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Found 34 papers, 0 papers with code
Measuring and Computing Database Inconsistency via Repairs
We propose a generic numerical measure of inconsistency of a database with respect to a set of integrity constraints.
Specifying and Computing Causes for Query Answers in Databases via Database Repairs and Repair Programs
In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes.
Datalog: Bag Semantics via Set Semantics
This use of Datalog$^\pm$ is extended to give a set semantics to duplicates in Datalog$^\pm$ itself.
Ontological Multidimensional Data Models and Contextual Data Qality
Data quality assessment and data cleaning are context-dependent activities.
Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints
In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality.
The Causality/Repair Connection in Databases: Causality-Programs
In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes for query answers from databases.
The Ontological Multidimensional Data Model
In this extended abstract we describe, mainly by examples, the main elements of the Ontological Multidimensional Data Model, which considerably extends a relational reconstruction of the multidimensional data model proposed by Hurtado and Mendelzon by means of tuple-generating dependencies, equality-generating dependencies, and negative constraints as found in Datalog+-.
Enforcing Relational Matching Dependencies with Datalog for Entity Resolution
Entity resolution (ER) is about identifying and merging records in a database that represent the same real-world entity.
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations.
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt.
A Hybrid Approach to Query Answering under Expressive Datalog+/-
Datalog+/- is a family of ontology languages that combine good computational properties with high expressive power.
Extending Weakly-Sticky Datalog+/-: Query-Answering Tractability and Optimizations
We apply the magic-sets rewriting in combination with the proposed QA algorithm for the optimization of QA over JWS programs.
Consistency and Trust in Peer Data Exchange Systems
We propose and investigate a semantics for "peer data exchange systems" where different peers are related by data exchange constraints and trust relationships.
Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints
In this work we further investigate connections between query-answer causality and abductive diagnosis and the view-update problem.
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers).
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations.
Tractable Query Answering and Optimization for Extensions of Weakly-Sticky Datalog+-
We consider a semantic class, weakly-chase-sticky (WChS), and a syntactic subclass, jointly-weakly-sticky (JWS), of Datalog+- programs.
Repair-Based Degrees of Database Inconsistency: Computation and Complexity
We propose a generic numerical measure of the inconsistency of a database with respect to a set of integrity constraints.
Causality-based Explanation of Classification Outcomes
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.
An ASP-Based Approach to Counterfactual Explanations for Classification
We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to decisions produced by classification models.
Score-Based Explanations in Data Management and Machine Learning
We describe some approaches to explanations for observed outcomes in data management and machine learning.
Declarative Approaches to Counterfactual Explanations for Classification
In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely "responsibility scores".
On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results
While in general computing Shapley values is an intractable problem, we prove a strong positive result stating that the $\mathsf{SHAP}$-score can be computed in polynomial time over deterministic and decomposable Boolean circuits.
Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis
We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning.
Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification
We describe how answer-set programs can be used to declaratively specify counterfactual interventions on entities under classification, and reason about them.
Extending Sticky-Datalog+/- via Finite-Position Selection Functions: Tractability, Algorithms, and Optimization
We propose a bottom-up QA algorithm for programs in the class sch(S), for a general selection function S. As a particular case, we obtain a polynomial-time QA algorithm for JWS and weakly-sticky programs.
Second-Order Specifications and Quantifier Elimination for Consistent Query Answering in Databases
Consistent answers to a query from a possibly inconsistent database are answers that are simultaneously retrieved from every possible repair of the database.
Reasoning about Counterfactuals and Explanations: Problems, Results and Directions
There are some recent approaches and results about the use of answer-set programming for specifying counterfactual interventions on entities under classification, and reasoning about them.
Answer-Set Programs for Repair Updates and Counterfactual Interventions
We briefly describe -- mainly through very simple examples -- different kinds of answer-set programs with annotations that have been proposed for specifying: database repairs and consistent query answering; secrecy view and query evaluation with them; counterfactual interventions for causality in databases; and counterfactual-based explanations in machine learning.
Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence
In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among different approaches.
Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation
The use of Shap scores has become widespread in Explainable AI.
From Database Repairs to Causality in Databases and Beyond
We describe some recent approaches to score-based explanations for query answers in databases.
Attribution-Scores in Data Management and Explainable Machine Learning
We describe recent research on the use of actual causality in the definition of responsibility scores as explanations for query answers in databases, and for outcomes from classification models in machine learning.
The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions.
