The semantic web needs more cognition (original) (raw)

Conceptual Space Markup Language (CSML): Towards the Cognitive Semantic Web

2009 IEEE International Conference on …, 2009

CSML is a semantic markup language created for the publishing and sharing of conceptual spaces, which are geometric structures that represent semantics at the conceptual level. CSML can be used to describe semantics that are not captured well by the ontology languages commonly used in the Semantic Web. Measurement of the semantic similarity of concepts as well as the combination of concepts without shared properties are common human cognitive tasks. However, these operations present sources of difficulty for tools reliant upon set-theoretic and syllogistic reasoning on symbolic ontologies. In contrast, these operations can be modeled naturally using conceptual spaces. This paper describes the design decisions behind CSML, introduces the key component elements of a CSML document, and presents examples of its usage.

How to make the Semantic Web more semantic

2004

The Semantic Web is not semantic. It is good for syllogistic reasoning, but there is much more to semantics than syllogisms. I argue that the current Semantic Web is too dependent on symbolic representations of information structures, which limits its representational capacity. As a remedy, I propose conceptual spaces as a tool for expressing more of the semantics. Conceptual spaces are built up from quality dimensions that have geometric or topological structures. With the aid of the dimensions, similarities between objects can easily be represented and it is argued that similarity is a central aspect of semantic content. By sorting the dimensions into domains, I define properties and concepts and show how prototype effects of concepts can be treated with the aid of conceptual spaces. I present an outline of how one can reconstruct most of the taxonomies and other meta-data that are explicitly coded in the current Semantic Web and argue that inference engines on the symbolic level ...

A metric conceptual space algebra

Spatial Information Theory, 2009

The modeling of concepts from a cognitive perspective is important for designing spatial information systems that interoperate with human users. Concept representations that are built using geometric and topological conceptual space structures are well suited for semantic similarity and concept combination operations. In addition, concepts that are more closely grounded in the physical world, such as many spatial concepts, have a natural fit with the geometric structure of conceptual spaces. Despite these apparent advantages, conceptual spaces are underutilized because existing formalizations of conceptual space theory have focused on individual aspects of the theory rather than the creation of a comprehensive algebra. In this paper we present a metric conceptual space algebra that is designed to facilitate the creation of conceptual space knowledge bases and inferencing systems. Conceptual regions are represented as convex polytopes and context is built in as a fundamental element. We demonstrate the applicability of the algebra to spatial information systems with a proof-of-concept application.

The Conceptual Web-Our Research Vision

… of the first Semantic Web Working …, 2001

We present a vision of an extension of the emerging semantic web into what we call a Conceptual Web, where the semantics is not only machine-understandable, but also available for the user in an appealing form, which creates substantial benefits in terms of overview and clarity. We are using visual modeling in UML and a technique called conceptual browsing to present the conceptual web to the user. This construction lives on top of the ordinary semantic web and thus shares the advantages of RDF, such as distributivity and scalability. * http://cid.nada.kth.se/il

Knowledge Representation and Reasoning in Conceptual Spaces

2007 IEEE Symposium on Foundations of Computational Intelligence, 2007

This paper presents a conceptual system in which concepts are defined by binary associations between properties. Properties are measurable membership functions, defined on sets equipped with a measure that are the disjoint domains of representation. Instances of concepts (observations) are sets of points from these domains. Requiring properties to be measurable enables their overlap to be precisely described. Similarity between concepts, and between observations and concepts, is naturally defined using fuzzy subsethood, and similarity and overlap are used to set attention in categorization tasks. This formulation therefore follows Gardenförs in recognizing the importance of property associations and of similarity in conceptual systems.

A Conceptual Space Approach to Semantic Networks

Computers & Mathematics with Applications 23 (1992), 6-9, March-May, s. 517-526.

Atmtract--If every entity has a set of attributes with each attribute having a value, we regard the complete set of an eatity's attribute-v,due pairs (e.8., color-red, heisht-4 era) as fully describing the entity. Such descriptions form a conceptual space, that is, an intensional space of concepts for which spatial inclusion corresponds to strict logical implication. An intensional losic of concepts is developed with which we can talk about concepts and their relations without referring to extensions of concepts. In this approach semantic networks are simply sets of interrelated formulas; i.e., they are theories in the logic of concepts. Default values are treated by introducing a new type of modal po~ibil~ty operator and a superconcept operator, not by revising the basic logical entailment relation. A concept may inherit values from a superconcept either strictly or by default as "concept qva superconcept." In this way inheritance problems turn out to be logical inference problems, and they can be solved in sound proof theory. (z)p, are formulas. A model of LC is a structure M, M = (V,S,F), where V is a (non-empty) set of values, S is a conceptual space, S = {f If: ATT--~ V}, 523

Quo Vadis, CS?–On the (non)-impact of Conceptual Structures on the Semantic Web (Position Paper)

2008

Abstract. Conceptual Structures is a field of research which shares abstract concepts and interests with recent work on knowledge representation for the Semantic Web. However, while the latter is an area of research and development which is rapidly expanding in recent years, the former fails to participate in these developments on a large scale. In this paper, we attempt to stimulate the Conceptual Structures community to catch the Semantic Web train.

Closing the Loop between knowledge patterns in cognition and the Semantic Web

Semantic Web, 2020

We discuss currently open issues in the discovery and representation of knowledge patterns in computational processing of meaning, in order to improve interoperability and cognitive validity of web-based semantics. We present the current state of knowledge patterns (KP) in Knowledge Representation, the Semantic Web and Cognitive Sciences, focusing on an intensional abstraction of heterogeneous predicates as a formal foundation for KP.

A Conceptual Model for Semantic Web Spaces

… , Freie Universität Berlin, …

In a previous technical report [TNL+04] we have introduced an extension of XML-based tuplespaces for the Semantic Web. By applying a tuplespace approach to the concur- rent interaction of multiple clients with distributed knowledge repositories, we foresee the benefits of a simple yet ...