5,157 research outputs found
Unifying Class-Based Representation Formalisms
The notion of class is ubiquitous in computer science and is central in many
formalisms for the representation of structured knowledge used both in
knowledge representation and in databases. In this paper we study the basic
issues underlying such representation formalisms and single out both their
common characteristics and their distinguishing features. Such investigation
leads us to propose a unifying framework in which we are able to capture the
fundamental aspects of several representation languages used in different
contexts. The proposed formalism is expressed in the style of description
logics, which have been introduced in knowledge representation as a means to
provide a semantically well-founded basis for the structural aspects of
knowledge representation systems. The description logic considered in this
paper is a subset of first order logic with nice computational characteristics.
It is quite expressive and features a novel combination of constructs that has
not been studied before. The distinguishing constructs are number restrictions,
which generalize existence and functional dependencies, inverse roles, which
allow one to refer to the inverse of a relationship, and possibly cyclic
assertions, which are necessary for capturing real world domains. We are able
to show that it is precisely such combination of constructs that makes our
logic powerful enough to model the essential set of features for defining class
structures that are common to frame systems, object-oriented database
languages, and semantic data models. As a consequence of the established
correspondences, several significant extensions of each of the above formalisms
become available. The high expressiveness of the logic we propose and the need
for capturing the reasoning in different contexts forces us to distinguish
between unrestricted and finite model reasoning. A notable feature of our
proposal is that reasoning in both cases is decidable. We argue that, by virtue
of the high expressive power and of the associated reasoning capabilities on
both unrestricted and finite models, our logic provides a common core for
class-based representation formalisms
Transition Systems for Model Generators - A Unifying Approach
A fundamental task for propositional logic is to compute models of
propositional formulas. Programs developed for this task are called
satisfiability solvers. We show that transition systems introduced by
Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers
can be adapted for solvers developed for two other propositional formalisms:
logic programming under the answer-set semantics, and the logic PC(ID). We show
that in each case the task of computing models can be seen as "satisfiability
modulo answer-set programming," where the goal is to find a model of a theory
that also is an answer set of a certain program. The unifying perspective we
develop shows, in particular, that solvers CLASP and MINISATID are closely
related despite being developed for different formalisms, one for answer-set
programming and the latter for the logic PC(ID).Comment: 30 pages; Accepted for presentation at ICLP 2011 and for publication
in Theory and Practice of Logic Programming; contains the appendix with
proof
Unifying Requirements and Code: an Example
Requirements and code, in conventional software engineering wisdom, belong to
entirely different worlds. Is it possible to unify these two worlds? A unified
framework could help make software easier to change and reuse. To explore the
feasibility of such an approach, the case study reported here takes a classic
example from the requirements engineering literature and describes it using a
programming language framework to express both domain and machine properties.
The paper describes the solution, discusses its benefits and limitations, and
assesses its scalability.Comment: 13 pages; 7 figures; to appear in Ershov Informatics Conference, PSI,
Kazan, Russia (LNCS), 201
Big data and the SP theory of intelligence
This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as "a universal framework for the representation and
processing of diverse kinds of knowledge" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.Comment: Accepted for publication in IEEE Acces
- …