Representing Concepts by Weighted Formulas

A concept is traditionally defined via the necessary and sufficient conditions that clearly determine its extension. By contrast, cognitive views of concepts intend to account for empirical data that show that categorisation under a concept presents typicality effects and a certain degree of indeterminacy. We propose a formal language to compactly represent concepts by leveraging on weighted logical formulas. In this way, we can model the possible synergies among the qualities that are relevant for categorising an object under a concept. We show that our proposal can account for a number of views of concepts such as the prototype theory and the exemplar theory. Moreover, we show how the proposed model can overcome some limitations of cognitive views

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Non classical concept representation and reasoning in formal ontologies

Formal ontologies are nowadays widely considered a standard tool for knowledge representation and reasoning in the Semantic Web. In this context, they are expected to play an important role in helping automated processes to access information. Namely: they are expected to provide a formal structure able to explicate the relationships between different concepts/terms, thus allowing intelligent agents to interpret, correctly, the semantics of the web resources improving the performances of the search technologies. Here we take into account a problem regarding Knowledge Representation in general, and ontology based representations in particular; namely: the fact that knowledge modeling seems to be constrained between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In particular, most common sense concepts seem not to be captured by the stringent semantics expressed by such formalisms as, for example, Description Logics (which are the formalisms on which the ontology languages have been built). The aim of this work is to analyse this problem, suggesting a possible solution suitable for formal ontologies and semantic web representations. The questions guiding this research, in fact, have been: is it possible to provide a formal representational framework which, for the same concept, combines both the classical modelling view (accounting for compositional information) and defeasible, prototypical knowledge ? Is it possible to propose a modelling architecture able to provide different type of reasoning (e.g. classical deductive reasoning for the compositional component and a non monotonic reasoning for the prototypical one)? We suggest a possible answer to these questions proposing a modelling framework able to represent, within the semantic web languages, a multilevel representation of conceptual information, integrating both classical and non classical (typicality based) information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of representation

Dual PECCS: A Cognitive System for Conceptual Representation and Categorization

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation

Extending ontological categorization through a dual process conceptual architecture

In this work we present a hybrid knowledge representation system aiming at extending the representational and reasoning capabilities of classical ontologies by taking into account the theories of typicality in conceptual processing. The system adopts a categorization process inspired to the dual process theories and, from a representational perspective, is equipped with a heterogeneous knowledge base that couples conceptual spaces and ontological formalisms. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves the representational and reasoning \ue2\u80\u9cconceptual\ue2\u80\u9d capabilities of standard ontology-based systems

A formal model for fuzzy ontologies.

Au Yeung Ching Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 97-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Semantic Web and Ontologies --- p.3Chapter 1.2 --- Motivations --- p.5Chapter 1.2.1 --- Fuzziness of Concepts --- p.6Chapter 1.2.2 --- Typicality of Objects --- p.6Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8Chapter 1.3 --- Objectives --- p.9Chapter 1.4 --- Contributions --- p.10Chapter 1.5 --- Structure of the Thesis --- p.11Chapter 2 --- Background Study --- p.13Chapter 2.1 --- The Semantic Web --- p.14Chapter 2.2 --- Ontologies --- p.16Chapter 2.3 --- Description Logics --- p.20Chapter 2.4 --- Fuzzy Set Theory --- p.23Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25Chapter 2.5.1 --- Theory of Concepts --- p.26Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28Chapter 2.5.3 --- Similarity between Concepts --- p.29Chapter 2.5.4 --- Context and Context Effects --- p.31Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39Chapter 2.9 --- Contextual Reasoning --- p.41Chapter 3 --- A Formal Model of Ontology --- p.44Chapter 3.1 --- Rationale --- p.45Chapter 3.2 --- Concepts --- p.47Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47Chapter 3.4 --- Subsumption of Concepts --- p.49Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51Chapter 3.6 --- Prototype Vector and Typicality --- p.54Chapter 3.7 --- An Example --- p.59Chapter 3.8 --- Similarity between Concepts --- p.61Chapter 3.9 --- Context and Contextualization of Ontology --- p.65Chapter 3.9.1 --- Formal Definitions --- p.67Chapter 3.9.2 --- Contextualization of an Ontology --- p.69Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71Chapter 4 --- Discussions and Analysis --- p.73Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73Chapter 4.2 --- Likeliness and Typicality --- p.78Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86Chapter 4.5 --- Potential Application of the Model --- p.88Chapter 4.5.1 --- Searching in the Semantic Web --- p.88Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90Chapter 5 --- Conclusions and Future Work --- p.91Chapter 5.1 --- Conclusions --- p.91Chapter 5.2 --- Future Research Directions --- p.93Publications --- p.96Bibliography --- p.9