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2021
Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are not feasible, and a subjective or epistemic approach is required. Aleatoric description logic models uncertainty in the world as aleatoric events, by the roll of the dice, where an agent has subjective beliefs about the bias of these dice. This provides a subjective Bayesian description logic, where propositions and relations are assigned probabilities according to what a rational agent would bet, given a configuration of possible individuals and dice. Aleatoric description logic is shown to generalise the description logic ALC, and can be seen to describe a probability space of interpretations of a restriction of ALC where all roles are functions. Several computational problems are considered and aleatoric description logic is shown to be able to...
Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2019
The paper proposes a new type of probabilistic description logics with a different interpretation of uncertain knowledge. The basic idea is that the probability of an axiom is not the probability of the axiom to be true in contrast to be false. Instead, it is the probability of the axiom to be true within the same knowledge base, i.e. in contrast to other axioms of the knowledge base to be true. The proposed description logic is evaluated with some sample knowledge bases and the results are discussed in the paper.
2020
While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
Uncertainty Reasoning for the Semantic Web III, 2014
We present a semantics for Probabilistic Description Logics that is based on the distribution semantics for Probabilistic Logic Programming. The semantics, called DISPONTE, allows to express assertional probabilistic statements. We also present two systems for computing the probability of queries to probabilistic knowledge bases: BUNDLE and TRILL. BUNDLE is based on the Pellet reasoner while TRILL exploits the declarative Prolog language. Both algorithms compute a propositional Boolean formula that represents the set of explanations to the query. BUNDLE builds a formula in Disjunctive Normal Form in which each disjunct corresponds to an explanation while TRILL computes a general Boolean pinpointing formula using the techniques proposed by Baader and Peñaloza. Both algorithms then build a Binary Decision Diagram (BDD) representing the formula and compute the probability from the BDD using a dynamic programming algorithm. We also present experiments comparing the performance of BUNDLE and TRILL.
Proc. of the 2008 Description Logic Workshop (DL 2009), 2009
This paper analyzes the probabilistic description logic PSHIQ by looking at it as a fragment of probabilistic first-order logic with semantics based on possible worlds. We argue that this is an appropriate way of investigating its properties and developing extensions. We show how the previously made arguments about different types of first-order probabilistic semantics apply to P-SHIQ. This approach has advantages for the future of both P-SHIQ, which can further evolve by incorporating semantic theories developed for the full first- ...
2008
The DL-Lite family of tractable description logics lies between the semantic web languages RDFS and OWL Lite. In this paper, we present a probabilistic generalization of the DL-Lite description logics, which is based on Bayesian networks. As an important feature, the new probabilistic description logics allow for flexibly combining terminological and assertional pieces of probabilistic knowledge.
Journal of Computer Science and Technology, 2015
We present a preliminary framework for reasoning with possibilistic description logics ontologies with disjunctive assertions (PoDLoDA ontologies for short). Given a PoDLoDA ontology, its terminological box is expressed in the description logic programming fragment but its assertional box allows four kinds of statements: an individual is a member of a concept, two individuals are related through a role, an individual is a member of the union of two or more concepts or two individuals are related through the union of two or more roles. Axioms and statements in PoDLoDA ontologies have a numerical certainty degree attached. A disjunctive assertion expresses a doubt respect to the membership of either individuals to union of concepts or pairs of individuals to the union of roles. Because PoDLoDA ontologies allow to represent incomplete and potentially inconsistent information, instance checking is addressed through an adaptation of Bodanza’s Suppositional Argumentation System that allow...
Advances in Soft Computing, 2008
This paper proposes a common framework for various probabilistic logics. It consists of a set of uncertain premises with probabilities attached to them. This raises the question of the strength of a conclusion, but without imposing a particular semantics, no general solution is possible. The paper discusses several possible semantics by looking at it from the perspective of probabilistic argumentation.
We present BUNDLE, a reasoner able to perform reasoning on probabilistic knowledge bases according to the semantics DISPONTE. In DISPONTE the axioms of a probabilistic ontology can be annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the axiom, while the statistical probability considers the populations to which the axiom is applied. BUNDLE exploits an underlying OWL DL reasoner, which is Pellet, that is able to return explanations for a query. However, it can work well with any reasoner able to return explanations for a query. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed.
2002
Probabilistic computation has proven to be a challenging and interesting area of research, both from the theoretical perspective of denotational semantics and the practical perspective of reasoning about probabilistic algorithms. On the theoretical side, the probabilistic powerdomain of Jones and Plotkin represents a significant advance. Further work, especially by Alvarez-Manilla, has greatly improved our understanding of the probabilistic powerdomain, and has helped clarify its relation to classical measure and integration theory. On the practical side, many researchers such as Kozen, Segala, Desharnais, and Kwiatkowska, among others, study problems of verification for probabilistic computation by defining various suitable logics for the classes of processes under study. The work reported here begins to bridge the gap between the domain theoretic and verification (model checking) perspectives on probabilistic computation by exhibiting sound and complete logics for probabilistic powerdomains that arise directly from given logics for the underlying domains. The category in which the construction is carried out generalizes Scott's Information Systems by taking account of full classical sequents. Via Stone duality, following Abramsky's Domain Theory in Logical Form, all known interesting categories of domains are embedded as subcategories. So the results reported here properly generalize similar constructions on specific categories of domains. The category offers a promising universe of semantic domains characterized by a very rich structure and good preservation properties of standard constructions. Furthermore, because the logical constructions make use of full classical sequents, the morphisms have a natural non-deterministic interpretation. Thus the category is a natural one in which to investigate the relationship between probabilistic and non-deterministic computation. We discuss the problem of integrating probabilistic and non-deterministic computation after presenting the construction of logics for probabilistic powerdomains.
Journal of Algorithms, 2008
This paper develops connections between objective Bayesian epistemology-which holds that the strengths of an agent's beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal-and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics.
Uncertain information is ubiquitous in real world domains and in the Semantic Web. Recently, the problem of representing this uncertainty in description logics has received an increasing attention. In probabilistic Description Logics, knowledge bases contain numeric parameters that are often difficult to specify for a human. Moreover, the information are incomplete and poorly structured. On the other hand, data is usually available about the domain that can be leveraged for tuning the parameters and learn the structure of the information. In this paper we consider the problem of learning both the structure and the parameters of Probabilistic Description Logics under the DISPONTE semantics. We overview two systems we hve implemented: EDGE, that returns the value of the probabilities associated with axioms tuned using an Expectation Maximization algorithm, and LEAP, that exploits EDGE and the system CELOE to learn both the structure and the parameters of DISPONTE knowledge bases.
2018
While many systems exist for reasoning with Description Logics knowledge bases, very few of them are able to cope with uncertainty. BUNDLE is a reasoning system, exploiting an underlying non-probabilistic reasoner (Pellet), able to perform inference w.r.t. Probabilistic Description Logics. In this paper, we report on a new modular version of BUNDLE that can use other OWL (non-probabilistic) reasoners and various approaches to perform probabilistic inference. BUNDLE can now be used as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. Due to the introduced modularity, BUNDLE performance now strongly depends on the method and OWL reasoner chosen to obtain the set of justifications. We provide an evaluation on several datasets as the inference settings vary.
2013
Uncertain information is ubiquitous in the Semantic Web, due to methods used for collecting data and to the inherently distributed nature of the data sources. It is thus very important to develop probabilistic Description Logics (DLs) so that the uncertainty is directly represented and managed at the language level. The DISPONTE semantics for probabilistic DLs applies the distribution semantics of probabilistic logic programming to DLs. In DISPONTE, axioms are labeled with numeric parameters representing their probability. These are often difficult to specify or to tune for a human. On the other hand, data is usually available that can be leveraged for setting the parameters. In this paper, we present EDGE that learns the parameters of DLs following the DISPONTE semantics. EDGE is an EM algorithm in which the required expectations are computed directly on the binary decision diagrams that are built for inference. Experiments on two datasets show that EDGE achieves higher areas under...
2015
Modeling real world domains requires ever more frequently to represent uncertain information. The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability. In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics. We present the algorithm BUNDLE for computing the probability of queries. BUNDLE exploits an underlying Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is used for computing the probability of the query.
Lecture Notes in Computer Science, 2015
We propose a framework for automated multi-attribute decision making, employing the probabilistic non-monotonic description logics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making situation, wherein background knowledge, available alternatives and weighted attributes are represented via probabilistic ontologies. It turns out that extending traditional utility theory with such description logics, enables us to model decision-making problems where probabilistic ignorance and default reasoning plays an important role. We provide several decision functions using the notions of expected utility and probability intervals, and study their properties.
The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL P , which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog. Note: An extended abstract / full version of a paper accepted to be presented at the Doctoral
Logic Journal of the IGPL (Oxford University Press), 2024
In this work we focus on extensions of Description Logics (DLs) of typicality by means of probabilities. We introduce a novel extension of the logic of typicality ALC + T R , able to represent and reason about typical properties and defeasible inheritance in DLs. The novel logic (ALCT P : Typical ALC with Probabilities as Proportions) allows inclusions of the form T(C) p D, with probability p representing a proportion, meaning that "all the typical Cs are Ds, and the probability that a C element is not a D element is 1 − p". We also compare and confront this novel logic with a similar one already presented in the literature (T CL , introduced in Lieto and Pozzato (2020, J. Exp. Theor. Artif. Intell., 32, 769-804)), inspired by the DISPONTE semantics and that allows inclusions of the form p : T(C) D with probability p, where p represents a degree of belief, whose meaning is that "we believe with a degree p that typical Cs' are also Ds.". We then show that the proposed ALCT P extension (like the previous T CL) can be applied in order to tackle a specific and challenging problem in the field of common-sense reasoning, namely the combination of prototypical concepts, that have been shown to be problematic to model for other symbolic approaches like fuzzy logic. We show that, for the proposed extension, the complexity of reasoning remains EXPTIME-complete as for the underlying standard monotonic DL ALC.
One shortcoming of classic Descriptions Logics, DLs, is their inability to encode probabilistic knowledge and reason over it. This is, however, a strong demand of some modern applications, e.g. in biology and healthcare. Therefore, probabilistic extensions of DLs are attracting attention nowadays. We introduce the probabilistic DL SHIQP which extends a known probabilistic DL. We investigate two reasoning problems for TBoxes: deciding consistency and computing tight probability bounds. It turns out that both problems are not harder than reasoning in the classic counterpart SHIQ. We gain insight into complexity sources.
2015
We present a preliminary framework for reasoning with possibilistic description logics ontologies with disjunctive assertions (PDLDA ontologies for short). PDLDA ontologies are composed of a terminology as well as an assertional box that allows to declare three kinds of assertional statements: an individual is a member of one concept, two individuals are related through a role, an individual is a member of the union of two or more concepts or two individuals are related through the union of two or more roles. Each axiom in the ontologies has a certainty degree as is usual in possibilistic logics. For reasoning with PDLDA ontologies, we interpret them in terms of a adaptation of Bodanza’s Suppositional Argumentation System. Our framework allows to reason with modus ponens and constructive dilemmas. We use it for determining the membership of individuals to concepts when there is doubt to exactly which one of the concepts in the union the individual belongs. We think that our approach...
2012
We present DISPONTE, a semantics for probabilistic ontologies that is based on the distribution semantics for probabilistic logic programs. In DISPONTE the axioms of a probabilistic ontology can be annotated with an epistemic or a statistical probability. The epistemic probability represents a degree of confidence in the axiom, while the statistical probability considers the populations to which the axiom is applied.
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