paper-with-me

홈 › Papers

A New Rational Algorithm for View Updating in Relational Databases

2014-07-13 · Radhakrishnan Delhibabu, Andreas Behrend

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.

📄 PDF Abstract BibTeX arXiv:1407.3512

Code (0)

등록된 구현이 없습니다.

Tasks

Negation

Similar Papers 제목 키워드 기반

Operational Change Detection for Geographical Information: Overview and Challenges

2025-03-18 · Nicolas Gonthier

Rapid evolution of territories due to climate change and human impact requires prompt and effective updates to geospatial databases maintained by the National Mapping Agency. This paper presents a comprehensive overview …

Change Detection

Relational Deep Learning: Graph Representation Learning on Relational Databases

2023-12-07 · Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen 외

Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning mode…

Deep LearningFeature EngineeringGraph Representation LearningRepresentation Learning

A Self-Explainable Heterogeneous GNN for Relational Deep Learning

2024-11-30 · Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger

Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existin…

Deep LearningGraph Neural Network

Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data

2026-06-10 · Arie Soeteman, Balder ten Cate, Maurice Funk, Benny Kimelfeld 외 arxiv

The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent approaches instead operate on databases …

Relational Reasoning

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

2020-02-24 · Tal Friedman, Guy Van Den Broeck

We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with r…