Dimitrios-Emmanuel Spanos

Bringing relational databases into the semantic web: a survey

Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for Semantic Web applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the Semantic Web community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.

Exposing scholarly information as Linked Open Data: RDFizing DSpace contents:

Purpose – This paper aims to introduce a transformation engine which can be used to convert an existing institutional repository installation into a Linked Open Data repository. Design/methodology/approach – The authors describe how the data that exist in a DSpace repository can be semantically annotated to serve as a Semantic Web (meta)data repository. Findings – The authors present a non-intrusive, standards-compliant approach that can run alongside with current practices, while incorporating state-of-the art methodologies. Originality/value – Also, they propose a set of mappings between domain vocabularies that can be (re)used towards this goal, thus offering an approach that covers both the technical and semantic aspects of the procedure.

Cognitive radio-aided wireless sensor networks for emergency response

A lot of research effort has been put into wireless sensor networks (WSNs) and several methods have been proposed to minimize the energy consumption and maximize the networks lifetime. However, little work has been carried out regarding WSNs deployed for emergency situations. We argue that such WSNs should function under a flexible channel allocation scheme when needed and be able to operate and adapt in dynamic, ever-changing environments coexisting with other interfering networks (IEEE 802.11b/g, 802.15.4, 802.15.1). In this paper, a simple and efficient method for the detection of a single operational frequency channel that guarantees satisfactory communication among all network nodes is proposed. Experimental measurements carried out in a real environment reveal the coexistence problem among networks in close proximity that operate in the same frequency band and prove the validity and efficiency of our approach.

Transient and Persistent RDF Views over Relational Databases in the Context of Digital Repositories

As far as digital repositories are concerned, numerous benefits emerge from the disposal of their contents as Linked Open Data (LOD). This leads more and more repositories towards this direction. However, several factors need to be taken into account in doing so, among which is whether the transition needs to be materialized in real-time or in asynchronous time intervals. In this paper we provide the problem framework in the context of digital repositories, we discuss the benefits and drawbacks of both approaches and draw our conclusions after evaluating a set of performance measurements. Overall, we argue that in contexts with infrequent data updates, as is the case with digital repositories, persistent RDF views are more efficient than real-time SPARQL-to-SQL rewriting systems in terms of query response times, especially when expensive SQL queries are involved.

SensorStream: a semantic real-time stream management system

As data proliferates at increasing rates, the need for real-time stream processing applications increases as well. In the same way that Data Stream Management Systems (DSMS) have emerged from the database community, there is now a similar concern in managing dynamic knowledge among the Semantic Web community. Unfortunately, early relevant approaches are, to a large extent, theoretical and do not present convincing evidence of their efficiency in real dynamic environments. In this paper, we present a framework for the effective, real-time processing of streaming data and we define and analyse in depth its key components. Our framework serves as a basis for the implementation of the SensorStream prototype, on which we run numerous performance and scalability measurements that outline its behaviour and demonstrate its suitability and scalability for solutions that require real-time information processing from distributed and heterogeneous data sources.

Mechanisms for distributed data fusion and reasoning in wireless sensor networks

Decision making in decentralized and dynamic environments is challenging due to the continuous changes in the network topology and the absence of specific nodes that are responsible to take decisions. These challenges are increased in case of sensor networks deployments. In this paper, a novel approach is presented for realizing distributed data fusion and reasoning in wireless sensor networks. The approach is based on the storage and retrieval of data in stable overlay networks that abstract the physical network topology and the design of proper mechanisms for the semantic annotation of the available information in order to be used in the decision making process.

Deploying Linked Open Data: Methodologies and Software Tools

This chapter provides an overview of the methodologies and technologies that support Linked Data designing and publishing. More specifically, this chapter starts with a presentation of the rationale and a discussion about how data can be opened up (i.e. published under an open license). Basic principles are first introduced regarding the cases in which content can be opened up and also, the most common approaches are presented in accomplishing this. Next, we discuss about how data can be modeled, authored, serialized and stored. In this chapter we also provide an overview of the most common technical solutions and widely used software tools that can serve this purpose. Overall, the chapter aims to provide an analysis of the sub-problems into which the Linked Open Data publishing task is to be broken down, namely opening, modeling, linking, processing, and visualizing content, followed by a presentation of the most representative software solutions.

Materializing the Web of Linked Data

This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data. It presents how Linked Data is used for web-scale data integration, information management and search. Special emphasis is given to the publication of Linked Data from relational databases as well as from real-time sensor data streams. The authors also trace the transformation from the document-based World Wide Web into a Web of Data. Materializing the Web of Linked Data is addressed to researchers and professionals studying software technologies, tools and approaches that drive the Linked Data ecosystem, and the Web in general.

Introduction: Linked Data and the Semantic Web

In this chapter, we introduce and discuss the problems that Linked Data solve and the concepts that are related to these problems. We introduce and analyze the basic concepts that are related to the generation of Linked Data and the Semantic Web in general. We provide a brief history of the Semantic Web and the associated evolution of concepts, problem frameworks and solution approaches, all targeted at offering efficient and intelligent solutions to information representation, management and exploitation. More specifically, we introduce the main reasons for the creation of the Semantic Web and the problems that it addresses. Next, we discuss the distinctions between basic terms such as data, information, knowledge, metadata, ontologies, semantic annotations etc. We introduce the notions of interoperability, integration, merging, mapping, and continue with introducing ontologies, reasoners, knowledge bases, all fundamental concepts in the Linked Data ecosystem.

Conclusions: Summary and Outlook

In this Chapter, we summarize and discuss the material presented throughout this book. We recapitulate what is presented and discussed in each Chapter. We discuss the most interesting aspects of the Web of Data landscape, highlighting its main contributions, and then continue with a discussion, mentioning our most important observations, including domain-specific benefits in the LOD domain. We conclude the Chapter with a discussion of open research challenges in the Linked Data domain.