43,932 research outputs found
Ontology technology for the development and deployment of learning technology systems - a survey
The World-Wide Web is undergoing dramatic changes at the moment. The Semantic Web is an initiative to bring meaning to the Web. The Semantic Web is based on ontology
technology – a knowledge representation framework – at its core. We illustrate the importance of this evolutionary development. We survey five scenarios demonstrating different forms of applications of ontology technologies in the development and deployment of learning technology
systems. Ontology technologies are highly useful to organise, personalise, and publish learning content and to discover, generate, and compose learning objects
Semantic model-driven development of web service architectures.
Building service-based architectures has become a major area of interest since the advent of Web services. Modelling these architectures is a central activity. Model-driven development is a recent approach to developing software systems based on the idea of making models the central artefacts for design representation, analysis, and code generation.
We propose an ontology-based engineering methodology for semantic model-driven composition and transformation of Web service architectures. Ontology technology as a logic-based knowledge representation and reasoning framework can provide answers to the needs of sharable and reusable semantic models and descriptions needed for service engineering. Based on modelling, composition and code generation techniques for service architectures, our approach provides a methodological framework for ontology-based semantic service architecture
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Web Service Discovery in the FUSION Semantic Registry
The UDDI specification was developed as an attempt to address the key challenge of effective Web service discovery and has become a widely adopted standard. However, the text-based indexing and search mechanism that UDDI registries offer does not suffice for expressing unambiguous and semantically rich representations of service capabilities, and cannot support the logic-based inference capacity required for facilitating automated service matchmaking. This paper provides an overview of the approach put forward in the FUSION project for overcoming this important limitation. Our solution combines SAWSDL-based service descriptions with service capability profiling based on OWL-DL, and automated matchmaking through DL reasoning in a semantically extended UDDI registry
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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