Temporal Knowledge Extraction for Dataset Discovery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00313816" target="_blank" >RIV/68407700:21230/17:00313816 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1927/" target="_blank" >http://ceur-ws.org/Vol-1927/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Temporal Knowledge Extraction for Dataset Discovery
Original language description
Linked data datasets are usually created with different data and metadata quality. This makes the exploration of these datasets a quite difficult task for the users. In this paper, we focus on improving discoverability of datasets based on their temporal characteristics. For this purpose, we identify the typology of temporal knowledge that can be observed inside data. We reuse existing temporal information extraction techniques available and employ them to create temporal search indices. We present a particular use-case of dataset discovery based on more detailed and completed temporal descriptions for each dataset in the Czech LOD cloud based on the analyzing of the unstructured content in the literals as well as the structured properties, taking into consideration varying data and metadata quality.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA16-09713S" target="_blank" >GA16-09713S: Efficient Exploration of Linked Data Cloud</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PROFILES 2017 Dataset Profiling and Federated Search for Web Data
ISBN
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ISSN
1613-0073
e-ISSN
1613-0073
Number of pages
15
Pages from-to
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Publisher name
CEUR-WS.org
Place of publication
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Event location
Vienna
Event date
Oct 21, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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