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About 10,000 papers for “Semantic computing”

Semantic Computing

Sherif Sakr, Albert Y. Zomaya · Encyclopedia of Big Data Technologies · 2019

Cited by 0

Semantic Computing

Phillip C.‐Y. Sheu · IFIP International Federation for Information Processing/IFIP · 2008

Cited by 1Open access

Semantic computing

Sheu, Phillip C -Y · CERN Document Server (European Organization for Nuclear Research) · 2010

Cited by 0

Secure Semantic Computing

Hiroshi Yamaguchi, Ryo Fujita, Shigeo Tsujii · 2016

Semantic computing is an emerging research field that has drawn much attention from both academia and industry. It addresses the derivation and matching of semantics of computational "contents" where "contents" may be anything including text, multimedia, hardware, network, etc. which can be mapped to many areas in computer science that involve analyzing and processing the intension of humans with computational content. This paper discusses some potential applications of Semantic Computing in Computer Science. We examine semantic analytical techniques as applied for primary sequence analysis for genes and proteins in biomedical area. We also introduce how privacy and confidentiality problems are resolved, while the query from a user are written in natural language.

Cited by 0

Semantic computing service trading

Aurélie Hurault, DAYDE, Michel · OpenGrey (Institut de l'Information Scientifique et Technique) · 2006

La recherche de services de calcul qui correspondent aux besoins d'un utilisateur sur une grille de calcul est une activité complexe. L'objet des travaux réalisés consiste à utiliser une description formelle du domaine d'application comportant l'ensemble des données et des opérateurs du domaine ainsi que leurs propriétés. Cette description est effectuée sous forme d'une spécification algébrique. Le courtage consiste alors à effectuer un filtrage modulo la théorie équationnelle associée à la spécification, entre le terme représentant le service souhaité et les termes représentant les services disponibles. Nous proposons et avons implanté deux algorithmes différents inspirés d'un parcours de l'arbre de recherche des solutions contraint par une quantité d'énergie. Nous présentons les domaines applicatifs algèbre linéaire et optimisation et nous nous intéressons au traitement de la combinaison de domaines. Enfin, nous concluons et présentons les perspectives principales de nos travaux.

Cited by 0

Semantic computing with IEML

Pierre Lévy · Collective Intelligence · 2023

This paper presents IEML, Information Economy MetaLanguage, a constructed language with the same expressive power as a natural language and with computable semantics. Distinguished from pragmatic and referential semantics, linguistic semantics have not yet been completely formalized. Only its syntagmatic dimension has been mathematized in the form of regular languages. Its paradigmatic dimension remained to be formalized. In order to complete the mathematizing of language, including its paradigmatic dimension, I have coded linguistic semantics with IEML. This article introduces its 3000-word dictionary, its formal grammar, and its integrated tools for building semantic graphs. For the future, IEML could become a vector for a fluid calculation and communication of meaning—semantic interoperability—capable of de-compartmentalizing the digital memory, and of advancing the progress of collective intelligence, artificial intelligence, and digital humanities. I conclude by indicating some research directions.

Cited by 4Open access

Towards Semantic tuplespace computing

Lyndon Nixon, Olena Antonechko, Robert Tolksdorf · 2007

In this paper we introduce Semantic Web Spaces, a middleware for coordinating knowledge processes on the Semantic Web. Co-ordination is an important aspect of any type of interaction between computer agents, but we find especially so on the Semantic Web in which the communication contains knowledge rather than data and correct inferences can only be made when the right knowledge is available at the right time. Because of this we have identified tuplespace computing as a relevant paradigm for agent communication on the Semantic Web and have prototypically realized a system based on a Linda-inspired coordination model and on core semantic technologies such as RDF, ontologies and reasoning.

Cited by 33

Semantic web services in clouds for semantic computing

Gurparkash Singh Kang, Jaiteg Singh, Maninderjit Singh Khanna · 2011

The progression of interleaved domains like software engineering, natural language processing, artificial intelligence, programming languages and cloud computing has resulted in evolution of concept called semantic computing.

Cited by 2

SEMANTIC COMPUTING AND COMPUTER SCIENCE

Hao Ke, Zhiyuan Gong, Chengjia Huo et al. · International Journal of Semantic Computing · 2011

Semantic Computing is an emerging research field that has drawn much attention from both academia and industry. It addresses the derivation and matching of semantics of computational "content" where "content" may be anything including text, multimedia, hardware, network, etc. which can be mapped to many areas in Computer Science that involve analyzing and processing the intentions of humans with computational content. This paper discusses some potential applications of Semantic Computing in Computer Science.

Cited by 9

PROBLEMS, SOLUTIONS, AND SEMANTIC COMPUTING

Phillip C.‐Y. Sheu, C. V. Ramamoorthy · International Journal of Semantic Computing · 2009

Semantic Computing extends Semantic Web both in breadth and depth. It bridges, and integrates, several computing technologies into a complete and unified theme. This article discusses the essences of Semantic Computing with a description of SemanticServices.Net, a new paradigm that enables "Problem-driven" search that may offer a new story to the Internet.

Cited by 12

Semantic Computing and Quality Software

Phillip C.‐Y. Sheu · 2007

The field Semantic Computing applies technolo- gies in natural language processing, data and knowledge engineering, software engineering, computer systems and networks, signal processing and pattern recogni- tion, and any combination of the above to extract, access, transform and synthesize the semantics as well as the contents of multimedia, texts, services and structured data.

Cited by 0

Semantic Classifier for Affective Computing

Mladen Stanojević, Sanja Vraneš · 2008

One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, Support Vector Machines, Linear Discriminant Analysis, Neural Networks or k-Nearest Neighbors, which classify emotions using several features obtained from facial expressions, body gestures, speech or different physiological signals. In this paper, we propose a Semantic Classifier as a new, simple and efficient approach to the problem of automatic emotion recognition. The implementation of the Semantic Classifier is based on the basic, natural principles used to decrease the complexity of problems found in n-dimensional spaces: discretization, structure identification and semantic optimization. The proposed classifier exhibits some self-organizing features and supports learning by repetition, generalization and specialization. It will be used to implement a distributed and robust system for emotion recognition.

Cited by 7

Semantic Discovery for Ubiquitous Computing

Robert E. Mcgrath, M. Dennis Mickunas, Roy H. Campbell · 2007

Recent trends in mobile and ubiquitous computing have created new requirements for automatic configuration and reconfiguration of network devices. A variety of new protocols have been proposed, which attempt to provide automatic "discovery" and configuration of network devices and services. These protocols face many technical challenges, including scalability, security, and semantic interoperability. XML is an important technology for the open exchange of semantic information, but does not solve the problem of semantic interoperability. Semantic standards must be developed through open community processes.

Cited by 0

Semantic computing for human phenotypes

Robert Gentleman, Rafael S. Gonçalves, Vincent J. Carey · bioRxiv (Cold Spring Harbor Laboratory) · 2024

Abstract In many fields, research progress may be hindered by indefiniteness of language used to describe experimental conditions and outcomes. Harmonization of data resources generated by independent groups is important for integrative analysis. Adoption of formal ontologies and vocabularies for experiment annotation should help with harmonization tasks, but the use of ontologies also suffers from a lack of definiteness. In this study we explore how natural language characterization of human diseases coupled with ontologic mapping of study outcome terminology can be used to integrate information from multiple studies of genetic origins of disease risk. Open source tools and workflows are presented. This work exposes areas for improvement in tooling for data harmonization, which is a fundamental requirement for efficient research progress.

Cited by 0Open access