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Found 17 papers, 5 papers with code
The DLV System for Knowledge Representation and Reasoning
As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems.
A Backtracking-Based Algorithm for Computing Hypertree-Decompositions
Hypertree decompositions of hypergraphs are a generalization of tree decompositions of graphs.
Deciding Monotone Duality and Identifying Frequent Itemsets in Quadratic Logspace
The monotone duality problem is defined as follows: Given two monotone formulas f and g in iredundant DNF, decide whether f and g are dual.
General and Fractional Hypertree Decompositions: Hard and Easy Cases
It is known that hw(H) <= k can be checked in polynomial time for fixed k, while checking ghw(H) <= k is NP-complete for any k greater than or equal to 3.
Databases Computational Complexity
Tree Projections and Constraint Optimization Problems: Fixed-Parameter Tractability and Parallel Algorithms
Solution methods have therefore been proposed in the literature that do not require a tree projection to be given, and that either correctly decide whether the given CSP instance is satisfiable, or return that a tree projection actually does not exist.
Datalog: Bag Semantics via Set Semantics
This use of Datalog$^\pm$ is extended to give a set semantics to duplicates in Datalog$^\pm$ itself.
Data Science with Vadalog: Bridging Machine Learning and Reasoning
Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning.
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable.
The Space-Efficient Core of Vadalog
Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs.
HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings
To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed -- giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw).
Databases
HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings
To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed -- giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw).
The HyperTrac Project: Recent Progress and Future Research Directions on Hypergraph Decompositions
Constraint Satisfaction Problems (CSPs) play a central role in many applications in Artificial Intelligence and Operations Research.
On the Complexity of Inductively Learning Guarded Rules
We investigate the computational complexity of mining guarded clauses from clausal datasets through the framework of inductive logic programming (ILP).
Non-Uniformly Terminating Chase: Size and Complexity
In this context, a key problem is non-uniform chase termination: does the chase of a database w. r. t.
Incremental Updates of Generalized Hypertree Decompositions
As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard.
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
The aim of this study is to investigate Machine Unlearning (MU), a burgeoning field focused on addressing concerns related to neural models inadvertently retaining personal or sensitive data.
Fuzzy Datalog$^\exists$ over Arbitrary t-Norms
One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data.
