Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Declarative information extraction using datalog with embedded extraction predicates
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An Algebraic Approach to Rule-Based Information Extraction
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Semantic Modularity and Module Extraction in Description Logics
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Concept learning in description logics using refinement operators
Machine Learning
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
ONTOCOM: a cost estimation model for ontology engineering
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
DOG4DAG: semi-automated ontology generation in OBO-Edit and Protégé
Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
The logical difference for the lightweight description logic EL
Journal of Artificial Intelligence Research
Concept learning for EL++ by refinement and reinforcement
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Artificial Intelligence
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There exist a handful of natural language processing and machine learning approaches for extracting Description Logic concept definitions from natural language texts. Typically, for a single target concept several textual sentences are used, from which candidate concept descriptions are obtained. These candidate descriptions may have confidence values associated with them. In a final step, the candidates need to be combined into a single concept, in the easiest case by selecting a relevant subset and taking its conjunction. However, concept descriptions generated in this manner can contain false information, which is harmful when added to a formal knowledge base. In this paper, we claim that this can be improved by considering formal constraints that the target concept needs to satisfy. We first formalize a reasoning problem for the selection of relevant candidates and examine its computational complexity. Then, we show how it can be reduced to SAT, yielding a practical algorithm for its solution. Furthermore, we describe two ways to construct formal constraints, one is automatic and the other interactive. Applying this approach to the SNOMED CT ontology construction scenario, we show that the proposed framework brings a visible benefit for SNOMED CT development.