Design Diagrams as Ontological Sources

Ontology is a basic building block for the semantic web. An active line of research in semantic web is focused on how to build and evolve ontologies using the information from different ontological sources inherent in the domain. A large part of the IT industry uses software engineering methodologies to build software solutions that solve real-world problems. For them, instead of creating solutions from scratch, reusing previously built software as much as possible is a business-imperative today. As part of their projects, they use design diagrams to capture various facets of the software development process. We discuss how semantic web technologies can help solution-building organizations achieve software reuse by first learning ontologies from design diagrams of existing solutions and then using them to create design diagrams for new solutions. Our technique, called OntExtract, extracts domain ontology information (entities and their relationship(s)) from class diagrams and further refines the extracted information using diagrams that express dynamic interactions among entities such as sequence diagram. A proof of concept implementations is also developed as a Plug-in over a commercial development environment IBM’s Rational Software Architect.

Applying Semantic Web to E-Tourism

Traditional E-Tourism applications store data internally in a form that is not interoperable with similar systems. Hence, tourist agents spend plenty of time updating data about vacation packages in order to provide good service to their clients. On the other hand, their clients spend plenty of time searching for the ‘perfect’ vacation package as the data about tourist offers are not integrated and are available from different spots on the Web. We developed Travel Guides - a prototype system for tourism management to illustrate how semantic web technologies combined with traditional E-Tourism applications: a.) help integration of tourism sources dispersed on the Web b) enable creating sophisticated user profiles. Maintaining quality user profiles enables system personalization and adaptivity of the content shown to the user. The core of this system is in ontologies – they enable machine readable and machine understandable representation of the data and more importantly reasoning.

SWARMS

This chapter describes the architecture and the main features of SWARMS, a platform for domain knowledge management. The platform aims at providing services for 1) efficiently storing and accessing the ontological information; 2) visualizing the networking structure in the ontological data; 3) searching and mining the semantic data. One advantage of the system is that it provides a suite of components for not only supporting efficient semantic data storage but also searching and mining the semantics. Another advantage is that the system supports visualization in the process of search and mining, which would greatly help a normal user to understand the knowledge inside the ontological data. SWARMS can be easily customized to adapt to different domains. The system has been applied to several domains, such as News, Software, and Social Network. In this chapter, we will also present the performance evaluations of the system.

Modeling and Querying XMLBased P2P Information Systems

Knowledge representation and management techniques can be efficiently used to improve data modeling and IR functionalities of P2P Information Systems, which have recently attracted a lot of attention from both industrial and academic research communities. These functionalities can be achieved by pushing semantics in both data and queries, and exploiting the derived expressiveness to improve file sharing primitives and lookup mechanisms made available by first-generation P2P systems. XML-based P2P Information Systems are a more specific instance of this class of systems, where the overall data domain is composed by very large, Internet-like distributed XML repositories from which users extract useful knowledge by means of IR methods implemented on top of XML join queries against the repositories. In this chapter, we first focus our attention on the definition and the formalization of the XML-based P2P Information Systems class, also deriving interesting properties on such systems, and then we present a knowledge-representation-and-management-based framework, enriched via semantics, that allows us to efficiently process knowledge and support advanced IR techniques in XML-based P2P Information Systems, thus achieving the definition of the so-called Semantically-Augmented XML-based P2P Information Systems. Also, we complete our analytical contribution with an experimental evaluation of our framework against state-of-the-art IR techniques for P2P networks, and its theoretical analysis in comparison with other similar semantics-based proposals.

Automatic Semantic Annotation Using Machine Learning

This chapter aims to give a thorough investigation of the techniques for automatic semantic annotation. The Semantic Web provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. However, lack of annotated semantic data is a bottleneck to make the Semantic Web vision a reality. Therefore, it is indeed necessary to automate the process of semantic annotation. In the past few years, there was a rapid expansion of activities in the semantic annotation area. Many methods have been proposed for automating the annotation process. However, due to the heterogeneity and the lack of structure of the Web data, automated discovery of the targeted or unexpected knowledge information still present many challenging research problems. In this chapter, we study the problems of semantic annotation and introduce the state-of-the-art methods for dealing with the problems. We will also give a brief survey of the developed systems based on the methods. Several real-world applications of semantic annotation will be introduced as well. Finally, some emerging challenges in semantic annotation will be discussed.

Ontologies and Intelligent Agents

The emerged form of information with computer-processable meaning (semantics) as presented in the framework of the Semantic Web (SW) facilitates machines to access it more efficiently. Information is semantically annotated in order to ease the discovery and retrieval of knowledge. Ontologies are the basic element of the SW. They carry knowledge about a domain and enable interoperability between different resources. Another technology that draws considerable attention nowadays is the technology of Intelligent Agents. Intelligent agents act on behalf of a user to complete tasks and may adapt their behavior to achieve their objectives. The objective of this chapter is to provide an exhaustive description of fundamentals regarding the combination of SW and intelligent agent technologies.

Improving Storage Concepts for Semantic Models and Ontologies

Ontologies are more commonly used today but still little consideration is given of how to efficiently store them. The proposed approach is built on reliable and efficient relational database management systems (RDBMS). It can be easily implemented for other systems and due to its vendor independence existing data can be migrated from one RDBMS to another relatively easy.

Probabilistic Models for the Semantic Web

Recently, there has been an increasing interest in formalisms for representing uncertain information on the Semantic Web. This interest is triggered by the observation that knowledge on the web is not always crisp and we have to be able to deal with incomplete, inconsistent and vague information. The treatment of this kind of information requires new approaches for knowledge representation and reasoning on the web as existing Semantic Web languages are based on classical logic which is known to be inadequate for representing uncertainty in many cases. While different general approaches for extending Semantic Web languages with the ability to represent uncertainty are explored, we focus our attention on probabilistic approaches. We survey existing proposals for extending semantic web languages or formalisms underlying Semantic Web languages in terms of their expressive power, reasoning capabilities as well as their suitability for supporting typical tasks associated with the Semantic Web.

Paving the Way to an Effective and Efficient Retrieval of Data over Semantic Overlay Networks

In a Peer-to-Peer (P2P) system, a Semantic Overlay Network (SON) models a network of peers whose connections are influenced by the peers’ content, so that semantically related peers connect with each other. This is very common in P2P communities, where peers share common interests, and a peer can belong to more than one SON, depending on its own interests. Querying such a kind of systems is not an easy task: The retrieval of relevant data can not rely on flooding approaches which forward a query to the overall network. A way of selecting which peers are more likely to provide relevant answers is necessary to support more efficient and effective query processing strategies. This chapter presents a semantic infrastructure for routing queries effectively in a network of SONs. Peers are semantically rich, in that peers’ content is modelled with a schema on their local data, and peers are related each other through semantic mappings defined between their own schemas. A query is routed through the network by means of a sequence of reformulations, according to the semantic mappings encountered in the routing path. As reformulations may lead to semantic approximations, we define a fully distributed indexing mechanism which summarizes the semantics underlying whole subnetworks, in order to be able to locate the semantically best directions to forward a query to. In support of our proposal, we demonstrate through a rich set of experiments that our routing mechanism overtakes algorithms which are usually limited to the only knowledge of the peers directly connected to the querying peer, and that our approach is particularly successful in a SONs scenario.

Contextual Hierarchy Driven Ontology Learning

Research in ontology learning had always separated between ontology building and evaluation tasks. Moreover, it had used for example a sentence, a syntactic structure or a set of words to establish the context of a word. However, this research avoids accounting for the structure of the document and the relation between the contexts. In our work, we combine these elements to generate an appropriate context definition for each word. Based on the context, we propose an unsupervised hierarchical clustering algorithm that, in the same time, extracts and evaluates the ontological concepts. Our results show that our concept discovery approach improves the conceptual quality and the relevance of the extracted ontological concepts, provides a support for the domain experts and facilitates the evaluation task for them.