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Julio Lemos

dblp:139/1028 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Computational complexity · 100%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology
0.312017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology-based query answering
0.312017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
Computational complexity › complexity of reasoning
data complexity
0.312017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
Computational complexity › constraint satisfaction › dichotomy theorem
dichotomy classification
0.312017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
Computational complexity
constraint satisfaction
0.112017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
Computational complexity › constraint satisfaction › infinite-domain constraint satisfaction
temporal constraint satisfaction
0.112017
Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach · AAAI 2017
YearPublicationVenuePosition
2017 Query Answering in DL-Lite with Datatypes: A Non-Uniform Approach
abstract
Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems.
André Hernich, Julio Lemos, Frank Wolter
AAAI2