15 research outputs found
PMK : a knowledge processing framework for autonomous robotics perception and manipulation
Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.Peer ReviewedPostprint (published version
SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology
In this era of knowledge economy in which knowledge have become the most precious
resource, surveys have shown that e-Learning has been on the increasing trend in various
organizations including, among others, education and corporate. The use of e-Learning is
not only aim to acquire knowledge but also to maintain competitiveness and advantages
for individuals or organizations. However, the early promise of e-Learning has yet to be
fully realized, as it has been no more than a handout being published online, coupled with
simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by
Web 2.0 technology still hardly overcome common problem such as information
overload and poor content aggregation in a highly increasing number of learning objects
in an e-Learning Management System (LMS) environment.
The aim of this research study is to exploit the Semantic Web (SW) and Knowledge
Management (KM) technology; the two emerging and promising technology to enhance
the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge
Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is
the backbone of SW and KM is introduced for managing knowledge especially from
learning object and developing automated question answering system (Aquas) with
expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to
develop the Ontology in this research work.
The potential of SW and KM technology is identified in this research finding which will
benefit e-Learning developer to develop e-Learning system especially with social
constructivist pedagogical approach from the point of view of KM framework and SW
environment. The (semi-) automatic ontological knowledge base construction system
(SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically
whilst the Aquas with expert locator has facilitated knowledge retrieval
that encourages knowledge sharing in e-Learning environment.
The experiment conducted has shown that the SAOKBCS can extract concept that is the
main component of Ontology from text learning object with precision of 86.67%, thus
saving the expert time and effort to build Ontology manually. Additionally the
experiment on Aquas has shown that more than 80% of users are satisfied with answers
provided by the system. The expert locator framework can also improve the performance
of Aquas in the future usage.
Keywords: semantic web aware â knowledge e-Learning Management System (SWAKMDLS),
semi-automatic ontological knowledge base construction system (SAOKBCS),
automated question answering system (Aquas), Ontology, expert locator
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Ontologie pour la traçabilité des manipulations d'images médicales
In medicine, physicians (general practitioner or specialist) realize a diagnosis to determine patientsâdisease and propose an adapted treatment. This diagnosis is based on research of causes (pathologies)and effects (symptoms) of affection. Today, to realize an effective medical diagnosis, it isimportant to realize a multidisciplinary analysis at a data level. But it is also important to make worktogether experts from different domains. A problem can happen if these experts do not work in thesame place. Thus, how is it possible to ease the way to collaborate together?With evolutions of communication technologies and more particularly Internet, it is easier to developremote collaborative applications. One of the fields covered by theses applications is telemedicineand telediagnosis. Thus, a remote panel of experts can meet together virtually through a virtual roomto ease diagnosis collaboration and co-production. Despite everything, forensic aspects slowed downdevelopment of remote practices due to privacy and personal information sharing.In this context, we developed a platform called COOVADIS (COllabOrative VAscular DIagnoSis) thatenables traceability in such applications based on three original ontologies (pathologies ontology,diagnosis ontology and traceability ontology). This framework was implemented in SaaS (Softwareas a Service) as a web server, to support the collaborative work between health professionals. It wasalso validated from a theoretical and clinical point of view.En mĂ©decine, le diagnostic est la dĂ©marche par laquelle le mĂ©decin, gĂ©nĂ©raliste ou spĂ©cialiste vadĂ©terminer lâaffection dont souffre le patient, et qui va permettre de proposer un traitement. Il reposesur la recherche des causes (pathologie) et des effets (symptĂŽmes) de lâaffection. Un diagnosticmĂ©dical efficace doit aujourdâhui int Ă©grer des analyses multidisciplinaires tant au niveau des donnĂ©esque des experts: et compte tenu de la r Ă©partition gĂ©ographique (par exemple de la dĂ©sertificationmÂŽ edicale), il peut ĂȘtre compliquĂ© de rĂ©unir au mĂȘme endroit les experts.Lâ Ă©volution des technologies de communication, en particulier Internet, a ouvert de nouvelles possibilitĂ©s dans le domaine des applications collaboratives Ă distance et tout particuliĂšrement celuidu t Ă©lĂ©-diagnostic mĂ©dical : par exemple un panel dâexperts distants se rĂ©unit virtuellement parlâintermĂ©diaire dâune salle dâexamen virtuelle qui favorisera la collaboration afin de coproduire undiagnostic. Mais dans le domaine de la mĂ©decine, lâaspect mĂ©dico-l Ă©gal est crucial, et il a freinĂ©ledĂ©veloppement de ces pratiques Ă distances.Dans ce contexte, nous avons dĂ©veloppĂ© une plateforme appelĂ©e COOVADIS (COllabOrative VAscularDIagnoSis) qui permet la traçabilitĂ© dans de telles applications en sâappuyant sur trois ontologiesoriginales (ontologie de la pathologie, ontologie du diagnostic et ontologie de traçabilitĂ©). Cette plateformedâaide `a la collaboration entre professionnels de santĂ© Ă Ă©tĂ© implĂ©mentĂ©e en mode SaaS(Software as a Service) sous la forme dâun serveur Web, et validĂ© dâun point de vue thĂ©orique et clinique
SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology
In this era of knowledge economy in which knowledge have become the most precious
resource, surveys have shown that e-Learning has been on the increasing trend in various
organizations including, among others, education and corporate. The use of e-Learning is
not only aim to acquire knowledge but also to maintain competitiveness and advantages
for individuals or organizations. However, the early promise of e-Learning has yet to be
fully realized, as it has been no more than a handout being published online, coupled with
simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by
Web 2.0 technology still hardly overcome common problem such as information
overload and poor content aggregation in a highly increasing number of learning objects
in an e-Learning Management System (LMS) environment.
The aim of this research study is to exploit the Semantic Web (SW) and Knowledge
Management (KM) technology; the two emerging and promising technology to enhance
the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge
Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is
the backbone of SW and KM is introduced for managing knowledge especially from
learning object and developing automated question answering system (Aquas) with
expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to
develop the Ontology in this research work.
The potential of SW and KM technology is identified in this research finding which will
benefit e-Learning developer to develop e-Learning system especially with social
constructivist pedagogical approach from the point of view of KM framework and SW
environment. The (semi-) automatic ontological knowledge base construction system
(SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically
whilst the Aquas with expert locator has facilitated knowledge retrieval
that encourages knowledge sharing in e-Learning environment.
The experiment conducted has shown that the SAOKBCS can extract concept that is the
main component of Ontology from text learning object with precision of 86.67%, thus
saving the expert time and effort to build Ontology manually. Additionally the
experiment on Aquas has shown that more than 80% of users are satisfied with answers
provided by the system. The expert locator framework can also improve the performance
of Aquas in the future usage.
Keywords: semantic web aware â knowledge e-Learning Management System (SWAKMDLS),
semi-automatic ontological knowledge base construction system (SAOKBCS),
automated question answering system (Aquas), Ontology, expert locator
Dependency Management 2.0 â A Semantic Web Enabled Approach
Software development and evolution are highly distributed processes that involve a multitude of supporting tools and resources. Application programming interfaces are commonly used by software developers to reduce development cost and complexity by reusing code developed by third-parties or published by the open source community. However, these application programming interfaces have also introduced new challenges to the Software Engineering community (e.g., software vulnerabilities, API incompatibilities, and software license violations) that not only extend beyond the traditional boundaries of individual projects but also involve different software artifacts. As a result, there is the need for a technology-independent representation of software dependency semantics and the ability to seamlessly integrate this representation with knowledge from other software artifacts.
The Semantic Web and its supporting technology stack have been widely promoted to model, integrate, and support interoperability among heterogeneous data sources. This dissertation takes advantage of the Semantic Web and its enabling technology stack for knowledge modeling and integration. The thesis introduces five major contributions: (1) We present a formal Software Build System Ontology â SBSON, which captures concepts and properties for software build and dependency management systems. This formal knowledge representation allows us to take advantage of Semantic Web inference services forming the basis for a more flexibility API dependency analysis compared to traditional proprietary analysis approaches. (2) We conducted a user survey which involved 53 open source developers to allow us to gain insights on how actual developers manage API breaking changes. (3) We introduced a novel approach which integrates our SBSON model with knowledge about source code usage and changes within the Maven ecosystem to support API consumers and producers in managing (assessing and minimizing) the impacts of breaking changes. (4) A Security Vulnerability Analysis Framework (SV-AF) is introduced, which integrates builds system, source code, versioning system, and vulnerability ontologies to trace and assess the impact of security vulnerabilities across project boundaries. (5) Finally, we introduce an Ontological Trustworthiness Assessment Model (OntTAM). OntTAM is an integration of our build, source code, vulnerability and license ontologies which supports a holistic analysis and assessment of quality attributes related to the trustworthiness of libraries and APIs in open source systems.
Several case studies are presented to illustrate the applicability and flexibility of our modelling approach, demonstrating that our knowledge modeling approach can seamlessly integrate and reuse knowledge extracted from existing build and dependency management systems with other existing heterogeneous data sources found in the software engineering domain. As part of our case studies, we also demonstrate how this unified knowledge model can enable new types of project dependency analysis