Class-based n-gram models of natural language
Computational Linguistics
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Amilcare: adaptive information extraction for document annotation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
OIL: An Ontology Infrastructure for the Semantic Web
IEEE Intelligent Systems
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Extracting knowledge from XML document repository: a semantic Web-based approach
Information Technology and Management
Learning non-taxonomic relationships from web documents for domain ontology construction
Data & Knowledge Engineering
Journal of Biomedical Informatics
ANITA: a narrative interpretation of taxonomies for their adaptation to text collections
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Ontology population and enrichment: state of the art
Knowledge-driven multimedia information extraction and ontology evolution
Editorial: Narrative-based taxonomy distillation for effective indexing of text collections
Data & Knowledge Engineering
On the need to bootstrap ontology learning with extraction grammar learning
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
Populating an allergens ontology using natural language processing and machine learning techniques
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
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Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.