic technologies: We define an ontology to represent graphs, their components (nodes, edges or paths), and the ...
Raji Ghawi +2 more
openaire +1 more source
Semantic Network Analysis on TCM Language System
IEEE International Workshop on Semantic Computing and Systems, 2008TCMLS is the largest traditional Chinese medicine ontology all over the world. This paper surveys the topology of TCMLS sub-ontologies in complex networks way. The result indicates that the network, composed of concepts and instances, displays both patterns of small-world and scale-free. Moreover, three centrality indices of nodes tend to covary in the
Jun Ma, Huajun Chen
openaire +1 more source
Deep Neural Networks in Semantic Analysis
2019This paper presents research of the possibilities of application deep neural networks in semantic analysis. This paper presents the current situation in this area and the prospects for application an artificial intelligence in semantic analysis and trend and tendencies of this science area.
Alexey Averkin, Sergey Yarushev
openaire +1 more source
Keyphrase Extraction Using Semantic Networks Structure Analysis
Sixth International Conference on Data Mining (ICDM'06), 2006Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks.
Chong Huang +4 more
openaire +1 more source