Adding Context to Knowledge and Action Bases
Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution. In this work, we enrich the KAB setting with contextual information, making use of different context dimensions. On the one hand, context is determined by the environment using context-changing actions that make use of the current state of the KB and the current context. On the other hand, it affects the set of TBox assertions that are relevant at each time point, and that have to be considered when processing queries posed over the KAB. Here we extend to our enriched setting the results on verification of rich temporal properties expressed in mu-calculus, which had been established for standard KABs. Specifically, we show that under a run-boundedness condition, verification stays decidable.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured informatio…
RelationCrowdsourced Databases and Sui Generis Rights
In this study we propose a new concept of databases (crowdsourced databases), adding a new conceptual approach to the debate on legal protection of databases in Europe. We also summarise the current legal framework and c…
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Knowledge graph completion aims to address the gaps of knowledge bases by adding new triples that represent facts. The complexity of this task depends on how many parts of a triple are already known. Instance completion …
Knowledge Graph CompletionKnowledge GraphsLink PredictionMulti-Label Classification+2Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable …
A multi-task semi-supervised framework for Text2Graph & Graph2Text
The Artificial Intelligence industry regularly develops applications that mostly rely on Knowledge Bases, a data repository about specific, or general, domains, usually represented in a graph shape. Similar to other data…
Information RetrievalRetrievalText Generation