Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Incremental formalization of document annotations through ontology-based paraphrasing
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
Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
Ontology learning from domain specific web documents
International Journal of Metadata, Semantics and Ontologies
KDTA: automated knowledge-driven text annotation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
An accuracy-enhanced light stemmer for arabic text
ACM Transactions on Speech and Language Processing (TSLP)
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
A framework for semantic annotation of digital evidence
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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This work exploits the logical structure of information rich texts to automatically annotate text segments contained within them using a domain ontology. The underlying assumption behind this work is that segments in such documents embody self contained informative units. Another assumption is that segment headings coupled with a document's hierarchical structure offer informal representations of segment content; and that matching segment headings to concepts in an ontology/thesaurus can result in the creation of formal labels/meta-data for these segments. When an encountered heading can not be matched with any concepts in the ontology, the hierarchical structure of the document is used to infer where a new concept represented by this heading should be added in the ontology. So, in this work the bootstrap ontology is also enriched by new concepts encountered within input documents. This paper also presents issues/problems related to matching textual entities to concepts in an incomplete ontology. The approach presented in this paper was applied to a set of agricultural extension documents. The results of carrying out this experiment demonstrates that the proposed approach is capable of automatically annotating segments with concepts that describe a segment's content with a high degree of accuracy.