Exploration of grateful dead concerts and memorabilia on the semantic Web

© 2018 CEUR-WS. All rights reserved. With the increasing importance attributed to intangible cultural heritage, of which music performance is an important part, public archive collections contain a growing proportion of audio and video material. Currently used models have only limited capabilities for their representation. This demo illustrates our proposal for a unified ontological model of live music recordings and associated tangible artefacts with a Web application for the exploration of live music events of the Grateful Dead

PervasIve nursing and docToral Assistant (PINATA)

Providing patient-centric health care services is the goal of health-care institutions. However, due to human-related aspects, this goal is frequently undermined. PINATA offers an automated patient-centric system based upon Pervasive Ambience Intelligence techniques and enriched with Semantic Web technologies. The system makes use of RFID sensors to track the movements of patients and medical staff in order to direct staff effectively. An automated camera system monitors the patients and alerts hospital staff in case of emergencies. Through handheld devices hospital staff is automatically provided with relevant patient information gathered from various sources. PINATA is based on a Service Oriented Architecture and makes use of domain specific ontologies.peer-reviewe

Active document enrichment using adaptive information extraction from text

The traditional process of document annotation for knowledge identification and extraction in the Semantic Web (SW) is complex and time consuming, as it requires manual annotation by domain experts. There is currently a strong interest in Text Mining technologies (and in particular in Human Language-based Technologies), for reducing the burden of text annotation for Knowledge Management (KM). In this poster we present Melita, an annotation interface that uses Adaptive Information Extraction from texts for reducing the burden of text annotation.peer-reviewe

Applying semantic web services to enterprise web

Enterprise Web provides a convenient, extendable, integrated platform for information sharing and knowledge management. However, it still has many drawbacks due to complexity and increasing information glut, as well as the heterogeneity of the information processed. Research in the field of Semantic Web Services has shown the possibility of adding higher level of semantic functionality onto the top of current Enterprise Web, enhancing usability and usefulness of resource, enabling decision support and automation. This paper aims to explore the use of Semantic Web Services in Enterprise Web and discuss the Semantic Web Services (SWS) approach for designing Enterprise Web applications. A Semantic Web Service oriented model is presented, in which resources and services are described by ontology, and processed through Semantic Web Service, allowing integrated administration, interoperability and automated reasoning

A METHOD FOR SEMANTIC WEB SERVICE COMPOSITION BASED ON PATTERN MATCHING

The composition of semantic web services is a very important and actual problem in the semantic web services research area. There are several semi-automatic approaches for this problem, but most of the results are related to automatic approaches. In this paper we present an automatic approach for the composition of semantic web services based on pattern matching. We consider a special type of semantic description, represented as a list of semantic descriptions corresponding to several semantic web services. The semantic description related to the semantic web service that we want to obtain is decomposed until all the parts of the semantic description correspond to semantic web services from a library. In the end, all the necessary semantic web services found in the library are composed in order to obtain the semantic web service that we wanted to construct.semantic web service composition, semantic description decomposition, pattern matching

Approaches to Semantic Web Services: An Overview and Comparison

Abstract. The next Web generation promises to deliver Semantic Web Services (SWS); services that are self-described and amenable to automated discovery, composition and invocation. A prerequisite to this, however, is the emergence and evolution of the Semantic Web, which provides the infrastructure for the semantic interoperability of Web Services. Web Services will be augmented with rich formal descriptions of their capabilities, such that they can be utilized by applications or other services without human assistance or highly constrained agreements on interfaces or protocols. Thus, Semantic Web Services have the potential to change the way knowledge and business services are consumed and provided on the Web. In this paper, we survey the state of the art of current enabling technologies for Semantic Web Services. In addition, we characterize the infrastructure of Semantic Web Services along three orthogonal dimensions: activities, architecture and service ontology. Further, we examine and contrast three current approaches to SWS according to the proposed dimensions

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Semantic Web meets Web 2.0 (and vice versa): The Value of the Mundane for the Semantic Web

Web 2.0, not the Semantic Web, has become the face of “the next generation Web” among the tech-literate set, and even among many in the various research communities involved in the Web. Perceptions in these communities of what the Semantic Web is (and who is involved in it) are often misinformed if not misguided. In this paper we identify opportunities for Semantic Web activities to connect with the Web 2.0 community; we explore why this connection is of significant benefit to both groups, and identify how these connections open valuable research opportunities “in the real” for the Semantic Web effort

Towards intelligent web services: the web service modeling ontology (WSMO)

The Semantic Web and the Semantic Web Services build a natural application area for Intelligent Agents, namely querying and reasoning about structured knowledge and semantic descriptions of services and their interfaces on the Web. This paper provides an overview of the Web Service Modeling Ontology, a conceptual framework for the semantical description of Web services