Learning relation axioms from text: An automatic Web-based approach
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
Ontology-based semantic similarity: A new feature-based approach
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
Identifying conceptual layers in the ontology development process
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Information Sciences: an International Journal
Preventing automatic user profiling in Web 2.0 applications
Knowledge-Based Systems
A semantic similarity method based on information content exploiting multiple ontologies
Expert Systems with Applications: An International Journal
Semantic similarity estimation from multiple ontologies
Applied Intelligence
Using profiling techniques to protect the user's privacy in twitter
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Detecting sensitive information from textual documents: an information-theoretic approach
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge
International Journal on Semantic Web & Information Systems
An automatic approach for ontology-based feature extraction from heterogeneous textualresources
Engineering Applications of Artificial Intelligence
Towards the estimation of feature-based semantic similarity using multiple ontologies
Knowledge-Based Systems
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Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.