Ontology type: schema:Chapter
2021-05-31
AUTHORSAndreas Steigmiller , Birte Glimm
ABSTRACTAutomated reasoning support is an important aspect of logic-based knowledge representation. The development of specialised procedures and sophisticated optimisation techniques significantly improved the performance even for complex reasoning tasks such as conjunctive query answering. Reasoning and query answering over knowledge bases with a large number of facts and expressive schemata remains, however, challenging.We propose a novel approach where the reasoning over assertional knowledge is split into small, similarly sized work packages to enable a parallelised processing with tableau algorithms, which are dominantly used for reasoning with more expressive Description Logics. To retain completeness in the presence of expressive schemata, we propose a specifically designed cache that allows for controlling and synchronising the interaction between the constructed partial models. We further report on encouraging performance improvements for the implementation of the techniques in the tableau-based reasoning system Konclude. More... »
PAGES23-39
The Semantic Web
ISBN
978-3-030-77384-7
978-3-030-77385-4
http://scigraph.springernature.com/pub.10.1007/978-3-030-77385-4_2
DOIhttp://dx.doi.org/10.1007/978-3-030-77385-4_2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1138468558
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Ulm University, Ulm, Germany",
"id": "http://www.grid.ac/institutes/grid.6582.9",
"name": [
"Ulm University, Ulm, Germany"
],
"type": "Organization"
},
"familyName": "Steigmiller",
"givenName": "Andreas",
"id": "sg:person.013641624343.88",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641624343.88"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Ulm University, Ulm, Germany",
"id": "http://www.grid.ac/institutes/grid.6582.9",
"name": [
"Ulm University, Ulm, Germany"
],
"type": "Organization"
},
"familyName": "Glimm",
"givenName": "Birte",
"id": "sg:person.015234565343.35",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015234565343.35"
],
"type": "Person"
}
],
"datePublished": "2021-05-31",
"datePublishedReg": "2021-05-31",
"description": "Automated reasoning support is an important aspect of logic-based knowledge representation. The development of specialised procedures and sophisticated optimisation techniques significantly improved the performance even for complex reasoning tasks such as conjunctive query answering. Reasoning and query answering over knowledge bases with a large number of facts and expressive schemata remains, however, challenging.We propose a novel approach where the reasoning over assertional knowledge is split into small, similarly sized work packages to enable a parallelised processing with tableau algorithms, which are dominantly used for reasoning with more expressive Description Logics. To retain completeness in the presence of expressive schemata, we propose a specifically designed cache that allows for controlling and synchronising the interaction between the constructed partial models. We further report on encouraging performance improvements for the implementation of the techniques in the tableau-based reasoning system Konclude.",
"editor": [
{
"familyName": "Verborgh",
"givenName": "Ruben",
"type": "Person"
},
{
"familyName": "Hose",
"givenName": "Katja",
"type": "Person"
},
{
"familyName": "Paulheim",
"givenName": "Heiko",
"type": "Person"
},
{
"familyName": "Champin",
"givenName": "Pierre-Antoine",
"type": "Person"
},
{
"familyName": "Maleshkova",
"givenName": "Maria",
"type": "Person"
},
{
"familyName": "Corcho",
"givenName": "Oscar",
"type": "Person"
},
{
"familyName": "Ristoski",
"givenName": "Petar",
"type": "Person"
},
{
"familyName": "Alam",
"givenName": "Mehwish",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-030-77385-4_2",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-030-77384-7",
"978-3-030-77385-4"
],
"name": "The Semantic Web",
"type": "Book"
},
"keywords": [
"expressive description logics",
"description logics",
"logic-based knowledge representation",
"complex reasoning tasks",
"sophisticated optimization techniques",
"expressive schemas",
"query answering",
"conjunctive queries",
"reasoning support",
"ABox reasoning",
"knowledge representation",
"assertional knowledge",
"knowledge bases",
"tableau algorithm",
"queries",
"optimization techniques",
"performance improvement",
"reasoning tasks",
"partial models",
"novel approach",
"reasoning",
"logic",
"work packages",
"large number",
"Answering",
"cache",
"algorithm",
"schema",
"important aspect",
"task",
"specialised procedures",
"implementation",
"technique",
"representation",
"processing",
"package",
"performance",
"completeness",
"knowledge",
"model",
"support",
"aspects",
"improvement",
"number",
"development",
"fact",
"basis",
"interaction",
"procedure",
"approach",
"presence",
"remains"
],
"name": "Parallelised ABox Reasoning and Query Answering with Expressive Description Logics",
"pagination": "23-39",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1138468558"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-030-77385-4_2"
]
}
],
"publisher": {
"name": "Springer Nature",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-030-77385-4_2",
"https://app.dimensions.ai/details/publication/pub.1138468558"
],
"sdDataset": "chapters",
"sdDatePublished": "2022-06-01T22:30",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/chapter/chapter_259.jsonl",
"type": "Chapter",
"url": "https://doi.org/10.1007/978-3-030-77385-4_2"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-77385-4_2'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-77385-4_2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-77385-4_2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-77385-4_2'
This table displays all metadata directly associated to this object as RDF triples.
154 TRIPLES
23 PREDICATES
77 URIs
70 LITERALS
7 BLANK NODES