Alessandro Oltramari

Sweetening Ontologies with DOLCE

In this paper we introduce the DOLCE upper level ontology, the first module of a Foundational Ontologies Library being developed within the WonderWeb project. DOLCE is presented here in an intuitive way; the reader should refer to the project deliverable for a detailed axiomatization. A comparison with WordNets top-level taxonomy of nouns is also provided, which shows how DOLCE, used in addition to the OntoClean methodology, helps isolating and understanding some major WordNet?s semantic limitations. We suggest that such analysis could hopefully lead to an ?ontologically sweetened? WordNet, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.

Conceptual analysis of lexical taxonomies: the case of WordNet top-level

In this paper we propose an analysis and an upgrade of WordNets top-level synset taxonomy. We briefly review WordNet and identify its main semantic limitations. Some principles from a forthcoming OntoClean methodology are applied to the ontological analysis of WordNet. A revised top-level taxonomy is proposed, which is meant to be more conceptually rigorous, cognitively transparent, and efficiently exploitable in several applications.

Ontology and the lexicon : a natural language processing perspective

Part I. Fundamental Aspects: 1. Ontology and the lexicon: a multi-disciplinary perspective Laurent Prevot, Chu-Ren Huang, Nicoletta Calzolari, Aldo Gangemi, Alessandro Lenci and Alessandro Oltramari 2. Formal ontology as interlingua: the SUMO and WordNet linking project and GlobalWordNet Adam Pease and Christiane Fellbaum 3. Interfacing WordNet with DOLCE: towards OntoWordNet Aldo Gangemi, Nicola Guarino, Claudio Masolo and Alessandro Oltramari 4. Reasoning over natural language text by means of FrameNet and ontologies Jan Scheffczyk, Collin F. Baker and Srini Narayanan 5. Synergizing ontologies and the lexicon: a roadmap Alessandro Oltramari, Aldo Gangemi, Chu-Ren Huang, Nicoletta Calzolari, Alessandro Lenci and Laurent Prevot Part II. Discovery and Representation of Conceptual Systems: 6. Experiments of ontology construction with formal concept analysis SuJian Li, Qin Lu and Wenjie Li 7. Ontology, lexicon, and fact repository as leveraged to interpret events of change Marjorie McShane, Sergei Nirenburg and Stephen Beale 8. Hantology: conceptual system discovery based on orthographic convention Ya-Min Chou and Chu-Ren Huang 9. Whats in a schema? A formal metamodel for ECG and FrameNet Aldo Gangemi Part III. Interfacing Ontologies and Lexical Resources: 10. Interfacing ontologies and lexical resources Laurent Prevot, Stefano Borgo and Alessandro Oltramari 11. Sinica BOW (Bilingual Ontological WordNet): integration of BilingualWord-Net and SUMO Chu-Ren Huang, Ru-Yng Chang and Hsiang-bin Lee 12. Ontology-based semantic lexicons: mapping between terms and object descriptions Paul Buitelaar 13. Merging global and specialized linguistic ontologies Manuela Speranza and Bernardo Magnini Part IV. Learning and Using Ontological Knowledge: 14. The life cycle of knowledge Alessandro Lenci 15. The omega ontology Andrew Philpot, Eduard Hovy and Patrick Pantel 16. Automatic acquisition of lexico-semantic knowledge for question answering Lonneke van der Plas, Gosse Bouma and Jori Mur 17. Agricultural ontology construction and maintenance in Thai Asanee Kawtrakul and Aurawan Imsombut.The relation between ontologies and language is at the forefront of both natural language processing (NLP) and knowledge engineering. Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to map accurately the relations between words and the concepts that they can be linked to. This book focuses on the integration of lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

Interfacing ontologies and lexical resources

During the last few years, a number of works aiming at interfacing ontologies and lexical resources have been initiated. This paper aims at clarifying the current picture of this domain. It compares ontologies built following different methodologies and analyses their combination with lexical resources. A point defended in the paper is that different methodologies lead to very different characteristics for the resulting resources. We classify these methodologies show how actual projects fit into this classification.

Knowledge as a measurable object in business contexts: a stock-and-flow approach

This paper addresses the issue of knowledge accounting in business contexts. Despite the efforts of researchers and practitioners, the approaches that have been proposed are different, have several limitations, and their soundness and usefulness have been questioned. Moving from a critical review of the state-of-the art of the methods of knowledge accounting already proposed in the knowledge management literature, the paper illustrates an approach to account for knowledge based on a recognition and measurement of knowledge objects flowing from the stocks of different economic players. This approach can allow a measurement of knowledge generated, possessed, and exchanged by companies, based on charts and indicators similar to those employed in traditional accounting. This can improve the interpretation and comparability of economic measures of knowledge in different business cases and over time.

Cognition and Technology

As the previous chapters emphasized, the human cognition—and the technology necessary to support it—are central to Cyber Situational Awareness. Therefore, this chapter focuses on challenges and approaches to integration of information technology and computational representations of human situation awareness. To illustrate these aspects of CSA, the chapter uses the process of intrusion detection as a key example. We argue that effective development of technologies and processes that produce CAS in a way properly aligned with human cognition calls for cognitive models—dynamic and adaptable computational representations of the cognitive structures and mechanisms involved in developing SA and processing information for decision making. While visualization and machine learning are often seen among the key approaches to enhancing CSA, we point out a number of limitations in their current state of development and applications to CSA. The current knowledge gaps in our understanding of cognitive demands in CSA include the lack of a theoretical model of cyber SA within a cognitive architecture; the decision gap, representing learning, experience and dynamic decision making in the cyberspace; and the semantic gap, addressing the construction of a common language and a set of basic concepts about which the security community can develop a shared understanding.

Senso Comune: A Collaborative Knowledge Resource for Italian

Senso Comune is an open knowledge base for the Italian language, available through a Web-based collaborative platform, whose construction is in progress. The resource integrates dictionary data coming from both users and legacy resources with an ontological backbone, which provides foundations for a formal characterization of lexical semantic structures (frames). A nucleus of basic Italian lemmas, which have been semantically analyzed and classified, is available for both online access and download. A restricted community of contributors is currently working on increasing the lexical coverage of the resource.

Pursuing artificial general intelligence by leveraging the knowledge capabilities of ACT-R

Intelligence is a multifaceted phenomenon which makes trying to capture its very essence a slippery task. In this paper, we commit to a hybrid notion of intelligence, conceived as the combination of cognitive operations and knowledge resources that leads to purposeful behavior. Accordingly, this paper describes an artificial system that benefits from both mechanism---centered and knowledge---centered approaches. In particular, the system integrates the ACT-R cognitive architecture with SCONE, a knowledge-based system for ontological reasoning, to combine ACT-Rs subsymbolic cognitive mechanisms with SCONEs knowledge representation and inference capabilities. We apply the hybrid system to computationally approximate human intelligent behavior in a task of visual recognition.

Extending cognitive architectures with semantic resources

This paper presents an integrated modeling framework where the learning and knowledge retrieval mechanisms of the ACT-R cognitive architecture are combined with a semantic resource. We aim to extend ACT-R with a scalable knowledge model, in order to support sub-symbolic processes with consistent, general high-level declarative representations. Design principles, methodology and implementation examples are provided.

Using knowledge as an object: challenges and implications

Knowledge Management Research & Practice (2012) 10, 202–205. doi:10.1057/kmrp.2012.32 Introduction The discipline of knowledge management (KM) has developed around practical purposes that have, nonetheless, important implications in conceptual and theoretical terms. One of these implications is the notion of knowledge that can or must be adopted. The possibility of designing proper methods and tools to facilitate effective knowledge transfer between individuals and organisations, to allow storage of knowledge in some kind of repository and to facilitate retrieval and reuse of knowledge requires a definition of knowledge, which must be, at the same time, conceptually sound and applicable to practical purposes. In many respects, this is a binding condition: it can be argued that all KM solutions imply an explicit or implicit notion of knowledge. However, the elusive nature of knowledge itself, the complexity and multidisciplinary nature of KM and the variety of its application fields have led to the definition and use of a wide range of notions of knowledge and its possible attributes (Holsapple, 2003; Andriessen, 2008). Consequently, there has been an intense discussion on what can be the most appropriate definition of knowledge to employ in the KM field. The discussion on the different types of knowledge, their interactions and the forms in which they can be cast is very animated today. Over the years, this discussion has made clear the complexity of the meanings one usually ascribes to the term ‘knowledge’. Starting from this observation, this Special Issue aims to contribute to the debate in an alternative way. Indeed, we did not ask whether there was the need for a single shared definition of knowledge nor what this definition could be. Considering the substantially practical purpose of KM, reaching a general consensus on a specific notion of knowledge can be less important than selecting one notion of knowledge on a case-by-case basis, provided that this notion is functional to the particular KM solution that one has to develop and apply. In other words, rather than looking for a theoretically sound and ‘universal’ definition of knowledge that satisfies any possible criteria, we argue that it is more suitable and fruitful to adopt a family of notions from which one can select the most appropriate for the application field at stake. Also, this multiple approach becomes feasible when one adopts modern techniques for concept analysis and classification as provided by the area of applied ontology (Staab & Studer, 2009). This position reflected in the Call for Papers for this Special Issue, is compatible with, if not directly adopted by, the approaches proposed in the papers published in it.