Personalized Information Retrieval in a Semantic-Based Learning Environment

Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently construct knowledge from information sources. Personalized and customizable access on digital materials collected from the Web according to one’s own personal requirements and interests is an example of active learning. Moreover, it is also necessary to provide techniques to locate suitable materials. In this chapter, we introduce a personalized learning environment providing intelligent support to achieve the expectations of active learning. The system exploits collaborative and semantic approaches to extract concepts from documents, and maintaining user and resources profiles based on domain ontologies. In such a way, the retrieval phase takes advantage of the common knowledge base used to extract useful knowledge and produces personalized views of the learning system.

Towards Semantic-Based P2P Reputation Systems

Peer-to-peer (P2P) systems represent nowadays a large portion of Internet traffic, and are fundamental data sources. In a pure P2P system, since no peer has the power or responsibility to monitor and restrain others behaviours, there is no method to verify the trustworthiness of shared resources, and malicious peers can spread untrustworthy data objects to the system. Furthermore, data descriptions are often simple features directly connected to data or annotations based on heterogeneous schemas, a fact that makes difficult to obtain a single coherent trust value on a resource. This chapter describes techniques where the combination of Semantic Web and peer-to-peer technologies is used for expressing the knowledge shared by peers in a well-defined and formal way. Finally, dealing with Semantic-based P2P networks, the chapter suggests a research effort in this direction, where the association between cluster-based overlay networks and reputation systems based on numerical approaches seems to be promising.

Search Engine

This chapter presents technologies and approaches for information retrieval in a knowledge base. We intend to show that the use of ontology for domain representation and knowledge search offers a more efficient approach for knowledge management. This approach focuses on the meaning of the word, thus becoming an important element in the building of the Semantic Web. The search based on both keywords and ontology allows more effective information retrieval exploiting the Semantic of the information in a variety of data. We present a method for taxonomy building, annotating, and searching documents with taxonomy concepts. We also describe our experience in the creation of an informal taxonomy, the automatic classification, and the validation of search results with traditional measures, such as precision, recall and f-measure.

Introduction to Ontology Engineering

This chapter provides an introduction to ontology engineering discussing the role of ontologies in informative systems, presenting a methodology for ontology design, and introducing ontology languages. The chapter starts by explaining why ontologies are needed in informative systems, then it introduces the reader to ontologies by leading him/her in a stepwise guide to ontology design. It concludes by introducing ontology languages and standards. This is a primer reading aimed at preparing novice readers of this book to understanding more complex dissertations; for this reason it can be avoided by expert readers.

OntoExtractor

This chapter introduces OntoExtractor, a tool for the semi-automatic generation of the taxonomy from a set of documents or data sources. The tool generates the taxonomy in a bottom-up fashion. Starting from structural analysis of the documents, it produces a set of clusters, which can be refined by a further grouping created by content analysis. Metadata describing the content of each cluster is automatically generated and analysed by the tool for producing the final taxonomy. A simulation of a tool, based on an implicit and explicit voting mechanism, for the maintenance of the taxonomy is also described. The author depicts a system that can be used to generate the taxonomy from a heterogeneous source of information, using wrappers for converting the original format of the document to a structured one. This way, OntoExtractor can virtually generate the taxonomy from any source of information just adding the proper wrapper. Moreover, the trust mechanism allows a reliable method for maintaining the taxonomy and for overcoming the unavoidable generation of wrong classes in the taxonomy.

K-link+

This chapter introduces a distributed framework for OKM (Organizational Knowledge Management) which allows IKWs (Individual Knowledge Workers) to build virtual communities that manage and share knowledge within workspaces. The proposed framework, called K-link+, supports the emergent way of doing business of IKWs, which allows users to work at any time from everywhere, by exploiting the VO (Virtual Office) model. Moreover, since semantic aspects represent a key point in dealing with organizational knowledge, K-link+ is supported by an ontological framework composed of: (i) an UO (Upper Ontology), which defines a shared common background on organizational knowledge domains; (ii) a set of UO specializations, namely Workspace Ontologies or Personal Ontologies, that can be used to manage and search content; (iii) a set of COKE (Core Organizational Knowledge Entities) which provides a shared definition of human resources, technological resources, knowledge objects, services; and (iv) an annotation mechanism that allows users to create associations between ontology concepts and knowledge objects. K-link+ features a hybrid (partly centralized and partly distributed) protocol to guarantee the consistency of shared knowledge and a distributed voting mechanism to foster the evolution of ontologies on the basis of user needs.

Approaches to Semantics in Knowledge Management

There are different approaches to modeling a computational system, each providing different semantics. We present a comparison among different approaches to semantics and we aim at identifying which peculiarities are needed to provide a system with uniquely interpretable semantics. We discuss different approaches, namely, Description Logics, Artificial Neural Networks, and relational database management systems. We identify classification (the process of building a taxonomy) as common trait. However, in this chapter we also argue that classification is not enough to provide a system with a Semantics, which emerges only when relations among classes are established and used among instances. Our contribution also analyses additional features of the formalisms that distinguish the approaches: closed versus. open world assumption, dynamic versus. static nature of knowledge, the management of knowledge, and the learning process.

KIWI

Research on semantic-aware knowledge management provides new solutions, technologies, and methods to manage organizational knowledge. These solutions open new opportunities to “virtual challenges” as e-collaboration, e-business, e-learning and e-government. The research carried out for the KIWI (Knowledge-based Innovation for the Web Infrastructure) project is focused on the strategies for the current Web evolution in the more powerful Semantic Web, where formal semantic representation of resources enables a more effective knowledge sharing. The first pillar of the KIWI framework concerns development of ontologies as a metadata layer. Resources can be formally and semantically annotated with these metadata, while search engines or software agents can use them for retrieving the right information item or applying their reasoning capabilities. The second pillar of the KIWI framework is focused on the semantic search engine. Their capabilities and functionalities have to be improved in order to take advantage of the new semantic descriptions. A set of prototypal tools that enable knowledge experts to produce a semantic knowledge management system was delivered by the project. The KIWI framework and tools are applied in some projects for designing and developing knowledge-based platforms with positive results.

Formalizing and Leveraging Domain Knowledge in the K4CARE Home Care Platform

This chapter proposes an agent-based architecture for home care support, whose main capability is to continuously admit and apply new medical knowledge entered into the system, capturing and codifying implicit knowledge deriving from the medical staff. Knowledge is the fundamental catalyst in all application domains, and this is particularly true especially for the medical context. Knowledge formalization, representation, exploitation, creation, and sharing are some of the most complex issues related to Knowledge Management. Moreover, Artificial Intelligence techniques and MAS (Multi-Agent System) in health care are increasingly justifying the large demand for their application, since traditional techniques are often not suitable to manage complex tasks or to adapt to unexpected events. The chapter presents also a methodology for approaching medical knowledge management from its representation symbolism to the implementation details. The codification of health care treatments, as well as the formalization of domain knowledge, serves as an explicit, a priori asset for the agent platform implementation. The system has the capability of applying new, implicit knowledge emerging from physicians.

Utilizing Semantic Web and Software Agents in a Travel Support System

The use of Semantic Web technologies in e-business is hampered by the lack of large, publicly-available sources of semantically-demarcated data. In this chapter, we present a number of intermediate steps on the road toward the Semantic Web. Specifically, we discuss how Semantic Web technologies can be adapted as the centerpiece of an agent-based travel support system. First, we present a complete description of the system under development. Second, we introduce ontologies developed for, and utilized in, our system. Finally, we discuss and illustrate through examples how ontologically demarcated data collected in our system is personalized for individual users. In particular, we show how the proposed ontologies can be used to create, manage, and deploy functional user profiles.