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We introduce negation under the stable model semantics in DatalogMTL - a temporal extension of Datalog with metric temporal operators. As a result, we obtain a rule language which combines the power of answer set programming with the temporal dimensi
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https://explore.openaire.eu/search/publication?articleId=od______1064::59cbe023e48ace663a03fc3d4de777ed
https://ora.ox.ac.uk/objects/uuid:1f080f80-8bf6-4448-9058-1b004123ffed
https://ora.ox.ac.uk/objects/uuid:1f080f80-8bf6-4448-9058-1b004123ffed
Although there has been significant interest in applying machine learning techniques to structured data, the expressivity (i.e., a description of what can be learned) of such techniques is still poorly understood. In this paper, we study data transfo
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https://ora.ox.ac.uk/objects/uuid:a0f2dd6a-b5fe-413b-9c07-bd9679512f7e
https://ora.ox.ac.uk/objects/uuid:a0f2dd6a-b5fe-413b-9c07-bd9679512f7e
DatalogMTL is a recently introduced extension of Datalog with operators from metric temporal logic (MTL). It allows for performing complex temporal reasoning tasks over the rational timeline, which makes it suitable for many practical applications. A
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https://explore.openaire.eu/search/publication?articleId=od______1064::aa8d37f9855610f3a900d41203bec77b
https://ora.ox.ac.uk/objects/uuid:5ad1df3d-7a4c-496d-a9ed-0830e3e88639
https://ora.ox.ac.uk/objects/uuid:5ad1df3d-7a4c-496d-a9ed-0830e3e88639
Publikováno v:
Journal of Artificial Intelligence Research. 76
DatalogMTL is an extension of Datalog with metric temporal operators that has recently found applications in stream reasoning and temporal ontology-based data access. In contrast to plain Datalog, where materialisation (a.k.a. forward chaining) natur
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https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a81ace5a4c2a826a2aa8a8a81cf24aa
https://doi.org/10.1613/jair.1.14040
https://doi.org/10.1613/jair.1.14040
DatalogMTL is a powerful extension of Datalog with operators from metric temporal logic (MTL), which has received significant attention in recent years. In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the co
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https://explore.openaire.eu/search/publication?articleId=od______1064::e413c7b79af7aa9e88c8f848146e9e72
https://ora.ox.ac.uk/objects/uuid:cac0a9ce-8e2b-427d-8740-f39efe1bd88a
https://ora.ox.ac.uk/objects/uuid:cac0a9ce-8e2b-427d-8740-f39efe1bd88a
The problem of answering complex First-order Logic queries over incomplete knowledge graphs is receiving growing attention in the literature. A promising recent approach to this problem has been to exploit neural link predictors, which can be effecti
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https://ora.ox.ac.uk/objects/uuid:27c9b239-20d0-449f-b425-b5065eb128fe
https://ora.ox.ac.uk/objects/uuid:27c9b239-20d0-449f-b425-b5065eb128fe
Akademický článek
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Graph Neural Networks (GNNs) are often used to realise learnable transformations of graph data. While effective in practice, GNNs make predictions via numeric manipulations in an embedding space, so their output cannot be easily explained symbolicall
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https://ora.ox.ac.uk/objects/uuid:5d732bae-b80a-4439-8b4d-918a413a1765
https://ora.ox.ac.uk/objects/uuid:5d732bae-b80a-4439-8b4d-918a413a1765