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.

/pdf/situations-and-attitudes-57f065zg2d.pdf

Situations and Attitudes.

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

Corrigendum to Ontological anti-patterns

Game theory is largely about interactions of agents whose decisions affect each other. The combination of agents’ actions corresponds to outcomes, which may impact agents’ goals, either positively or negatively. The analysis of the probable consequences and expected utilities of all possible outcomes is fundamental to support agents when deciding whether to engage in a certain strategy. In this paper, we move in this direction by initiating the investigation of the ontological foundation of the emergence of value and risk from outcomes in game theoretical approaches. In order to understand the influence of these forces over outcomes, a precise and rigorous conceptualization, based on foundational ontologies, is needed. To this aim, we present and discuss here a preliminary ontological modeling of basic concepts of game theory, as well as the relation to value, risk and outcomes.

Modeling the Emergence of Value and Risk in Game Theoretical Approaches

This paper explores the use of „process-related models‟ – such as Enterprise Architecture (EA) models – as non-ontological resources (NORs) in the Ontology Engineering (OE) trajectory. These models are commonly available in enterprise repositories in process-rich social domains (e.g., e-Government, finance, software engineering, manufacturing), and serve as valuable sources of consolidated knowledge. We focus on the role of EA models in supporting what we are naming here Early Ontology Engineering, comprising the phases of purpose and scope identification as well as the identification of functional requirements for creating domain ontologies. This is because these models characterize, among other aspects, the organizational context and the business motivations/goals. Therefore, they may facilitate the identification of intended uses/purpose of an ontology to be integrated to the EA, as a means to address goals of the organization stakeholders. We show how this approach is being applied in a real-world e-Government project in the Public Security Domain.

/pdf/where-enterprise-architecture-and-early-ontology-engineering-23zx314xsk.pdf

Where Enterprise Architecture and Early Ontology Engineering Meet: A Case Study in the Public Security Domain

In recent years, disruptive technologies have advanced at a rapid pace. These new developments have the power to accelerate the production and delivery, improve the quality, and reduce the costs of goods and services, as well as to contribute to individual and collective well-being. However, their adoption relies largely on user trust. And trust, due to its dynamic nature, is fragile. Therefore, just as important as to build trust is to maintain it. To build sustainable trust it is fundamental to understand the composition of trust relations and what factors can influence them. To address this issue, in this paper, we provide ontological foundations for trust dynamics. We extend our previous work, the Reference Ontology of Trust (ROT), to clarify and provide a deeper account of some building blocks of trust as well as the many factors that can influence trust relations. We illustrate the working of ROT by applying it to a real case study concerning citizens’ trust in central bank digital currency ecosystems, which has been conducted in close collaboration with a national central bank. 

/pdf/ontological-foundations-for-trust-dynamics-the-case-of-1gq9agxw.pdf

Giancarlo Guizzardi

Papers

Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

Corrigendum to Ontological anti-patterns

Modeling the Emergence of Value and Risk in Game Theoretical Approaches

Where Enterprise Architecture and Early Ontology Engineering Meet: A Case Study in the Public Security Domain

Ontological Foundations for Trust Dynamics: The Case of Central Bank Digital Currency Ecosystems