Renata Wassermann

Base Revision for Ontology Debugging

Belief Revision deals with the problem of adding new information to a knowledge base in a consistent way. Ontology Debugging, on the other hand, aims to find the axioms in a terminological knowledge base which caused the base to become inconsistent. In this article, we propose a belief revision approach in order to find and repair inconsistencies in ontologies represented in some description logic (DL). As the usual belief revision operators cannot be directly applied to DLs, we propose new operators that can be used with more general logics and show that, in particular, they can be applied to the logics underlying OWL-DL and Lite.

Minimal change: Relevance and recovery revisited

The operation of contraction (referring to the removal of knowledge from a knowledge base) has been extensively studied in the research field of belief change, and different postulates (e.g., the AGM postulates with recovery, or relevance) have been proposed, as well as several constructions (e.g., partial meet) that allow the definition of contraction operators satisfying said postulates. Most of the related work has focused on classical logics, i.e., logics that satisfy certain intuitive assumptions; in such logics, several nice properties and equivalences related to the above postulates and constructions have been shown to hold. Unfortunately, previous work has shown that the postulates@? applicability and the related results generally fail for non-classical logics. Motivated by the fact that non-classical logics (like Description Logics or Horn logic) are increasingly being used in various applications, we study contraction for all monotonic logics, classical or not. In particular, we identify several sufficient conditions for the various postulates to be applicable, and show that, in practice, relevance is a more suitable (i.e., applicable) minimality criterion than recovery for non-classical logics. In addition, we revisit some important related results from the classical belief change literature and study conditions sufficient for them to hold for non-classical logics; the corresponding results for classical logics emerge as corollaries of our more general results. Our work is another step towards the aim of exploiting the rich belief change literature for addressing the evolution problem in a larger class of logics.

Horn clause contraction functions

In classical, AGM-style belief change, it is assumed that the underlying logic contains classical propositional logic. This is clearly a limiting assumption, particularly in Artificial Intelligence. Consequently there has been recent interest in studying belief change in approaches where the full expressivity of classical propositional logic is not obtained. In this paper we investigate belief contraction in Horn knowledge bases. We point out that the obvious extension to the Horn case, involving Horn remainder sets as a starting point, is problematic. Not only do Horn remainder sets have undesirable properties, but also some desirable Horn contraction functions are not captured by this approach. For Horn belief set contraction, we develop an account in terms of a model-theoretic characterisation involving weak remainder sets. Maxichoice and partial meet Horn contraction is specified, and we show that the problems arising with earlier work are resolved by these approaches. As well, constructions of the specific operators and sets of postulates are provided, and representation results are obtained. We also examine Horn package contraction, or contraction by a set of formulas. Again, we give a construction and postulate set, linking them via a representation result. Last, we investigate the closely-related notion of forgetting in Horn clauses. This work is arguably interesting since Horn clauses have found widespread use in AI; as well, the results given here may potentially be extended to other areas which make use of Horn-like reasoning, such as logic programming, rule-based systems, and description logics. Finally, since Horn reasoning is weaker than classical reasoning, this work sheds light on the foundations of belief change.

An information retrieval application using ontologies

Searching for information in long videos can be a time-consuming experience. In this paper, we describe OnAIR, an ontology-aided information retrieval system applied to retrieve clips from video collections.We used a video collection compiled from interviews with Ana Teixeira, a Brazilian artist. The interviews were made by Paula P. Braga, the domain expert. The interview is developed in the domain of contemporary art and the system uses a domain ontology to expand the queries with related terms. We tested the system with a battery of queries, and we veri.ed that the ontology contributes to the e.ciency improvement in terms of the relevance of retrieved documents. We designed the system to work in a domain-independent way, allowing us to move to other domains by just changing the underlying ontologies and video collections.

On AGM for Non-Classical Logics

The AGM theory of belief revision provides a formal framework to represent the dynamics of epistemic states. In this framework, the beliefs of the agent are usually represented as logical formulas while the change operations are constrained by rationality postulates. In the original proposal, the logic underlying the reasoning was supposed to be supraclassical, among other properties. In this paper, we present some of the existing work in adapting the AGM theory for non-classical logics and discuss their interconnections and what is still missing for each approach.

An argumentation machinery to reason over inconsistent ontologies

Widely accepted argumentation techniques are adapted to define a non-standard description logic (DL) reasoning machinery. A DL-based argumentation framework is introduced to reason about potentially inconsistent ontologies. Arguments in this framework can handle different DL families like ALC, eL, and DL-Lite. Afterwards, we propose an algorithm based on debugging techniques and classical tableau-based ALC satisfiability to build arguments, and discuss about the computational cost of reasoning through the proposed machinery.

The universe of propositional approximations

The idea of approximate entailment has been proposed by Schaerf and Cadoli [Tractable reasoning via approximation, Artif. Intell. 74(2) (1995) 249-310] as a way of modelling the reasoning of an agent with limited resources. In that framework, a family of logics, parameterised by a set of propositional letters, approximates classical logic as the size of the set increases.The original proposal dealt only with formulas in clausal form, but in Finger and Wassermann [Approximate and limited reasoning: semantics, proof theory, expressivity and control, J. Logic Comput. 14(2) (2004) 179-204], one of the approximate systems was extended to deal with full propositional logic, giving the new system semantics, an axiomatisation, and a sound and complete proof method based on tableaux. In this paper, we extend another approximate system by Schaerf and Cadoli, presented in a subsequent work [M. Cadoli, M. Schaerf, The complexity of entailment in propositional multivalued logics, Ann. Math. Artif. Intell. 18(1) (1996) 29-50] and then take the idea further, presenting a more general approximation framework of which the previous ones are particular cases, and show how it can be used to formalise heuristics used in theorem proving.

Logics for Approximate Reasoning: Approximating Classical Logic From Above

Approximations are used for dealing with problems that are hard, usually NP-hard or coNP-hard. In this paper we describe the notion of approximating classical logic from above and from below, and concentrate in the first. We present the family s1 of logics, and show it performs approximation of classical logic from above. The family s1 can be used for disproving formulas (the SAT-problem) in a local way, concentrating only on the relevant part of a large set of formulas.

A hybrid cloud-P2P architecture for multimedia information retrieval on VoD services

Recent research in Cloud Computing and Peer-to-Peer systems for Video-on-Demand (VoD) has focused on multimedia information retrieval, using cloud nodes as video streaming servers and peers as a way to distribute and share the video segments. A key challenge faced by these systems is providing an efficient way to retrieve the information segments descriptor, composed of its metadata and video segments, distributed among the cloud nodes and the Peer-to-Peer (P2P) network. In this paper, we propose a novel Cloud Computing and P2P hybrid architecture for multimedia information retrieval on VoD services that supports random seeking while providing scalability and efficiency. The architecture comprises Cloud and P2P layers. The Cloud layer is responsible for video segment metadata retrieval, using ontologies to improve the relevance of the retrieved information, and for distributing the metadata structures among cloud nodes. The P2P layer is responsible for finding peers that have the physical location of a segment. In this layer, we use trackers, which manage and collect the segments shared among other peers. We also use two Distributed Hash Tables, one to find these trackers and the other to store the information collected in case the tracker leaves the network and another peer needs to replace it. Unlike previous work, our architecture separates cloud nodes and peers responsibilities to manage the video metadata and its segments, respectively. Also, we show via simulations, the possibility of converting any peer to act as a tracker, while maintaining system scalability and performance, avoiding using centralized and powerful servers.

On the revision of planning tasks

When a planning task cannot be solved then it can often be made solvable by modifying it a bit: one may change either the set of actions, or the initial state, or the goal description. We show that modification of actions can be reduced to initial state modification. We then apply Katsuno and Mendelzons distinction between update and revision and show that the modification of the initial state is an update and the modification of the goal description is a revision. We consider variants of Forbuss update and Dalals revision operation and argue that existing belief change operations do not apply as they stand because their inputs are boolean formulas, while plan task modification involves counterfactual statements. We show that they can be captured in Dynamic Logic of Propositional Assignments DL-PA.