66 research outputs found
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
A GPFCSP-Based Fuzzy XQuery Interpreter
Nowadays XQuery has become the strongest standard for querying XML data. However, most of the real world information is in the form of imprecise, vague, ambiguous, uncertain and incomplete values. That is why there is a need for a flexible query language in which users can formulate queries that arise from their own criteria. In this paper, we propose an implementation of the Fuzzy XQuery - an extension of the XQuery query language based on the fuzzy set theory. In particular, we provide priority, threshold and fuzzy expressions for handling flexible queries. In addition, we have implemented an interpreter for this language by using the GPFCSP concept in Java and eXist-db environment
Differentiable Logics for Neural Network Training and Verification
The rising popularity of neural networks (NNs) in recent years and their
increasing prevalence in real-world applications have drawn attention to the
importance of their verification. While verification is known to be
computationally difficult theoretically, many techniques have been proposed for
solving it in practice. It has been observed in the literature that by default
neural networks rarely satisfy logical constraints that we want to verify. A
good course of action is to train the given NN to satisfy said constraint prior
to verifying them. This idea is sometimes referred to as continuous
verification, referring to the loop between training and verification. Usually
training with constraints is implemented by specifying a translation for a
given formal logic language into loss functions. These loss functions are then
used to train neural networks. Because for training purposes these functions
need to be differentiable, these translations are called differentiable logics
(DL). This raises several research questions. What kind of differentiable
logics are possible? What difference does a specific choice of DL make in the
context of continuous verification? What are the desirable criteria for a DL
viewed from the point of view of the resulting loss function? In this extended
abstract we will discuss and answer these questions.Comment: FOMLAS'22 pape
Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning
Integrating logical reasoning and machine learning by approximating logical
inference with differentiable operators is a widely used technique in
Neuro-Symbolic systems.
However, some differentiable operators could bring a significant bias during
backpropagation and degrade the performance of Neuro-Symbolic learning.
In this paper, we reveal that this bias, named \textit{Implication Bias} is
common in loss functions derived from fuzzy logic operators.
Furthermore, we propose a simple yet effective method to transform the biased
loss functions into \textit{Reduced Implication-bias Logic Loss (RILL)} to
address the above problem.
Empirical study shows that RILL can achieve significant improvements compared
with the biased logic loss functions, especially when the knowledge base is
incomplete, and keeps more robust than the compared methods when labelled data
is insufficient.Comment: ACML'2023 Journal Track(Accepted by Machine Learning Journal
Minimalistic fuzzy ontology reasoning: An application to Building Information Modeling
This paper presents a minimalistic reasoning algorithm to solve imprecise instance retrieval in fuzzy ontologies with application to querying Building Information Models (BIMs)—a knowledge representation formalism used in the construction industry. Our proposal is based on a novel lossless reduction of fuzzy to crisp reasoning tasks, which can be processed by any Description Logics reasoner. We implemented the minimalistic reasoning algorithm and performed an empirical evaluation of its performance in several tasks: interoperation with classical reasoners (Hermit and TrOWL), initialization time (comparing TrOWL and a SPARQL engine), and use of different data structures (hash tables, databases, and programming interfaces). We show that our software can efficiently solve very expressive queries not available nowadays in regular or semantic BIMs tools
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