3,407 research outputs found
Ontology and medical terminology: Why description logics are not enough
Ontology is currently perceived as the solution of first resort for all problems related to biomedical terminology, and the use of description logics is seen as a minimal requirement on adequate ontology-based systems. Contrary to common conceptions, however, description logics alone are not able to prevent incorrect representations; this is because they do not come with a theory indicating what is computed by using them, just as classical arithmetic does not tell us anything about the entities that are added or subtracted. In this paper we shall show that ontology is indeed an essential part of any solution to the problems of medical terminology – but only if it is understood in the right sort of way. Ontological engineering, we shall argue, should in every case go hand in hand with a sound ontological theory
On the similarity relation within fuzzy ontology components
Ontology reuse is an important research issue. Ontology
merging, integration, mapping, alignment and versioning
are some of its subprocesses. A considerable research work has
been conducted on them. One common issue to these subprocesses
is the problem of defining similarity relations among ontologies
components. Crisp ontologies become less suitable in all domains
in which the concepts to be represented have vague, uncertain
and imprecise definitions. Fuzzy ontologies are developed to
cope with these aspects. They are equally concerned with the
problem of ontology reuse. Defining similarity relations within
fuzzy context may be realized basing on the linguistic similarity
among ontologies components or may be deduced from their
intentional definitions. The latter approach needs to be dealt
with differently in crisp and fuzzy ontologies. This is the scope
of this paper.ou
Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems
In this paper I will present an analysis of the impact that the notion of “bounded rationality”,
introduced by Herbert Simon in his book “Administrative Behavior”, produced in the
field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated
Decision Making (ADM), I will show how the introduction of the cognitive dimension into
the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the
development of a line of research aiming at the realisation of artificial systems whose decisions
are based on the adoption of powerful shortcut strategies (known as heuristics) based
on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the
“heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial
complexity in real-life situations and still represents an important strategy for the design
and implementation of intelligent systems
Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach
Matchmaking arises when supply and demand meet in an electronic marketplace,
or when agents search for a web service to perform some task, or even when
recruiting agencies match curricula and job profiles. In such open
environments, the objective of a matchmaking process is to discover best
available offers to a given request. We address the problem of matchmaking from
a knowledge representation perspective, with a formalization based on
Description Logics. We devise Concept Abduction and Concept Contraction as
non-monotonic inferences in Description Logics suitable for modeling
matchmaking in a logical framework, and prove some related complexity results.
We also present reasonable algorithms for semantic matchmaking based on the
devised inferences, and prove that they obey to some commonsense properties.
Finally, we report on the implementation of the proposed matchmaking framework,
which has been used both as a mediator in e-marketplaces and for semantic web
services discovery
A hybrid approach for modeling uncertainty in terminological logics
This paper proposes a probabilistic extension of terminological logics. The extension maintains the original performance of drawing inferences in a hierarchy of terminological definitions. It enlarges the range of applicability to real world domains determined not only by definitional but also by uncertain knowledge. First, we introduce the propositionally complete terminological language ALC. On the basis of the language construct "probabilistic implication" it is shown how statistical information on concept dependencies can be represented. To guarantee (terminological and probabilistic) consistency, several requirements have to be met. Moreover, these requirements allow one to infer implicitly existent probabilistic relationships and their quantitative computation. By explicitly introducing restrictions for the ranges derived by instantiating the consistency requirements, exceptions can also be handled. In the categorical cases this corresponds to the overriding of properties in non monotonic inheritance networks. Consequently, our model applies to domains where both term descriptions and non-categorical relations between term extensions have to be represented
Type-elimination-based reasoning for the description logic SHIQbs using decision diagrams and disjunctive datalog
We propose a novel, type-elimination-based method for reasoning in the
description logic SHIQbs including DL-safe rules. To this end, we first
establish a knowledge compilation method converting the terminological part of
an ALCIb knowledge base into an ordered binary decision diagram (OBDD) which
represents a canonical model. This OBDD can in turn be transformed into
disjunctive Datalog and merged with the assertional part of the knowledge base
in order to perform combined reasoning. In order to leverage our technique for
full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that
preserves satisfiability and entailment of positive and negative ground facts.
The proposed technique is shown to be worst case optimal w.r.t. combined and
data complexity and easily admits extensions with ground conjunctive queries.Comment: 38 pages, 3 figures, camera ready version of paper accepted for
publication in Logical Methods in Computer Scienc
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