AbstractThe category system in Wikipedia can be taken as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexico-syntactic matching. The result is a large scale taxonomy. For evaluation we propose a method which (1) manually determines the quality of our taxonomy, and (2) automatically compares its coverage with ResearchCyc, one of the largest manually created ontologies, and the lexical database WordNet. Additionally, we perform an extrinsic evaluation by computing semantic similarity between words in benchmarking datasets. The results show that the taxonomy compares favorably in quality and coverage with broad-coverage manually created resources
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
AbstractThe category system in Wikipedia can be taken as a conceptual network. We label the semantic...
We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
We present a knowledge-rich methodology for dis-ambiguating Wikipedia categories with WordNet synset...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
AbstractA knowledge base for real-world language processing applications should consist of a large b...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
AbstractThe category system in Wikipedia can be taken as a conceptual network. We label the semantic...
We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
We present a knowledge-rich methodology for dis-ambiguating Wikipedia categories with WordNet synset...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
AbstractA knowledge base for real-world language processing applications should consist of a large b...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The Wikipedia category graph serves as the taxonomic backbone for large-scale knowledge graphs like ...
The hyperlink structure of Wikipedia constitutes a key resource for many Natural Language Processing...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...