59 research outputs found
Object-Oriented Similarity Measures for Semantic Web Service Matchmaking
The semantic annotation of Web services capabilities with ontological information aims at providing the neces-sary infrastructure for facilitating efficient and accurate service discovery. The main idea is to apply reasoning techniques over semantically enhanced Web service re-quests and advertisements in order to determine Web ser-vices that meet certain requirements. In this paper we present our work for introducing similarity measures in-spired from the domain of Object-Oriented paradigm for ontology concept matching. Our work focuses on the utili-zation of such measures over an Object-Oriented schema that is created through mapping rules of OWL constructs and semantics into the Object-Oriented model. The goal of the approach is to combine the Object-Oriented repre-sentation of the information and the reasoning over OWL semantics in order to enhance the retrieval of semanti-cally relevant, to some criteria, Web services
Ontology-based personalized job recommendation framework for migrants and refugees
Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendation
Neural Crystals
We face up to the challenge of explainability in Multimodal Artificial
Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum
computing, interpretable and transparent spin-geometrical neural architectures
for early fusion of large-scale, heterogeneous, graph-structured data are
envisioned, harnessing recent evidence for relativistic quantum neural coding
of (co-)behavioral states in the self-organizing brain, under competitive,
multidimensional dynamics. The designs draw on a self-dual classical
description - via special Clifford-Lipschitz operations - of spinorial quantum
states within registers of at most 16 qubits for efficient encoding of
exponentially large neural structures. Formally 'trained', Lorentz neural
architectures with precisely one lateral layer of exclusively inhibitory
interneurons accounting for anti-modalities, as well as their co-architectures
with intra-layer connections are highlighted. The approach accommodates the
fusion of up to 16 time-invariant interconnected (anti-)modalities and the
crystallization of latent multidimensional patterns. Comprehensive insights are
expected to be gained through applications to Multimodal Big Data, under
diverse real-world scenarios.Comment: preprint revised; to appear In Proceedings of the IEEE International
Conference on Big Data 2023/ 3rd Workshop on Multimodal AI (MMAI 2023
On the Combination of Textual and Semantic Descriptions for Automated Semantic Web Service Classification
Abstract Semantic Web services have emerged as the solution to the need for automating several aspects related to service-oriented architectures, such as service discovery and composition, and they are realized by combining Semantic Web technologies and Web service standards. In the present paper, we tackle the problem of automated classification of Web services according to their application domain taking into account both the textual description and the semantic annotations of OWL-S advertisements. We present results that we obtained by applying machine learning algorithms on textual and semantic descriptions separately and we propose methods for increasing the overall classification accuracy through an extended feature vector and an ensemble of classifiers
Semantic Web Service Composition using Planning and Ontology Concept Relevance
Abstract: This paper presents PORSCE II, a system that combines planning and ontology concept relevance for automatically composing semantic web services. The presented approach includes transformation of the web service composition problem into a planning problem, enhancement with semantic awareness and relaxation and solution through external planners. The produced plans are visualized and their accuracy is assessed
Towards Semantic Detection of Smells in Cloud Infrastructure Code
Automated deployment and management of Cloud applications relies on
descriptions of their deployment topologies, often referred to as
Infrastructure Code. As the complexity of applications and their deployment
models increases, developers inadvertently introduce software smells to such
code specifications, for instance, violations of good coding practices, modular
structure, and more. This paper presents a knowledge-driven approach enabling
developers to identify the aforementioned smells in deployment descriptions. We
detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs
capturing deployment models. We show the feasibility of our approach with a
prototype and three case studies.Comment: 5 pages, 6 figures. The 10 th International Conference on Web
Intelligence, Mining and Semantics (WIMS 2020
Implementation of an Interactive Crowd-Enhanced Content Management System for Tourism Development
This paper investigated the role of interactive tourist mobile apps in tourism development. The researchers presented the e-Tracer application, which was developed taking into consideration the recent advantages in mobile computing, the importance of user-generated content and the needs of northern Greece and the lower Balkan countries. Apart from crowd-based content creation, a new generation of apps for tourism development may include additional components like serious games for tourists, map-based navigation systems and augmented/virtual reality applications, in order to offer memorable user experiences for tourists. An agile content management system design methodology was followed by taking into account the needs of alternative tourist destinations, small to medium sized real-world museums and driver rest areas located around highways which connect cross-country destinations in the lower Balkan countries and Turkey. This work positioned the role of interactive crowd-enhanced platforms for content management of tourist-related information in tourism development, economic growth and sustainability of the Egnatia motorway surrounding areas in Greece.
Keywords: mobile computing, content management systems, recommender systems, serious games, virtual/augmented reality, tourism developmen
SODALITE@RT: Orchestrating Applications on Cloud-Edge Infrastructures
AbstractIoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this paper, we present the main features of the SODALITE@RT: modeling of cloud-edge resources and applications using open standards and infrastructural code, and automated deployment, monitoring, and management of the applications in the target infrastructures based on such models. The capabilities of the SODALITE@RT are demonstrated through a relevant case study
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