An algorithm for suffix stripping
Readings in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
User-System Cooperation in Document Annotation Based on Information Extraction
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Bootstrapping an ontology-based information extraction system
Intelligent exploration of the web
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
FilmEd - Collaborative Video Indexing, Annotation and Discussion Tools Over Broadband Networks
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Using corpus-derived name lists for named entity recognition
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A workbench for finding structure in texts
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
KnowItNow: fast, scalable information extraction from the web
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
OntoCAPE-A large-scale ontology for chemical process engineering
Engineering Applications of Artificial Intelligence
Learning non-taxonomic relationships from web documents for domain ontology construction
Data & Knowledge Engineering
k-ANMI: A mutual information based clustering algorithm for categorical data
Information Fusion
Pattern-based automatic taxonomy learning from the Web
AI Communications
Ontology-based information extraction and integration from heterogeneous data sources
International Journal of Human-Computer Studies
Locating complex named entities in web text
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Cerno: Light-weight tool support for semantic annotation of textual documents
Data & Knowledge Engineering
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
A methodology to learn ontological attributes from the Web
Data & Knowledge Engineering
Processing natural language without natural language processing
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Ontology-driven web-based semantic similarity
Journal of Intelligent Information Systems
Semantic Clustering Using Multiple Ontologies
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Automatic extraction of acronym definitions from the Web
Applied Intelligence
Content annotation for the semantic web: an automatic web-based approach
Knowledge and Information Systems
On the declassification of confidential documents
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Editorial: Special issue on semantic information and engineering systems
Engineering Applications of Artificial Intelligence
Extracting significant Website Key Objects: A Semantic Web mining approach
Engineering Applications of Artificial Intelligence
Semantic-ART: a framework for semantic annotation of regulatory text
Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval
Learning relation axioms from text: An automatic Web-based approach
Expert Systems with Applications: An International Journal
Two web-based approaches for noun sense disambiguation
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Scalable semantic annotation of text using lexical and web resources
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Transfer learning of syntactic structures for building taxonomies for search engines
Engineering Applications of Artificial Intelligence
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Data mining algorithms such as data classification or clustering methods exploit features of entities to characterise, group or classify them according to their resemblance. In the past, many feature extraction methods focused on the analysis of numerical or categorical properties. In recent years, motivated by the success of the Information Society and the WWW, which has made available enormous amounts of textual electronic resources, researchers have proposed semantic data classification and clustering methods that exploit textual data at a conceptual level. To do so, these methods rely on pre-annotated inputs in which text has been mapped to their formal semantics according to one or several knowledge structures (e.g. ontologies, taxonomies). Hence, they are hampered by the bottleneck introduced by the manual semantic mapping process. To tackle this problem, this paper presents a domain-independent, automatic and unsupervised method to detect relevant features from heterogeneous textual resources, associating them to concepts modelled in a background ontology. The method has been applied to raw text resources and also to semi-structured ones (Wikipedia articles). It has been tested in the Tourism domain, showing promising results.