7,325 research outputs found
Multi-Attribute Decision Making using Weighted Description Logics
We introduce a framework based on Description Logics, which can be used to encode and solve decision problems in terms of combining inference services in DL and utility theory to represent preferences of the agent. The novelty of the approach is that we consider ABoxes as alternatives and weighted concept and role assertions as preferences in terms of possible outcomes. We discuss a relevant use case to show the benefits of the approach from the decision theory point of view
Multi-attribute decision making with weighted description logics
We introduce a decision-theoretic framework based on Description Logics
(DLs), which can be used to encode and solve single stage multi-attribute decision problems. In particular, we consider the background knowledge as a DL
knowledge base where each attribute is represented by a concept, weighted by
a utility value which is asserted by the user. This yields a compact representation of preferences over attributes. Moreover, we represent choices as knowledge
base individuals, and induce a ranking via the aggregation of attributes that
they satisfy. We discuss the benefits of the approach from a decision theory
point of view. Furthermore, we introduce an implementation of the framework
as a Protégé plugin called uDecide. The plugin takes as input an ontology as
background knowledge, and returns the choices consistent with the user’s (the
knowledge base) preferences. We describe a use case with data from DBpedia.
We also provide empirical results for its performance in the size of the ontology
using the reasoner Konclude
uDecide: A protégé plugin for multiattribute decision making
This paper introduces the Protege plugin uDecide. With the help of uDecide it is possible to solve multi-attribute decision
making problems encoded in a straight forward extension of standard Description Logics. The formalism allows to specify background knowledge in terms of an ontology, while each attribute is represented as a weighted class expression. On
top of such an approach one can compute the best choice (or the best k-choices) taking background knowledge into account in the appropriate way. We show how to implement
the approach on top of existing semantic web technologies
and demonstrate its benefits with the help of an interesting use case that illustrates how to convert an existing web
resource into an expert system with the help of uDecide
Fuzzy ontology representation using OWL 2
AbstractThe need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing a procedure to represent such information within current standard languages and tools. In this work, we follow the latter approach, by identifying the syntactic differences that a fuzzy ontology language has to cope with, and by proposing a concrete methodology to represent fuzzy ontologies using OWL 2 annotation properties. We also report on some prototypical implementations: a plug-in to edit fuzzy ontologies using OWL 2 annotations and some parsers that translate fuzzy ontologies represented using our methodology into the languages supported by some reasoners
Public initiatives of settlement transformation. A theoretical-methodological approach to selecting tools of multi-criteria decision analysis
In Europe, the operating context in which initiatives of settlement transformation are currently initiated is characterized by a complex, elaborate combination of technical, regulatory and governance-related factors. A similar set of considerations makes it necessary to address the complex decision-making problems to be resolved through multidisciplinary, comparative approaches designed to rationalize the process and treat the elements to be considered in systematic fashion with respect to the range of alternatives available as solutions. Within a context defined in this manner, decision-making processes must often be used to obtain multidisciplinary and multidimensional analyses to support the choices made by the decision-makers. Such analyses are carried out using multi-criteria tools designed to arrive at syntheses of the numerous forms of input data needed to describe decision-making problems of similar complexity, so that one or more outcomes of the synthesis make possible informed, well thought-out, strategic decisions. The technical literature on the topic proposes numerous tools of multi-criteria analysis for application in different decision-making contexts. Still, no specific contributions have been drawn up to date on the approach to take in selecting the tool best suited to providing adequate responses to the queries of evaluation that arise most frequently in the various fields of application, and especially in the settlement sector. The objective of this paper is to propose, by formulating a taxonomy of the endogenous and exogenous variables of tools of multi-criteria analysis, a methodology capable of selecting the tool best suited to the queries of evaluation which arise regarding the chief categories of decision-making problems, and particularly in the settlement sector
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