dc.contributor.author |
Klarman, S
|
|
dc.contributor.author |
Britz, K
|
|
dc.date.accessioned |
2016-02-23T09:10:20Z |
|
dc.date.available |
2016-02-23T09:10:20Z |
|
dc.date.issued |
2015-07 |
|
dc.identifier.citation |
Klarman, S and Britz, K. 2015. Towards unsupervised ontology learning from data. In: International Workshop on Defeasible and Ampliative Reasoning (DARe-15), Buenos Aires, Argentina, July 2015 |
en_US |
dc.identifier.uri |
http://ceur-ws.org/Vol-1423/DARe-15_5.pdf
|
|
dc.identifier.uri |
http://ceur-ws.org/Vol-1423/
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/8414
|
|
dc.description |
International Workshop on Defeasible and Ampliative Reasoning (DARe-15), Buenos Aires, Argentina, 25-27 July 2015 |
en_US |
dc.description.abstract |
Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital — yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
CEUR-WS |
en_US |
dc.relation.ispartofseries |
Workflow;15627 |
|
dc.subject |
Ontologies |
en_US |
dc.subject |
Ontology Learning |
en_US |
dc.subject |
Description logic TBoxes |
en_US |
dc.subject |
Description logic EL |
en_US |
dc.subject |
Finite Learning Sets |
en_US |
dc.subject |
Learning Algorithms |
en_US |
dc.subject |
Learning Model |
en_US |
dc.title |
Towards unsupervised ontology learning from data |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Klarman, S., & Britz, K. (2015). Towards unsupervised ontology learning from data. CEUR-WS. http://hdl.handle.net/10204/8414 |
en_ZA |
dc.identifier.chicagocitation |
Klarman, S, and K Britz. "Towards unsupervised ontology learning from data." (2015): http://hdl.handle.net/10204/8414 |
en_ZA |
dc.identifier.vancouvercitation |
Klarman S, Britz K, Towards unsupervised ontology learning from data; CEUR-WS; 2015. http://hdl.handle.net/10204/8414 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Klarman, S
AU - Britz, K
AB - Data-driven elicitation of ontologies from structured data is a well-recognized knowledge acquisition bottleneck. The development of efficient techniques for (semi-)automating this task is therefore practically vital — yet, hindered by the lack of robust theoretical foundations. In this paper, we study the problem of learning Description Logic TBoxes from interpretations, which naturally translates to the task of ontology learning from data. In the presented framework, the learner is provided with a set of positive interpretations (i.e., logical models) of the TBox adopted by the teacher. The goal is to correctly identify the TBox given this input. We characterize the key constraints on the models that warrant finite learnability of TBoxes expressed in selected fragments of the Description Logic EL and define corresponding learning algorithms.
DA - 2015-07
DB - ResearchSpace
DP - CSIR
KW - Ontologies
KW - Ontology Learning
KW - Description logic TBoxes
KW - Description logic EL
KW - Finite Learning Sets
KW - Learning Algorithms
KW - Learning Model
LK - https://researchspace.csir.co.za
PY - 2015
T1 - Towards unsupervised ontology learning from data
TI - Towards unsupervised ontology learning from data
UR - http://hdl.handle.net/10204/8414
ER -
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en_ZA |