On the emergent Semantic Web and overlooked issues

The emergent Semantic Web, despite being in its infancy, has already received a lotof attention from academia and industry. This resulted in an abundance of prototype systems and discussion most of which are centred around the underlying infrastructure. However, when we critically review the work done to date we realise that there is little discussion with respect to the vision of the Semantic Web. In particular, there is an observed dearth of discussion on how to deliver knowledge sharing in an environment such as the Semantic Web in effective and efficient manners. There are a lot of overlooked issues, associated with agents and trust to hidden assumptions made with respect to knowledge representation and robust reasoning in a distributed environment. These issues could potentially hinder further development if not considered at the early stages of designing Semantic Web systems. In this perspectives paper, we aim to help engineers and practitioners of the Semantic Web by raising awareness of these issues

SEMANTIC DATA CLOUDING OVER THE WEBS

Very often, for business or personal needs, users require to retrieve, in a very fast way, all the available relevant information about a focused target entity, in order to take decisions, organize business work, plan future actions. To answer this kind of \u201centity\u201d- driven user needs, a huge multiplicity of web resources is actually available, coming from the Social Web and related user-centered services (e.g., news publishing, social networks, microblogging systems), from the Semantic Web and related ontologies and knowledge repositories, and from the conventional Web of Documents. The Ph.D. thesis is devoted to define the notion of in-cloud and a semantic clouding approach for the construction of in-clouds that works over the Social Web, the Semantic Web, and the Web of Documents. in-clouds are built for a target entity of interest to organize all relevant web resources, modeled as web data items, into a graph, on the basis of their level of prominence and reciprocal closeness. Prominence captures the importance of a web resource within the in-cloud, by distinguishing, also in a visual way \u201ca la tagcloud\u201d, how much relevant web resources are with respect to the target entity. The level of closeness between web resources is evaluated using matching and clustering techniques, with the goal of determining how similar web resources are to each other and with respect to the target entity

Explanation and diagnosis services for unsatisfiability and inconsistency in description logics

Description Logics (DLs) are a family of knowledge representation formalisms with formal semantics and well understood computational complexities. In recent years, they have found applications in many domains, including domain modeling, software engineering, configuration, and the Semantic Web. DLs have deeply influenced the design and standardization of the Web Ontology Language OWL. The acceptance of OWL as a web standard has reciprocally resulted in the widespread use of DL ontologies on the web. As more applications emerge with increasing complexity, non-standard reasoning services, such as explanation and diagnosis, have become important capabilities that a DL reasoner should provide. For example, unsatisfiability and inconsistency may arise in an ontology due to unintentional design defects or changes in the ontology evolution process. Without explanations, searching for the cause is like looking for a needle in a haystack. It is, therefore, surprising that most of the existing DL reasoners do not provide explanation services; they provide "Yes/No" answers to satisfiability or consistency queries without giving any reasons. This thesis presents our solution for providing explanation and diagnosis services for DL reasoners. We firstly propose a framework based on resolution to explain inconsistency and unsatisfiability in Description Logic. A sound and complete algorithm is developed to generate explanations for the DL language [Special characters omitted.] ALCHI based on the unsatisfiability and inconsistency patterns in [Special characters omitted.] ALCHI . We also develop a technique based on Shapley values to measure inconsistencies in ontologies for diagnosis purposes. This measure is used to identify which axioms in an input ontology or which parts of these axioms need to be repaired in order to make the input consistent. We also investigate optimization techniques to compute the inconsistency measures based on particular properties of DLs. Based on the above theoretical foundations, a running prototype system is implemented to evaluate the practicability of the proposed services. Our preliminary empirical results show that the resolution based explanation framework and the diagnosis procedure based on inconsistency measures can be applied in the real world application

Reasoning with Contexts in Description Logics

Harmelen, F.A.H. van [Promotor]Schlobach, K.S. [Copromotor

Dwelling on ontology - semantic reasoning over topographic maps

The thesis builds upon the hypothesis that the spatial arrangement of topographic features, such as buildings, roads and other land cover parcels, indicates how land is used. The aim is to make this kind of high-level semantic information explicit within topographic data. There is an increasing need to share and use data for a wider range of purposes, and to make data more definitive, intelligent and accessible. Unfortunately, we still encounter a gap between low-level data representations and high-level concepts that typify human qualitative spatial reasoning. The thesis adopts an ontological approach to bridge this gap and to derive functional information by using standard reasoning mechanisms offered by logic-based knowledge representation formalisms. It formulates a framework for the processes involved in interpreting land use information from topographic maps. Land use is a high-level abstract concept, but it is also an observable fact intimately tied to geography. By decomposing this relationship, the thesis correlates a one-to-one mapping between high-level conceptualisations established from human knowledge and real world entities represented in the data. Based on a middle-out approach, it develops a conceptual model that incrementally links different levels of detail, and thereby derives coarser, more meaningful descriptions from more detailed ones. The thesis verifies its proposed ideas by implementing an ontology describing the land use ‘residential area’ in the ontology editor Protégé. By asserting knowledge about high-level concepts such as types of dwellings, urban blocks and residential districts as well as individuals that link directly to topographic features stored in the database, the reasoner successfully infers instances of the defined classes. Despite current technological limitations, ontologies are a promising way forward in the manner we handle and integrate geographic data, especially with respect to how humans conceptualise geographic space