SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring semantic similarity in the taxonomy of WordNet
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Versatile structural disambiguation for semantic-aware applications
Proceedings of the 14th ACM international conference on Information and knowledge management
Fast and effective clustering of XML data using structural information
Knowledge and Information Systems
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
A flexible structured-based representation for XML document mining
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Transforming XML trees for efficient classification and clustering
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Evaluating PageRank methods for structural sense ranking in labeled tree data
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
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A common limit of most existing methods that manage XML structure information is that they do not handle the semantic meanings that might be associated to the markup tags. In this paper, we study how to map structure information available from XML elements into semantically related concepts in order to support the generation of XML semantic features of XML structural type. For this purpose, we define an unsupervised word sense disambiguation method to select the most appropriate meaning for each element contextually to its respective XML path. The proposed approach exploits conceptual relations provided by a lexical ontology such as WordNet and employs different notions of sense relatedness . Experiments with data from various application domains are discussed, showing that our approach can be effectively used to generate structural semantic features.