Coping with Incomplete Data: Recent Advances

Handling incomplete data in a correct manner is a notoriously hard problem in databases. Theoretical approaches rely on the computationally hard notion of certain answers, while practical solutions rely on ad hoc query evaluation techniques based on three-valued logic. Can we find a middle ground, and produce correct answers efficiently? The paper surveys results of the last few years motivated by this question. We re-examine the notion of certainty itself, and show that it is much more varied than previously thought. We identify cases when certain answers can be computed efficiently and, short of that, provide deterministic and probabilistic approximation schemes for them. We look at the role of three-valued logic as used in SQL query evaluation, and discuss the correctness of the choice, as well as the necessity of such a logic for producing query answers

Coping with Incomplete Data: Recent Advances

International audienceHandling incomplete data in a correct manner is a notoriously hard problem in databases. Theoretical approaches rely on the computationally hard notion of certain answers, while practical solutions rely on ad hoc query evaluation techniques based on threevalued logic. Can we find a middle ground, and produce correct answers efficiently? The paper surveys results of the last few years motivated by this question. We reexamine the notion of certainty itself, and show that it is much more varied than previously thought. We identify cases when certain answers can be computed efficiently and, short of that, provide deterministic and probabilistic approximation schemes for them. We look at the role of three-valued logic as used in SQL query evaluation, and discuss the correctness of the choice, as well as the necessity of such a logic for producing query answers

Querying Incomplete Data : Complexity and Tractability via Datalog and First-Order Rewritings

To answer database queries over incomplete data the gold standard is finding certain answers: those that are true regardless of how incomplete data is interpreted. Such answers can be found efficiently for conjunctive queries and their unions, even in the presence of constraints. With negation added, the problem becomes intractable however. We concentrate on the complexity of certain answers under constraints, and on effficiently answering queries outside the usual classes of (unions) of conjunctive queries by means of rewriting as Datalog and first-order queries. We first notice that there are three different ways in which query answering can be cast as a decision problem. We complete the existing picture and provide precise complexity bounds on all versions of the decision problem, for certain and best answers. We then study a well-behaved class of queries that extends unions of conjunctive queries with a mild form of negation. We show that for them, certain answers can be expressed in Datalog with negation, even in the presence of functional dependencies, thus making them tractable in data complexity. We show that in general Datalog cannot be replaced by first-order logic, but without constraints such a rewriting can be done in first-order. The paper is under consideration in Theory and Practice of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

Wine Quality Assessment under the Eindhoven Classification Method

The identification, classification and recording of events leading to deterioration of wine quality is essential for developing appropriate strategies to avoid them. This work introduces an adverse event reporting and learning system that can help preventing hazards and ensure the quality of the wines. The Eindhoven Classification Method (ECM) has been extended and adapted to the incidents of the wine industry. Logic Programming (LP) was used for Knowledge Representation and Reasoning (KRR) in order to model the universe of discourse, even in the presence of incomplete data, information or knowledge. On the other hand, the evolutionary process of the body of knowledge is to be understood as a process of energy devaluation, enabling the automatic extraction of knowledge and the generation of reports to identify the most relevant causes of errors that can lead to a poor wine quality. In addition, the answers to the problem are object of formal evidence through theorem proving

Multi-agent Confidential Abductive Reasoning

In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. Most collaborative reasoning systems operate on the assumption that agents can share or communicate any information they have. However, in application domains like multi-agent systems for healthcare or distributed software agents for security policies in coalition networks, confidentiality of knowledge is an additional primary concern. These agents are required to collaborately compute consistent answers for a query whilst preserving their own private information. This paper addresses this issue showing how this dichotomy between "open communication" in collaborative reasoning and protection of confidentiality can be accommodated. We present a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. A case study on security policy analysis in distributed coalition networks is described, as an example of many applications of this system

Declarative Debugging of Missing Answers for Maude

Declarative debugging is a semi-automatic technique that starts from an incorrect computation and locates a program fragment responsible for the error by building a tree representing this computation and guiding the user through it to find the error. Membership equational logic (MEL) is an equational logic that in addition to equations allows the statement of membership axioms characterizing the elements of a sort. Rewriting logic is a logic of change that extends MEL by adding rewrite rules, that correspond to transitions between states and can be nondeterministic. In this paper we propose a calculus that allows to infer normal forms and least sorts with the equational part, and sets of reachable terms through rules. We use an abbreviation of the proof trees computed with this calculus to build appropriate debugging trees for missing answers (results that are erroneous because they are incomplete), whose adequacy for debugging is proved. Using these trees we have implemented a declarative debugger for Maude, a high-performance system based on rewriting logic, whose use is illustrated with an example

Connection-minimal Abduction in EL via Translation to FOL -- Technical Report

Abduction in description logics finds extensions of a knowledge base to makeit entail an observation. As such, it can be used to explain why theobservation does not follow, to repair incomplete knowledge bases, and toprovide possible explanations for unexpected observations. We consider TBoxabduction in the lightweight description logic EL, where the observation is aconcept inclusion and the background knowledge is a TBox, i.e., a set ofconcept inclusions. To avoid useless answers, such problems usually come withfurther restrictions on the solution space and/or minimality criteria that helpsort the chaff from the grain. We argue that existing minimality notions areinsufficient, and introduce connection minimality. This criterion followsOccam's razor by rejecting hypotheses that use concept inclusions unrelated tothe problem at hand. We show how to compute a special class ofconnection-minimal hypotheses in a sound and complete way. Our technique isbased on a translation to first-order logic, and constructs hypotheses based onprime implicates. We evaluate a prototype implementation of our approach onontologies from the medical domain.<br

Connection-Minimal Abduction in {E}L via Translation to {FOL} (Extended Abstract)

International audienceAbduction in description logics finds extensions of a knowledge base to make it entail an observation. As such, it can be used to explain why the observation does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic EL , where the observation is a concept inclusion and the background knowledge is a TBox, i.e., a set of concept inclusions. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion follows Occam’s razor by rejecting hypotheses that use concept inclusions unrelated to the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain