%0 Book Section %1 Fernandez_Gil2023-zu %A Fernández Gil, Oliver %A Patrizi, Fabio %A Perelli, Giuseppe %A Turhan, Anni-Yasmin %B Frontiers in Artificial Intelligence and Applications %D 2023 %I IOS Press %K topic_knowledge %T Optimal alignment of temporal knowledge bases %X Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-) optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.

@incollection{Fernandez_Gil2023-zu, abstract = {Answering temporal CQs over temporalized Description Logic knowledge bases (TKB) is a main technique to realize ontology-based situation recognition. In case the collected data in such a knowledge base is inaccurate, important query answers can be missed. In this paper we introduce the TKB Alignment problem, which computes a variant of the TKB that minimally changes the TKB, but entails the given temporal CQ and is in that sense (cost-) optimal. We investigate this problem for ALC TKBs and conjunctive queries with LTL operators and devise a solution technique to compute (cost-optimal) alignments of TKBs that extends techniques for the alignment problem for propositional LTL over finite traces.}, added-at = {2024-09-10T10:41:24.000+0200}, author = {Fern{\'a}ndez Gil, Oliver and Patrizi, Fabio and Perelli, Giuseppe and Turhan, Anni-Yasmin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2d3036ad32dbfa711f78478971709d4c6/scadsfct}, booktitle = {Frontiers in Artificial Intelligence and Applications}, interhash = {f4baeb08263706f0f7637f3a282c2f5a}, intrahash = {d3036ad32dbfa711f78478971709d4c6}, keywords = {topic_knowledge}, month = sep, publisher = {IOS Press}, series = {Frontiers in artificial intelligence and applications}, timestamp = {2024-11-28T17:41:10.000+0100}, title = {Optimal alignment of temporal knowledge bases}, year = 2023 }