Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

1.. HARD CASES 5. Legal Rights A. Legislation . . . We might therefore do well to consider how a philosophical judge might develop, in appropriate cases, theories of what legislative purpose and legal principles require. We shall find that he would construct these theories in the same manner as a philosophical referee would construct the character of a game. I have invented, for this purpose, a lawyer of superhuman skill, learning, patience and acumen, whom I shall call Hercules. I suppose that Hercules is a judge in some representative American jurisdiction. I assume that he accepts the main uncontroversial constitutive and regulative rules of the law in his jurisdiction. He accepts, that is, that statutes have the general power to create and extinguish legal rights, and that judges have the general duty to follow earlier decisions of their court or higher courts whose rationale, as l

Taking Rights Seriously

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

Taxonomies play a central role in conceptual domain modeling having a direct impact in areas such as knowledge representation, ontology engineering, software engineering, as well as in knowledge organization in information sciences. Despite their key role, there is in the literature little guidance on how to build high-quality taxonomies, with notable exceptions such as the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules for forming taxonomic structures. In this paper, we show how to leverage on the formal rules underlying these techniques to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. To validate our proposal, we formalize these operations as a graph grammar. Moreover, to demonstrate our claim of correctness by construction, we use automatic verification techniques over the grammar language to show that: (i) all taxonomies produced by the grammar rules are correct; and (ii) the rules can generate all correct taxonomies.

Building Correct Taxonomies with a Well-Founded Graph Grammar.

Risk Propagation (RP) is a central technique that allows the calculation of the cascading effect of risk within a system. At the current state, there is a lack of risk propagation solutions that can be used to assess the impact of risk at different levels of abstraction, accounting for actors, processes, physical-digital objects, and their relations. To fill this gap, in this paper, we propose a process-aware risk propagation approach that builds on two main components: i. an ontology, which supports functionalities typical of Semantic Web technologies (SWT), and ii. an ad hoc method to calculate the propagation of risk within the given system. We implemented our approach in a proof-of-concept tool, which was validated in the cybersecurity domain.

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An Ontology-Driven Approach for Process-Aware Risk Propagation

Ontological foundations for trust management: Extending the reference ontology of trust

Forward engineering relational schemas based on conceptual models is an established practice, with a number of commercial tools available and widely used in production settings. These tools employ automated transformation strategies to address the gap between the primitives offered by conceptual modeling languages (such as ER and UML) and the relational model. Despite the various benefits of automated transformation, once a database schema is obtained, data access is usually undertaken by relying on the resulting schema, at a level of abstraction lower than that of the source conceptual model. Data access then requires both domain knowledge and comprehension of the (non-trivial) technical choices embodied in the resulting schema. We address this problem by forward engineering not only a relational schema, but also creating an ontology-based data access mapping for the resulting schema. This mapping is used to expose data in terms of the original conceptual model, and hence queries can be written at a high level of abstraction, independently of the transformation strategy selected.

Forward Engineering Relational Schemas and High-Level Data Access from Conceptual Models.

In this very issue, Augusto (2022) formulates two requirements upon which to evaluate the adequacy of a foundational ontology. Speciﬁcally, the ontological categories: (i) should be understood as the most general kinds of things and (ii) are organized in a non-overlapping ﬁnite hierarchy. On the basis of such constraints, he claims that most existing foundational ontologies engineered in the context of Applied Ontology, including the UFO-B ontology, are inadequate. In this article, ﬁrst we show that his objection against UFO-B can be dissipated by pointing to a trivial terminological confusion. We then argue that his two constraints are not plausible. Then, we show that the disagreement between our point of view and Augusto’s framework is not restricted to those two constraints but extends over the notion of conceptualization of reality.

Giancarlo Guizzardi

Papers

Building Correct Taxonomies with a Well-Founded Graph Grammar.

An Ontology-Driven Approach for Process-Aware Risk Propagation

Ontological foundations for trust management: Extending the reference ontology of trust

Forward Engineering Relational Schemas and High-Level Data Access from Conceptual Models.

On the Adequacy of Requirements for Foundational Ontologies