Add to ORKGA novel method for unsupervised acquisition of knowledge for taxonomies of concepts from raw Wikipedia text is presented. We assume that the concepts classified under the same node in a taxonomy are described in a comparable way in Wikipedia. The concepts in 6 tax-onomies extracted from WordNet are mapped onto Wikipedia pages and the lexico-syntactic patterns describing semantic structures express-ing relevant knowledge for the concepts are au-tomatically learnt
Background knowledge as provided by repositories such as WordNet is of critical importance for linki...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...
Abstract—An ontology is a structured knowledgebase of concepts organized by relations among them. Bu...
In this paper, we present an approach for structural classification of taxonomies for knowledge acqu...
This thesis extracts conceptual structures from multiple sources: Wordnet, Web Corpora and Wikipedia...
ABSTRACT In the digital era, Wikipedia represents a comprehensive cross-domain source of knowledge w...
This paper presents a technique aimed to extract structured information from unstructured Wikipedia ...
AbstractThe category system in Wikipedia can be taken as a conceptual network. We label the semantic...
Abstract — Wikipedia, the largest online and free encyclopedia often extracts extraneous information...
We present a simple but effective method of automatically extracting domain-specific terms using Wik...
This thesis focuses on the design of algorithms for the extraction of knowledge (in terms of entitie...
There are many opportunities to improve the interactivity of information retrieval systems beyond th...
In this paper we present an approach for building a Wikipedia-based semantic network by integrating ...
This thesis deals with automatic type extraction in English Wikipedia articles and their attributes....
Background knowledge as provided by repositories such as WordNet is of critical importance for linki...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...
Abstract—An ontology is a structured knowledgebase of concepts organized by relations among them. Bu...
In this paper, we present an approach for structural classification of taxonomies for knowledge acqu...
This thesis extracts conceptual structures from multiple sources: Wordnet, Web Corpora and Wikipedia...
ABSTRACT In the digital era, Wikipedia represents a comprehensive cross-domain source of knowledge w...
This paper presents a technique aimed to extract structured information from unstructured Wikipedia ...
AbstractThe category system in Wikipedia can be taken as a conceptual network. We label the semantic...
Abstract — Wikipedia, the largest online and free encyclopedia often extracts extraneous information...
We present a simple but effective method of automatically extracting domain-specific terms using Wik...
This thesis focuses on the design of algorithms for the extraction of knowledge (in terms of entitie...
There are many opportunities to improve the interactivity of information retrieval systems beyond th...
In this paper we present an approach for building a Wikipedia-based semantic network by integrating ...
This thesis deals with automatic type extraction in English Wikipedia articles and their attributes....
Background knowledge as provided by repositories such as WordNet is of critical importance for linki...
Natural Language Processing systems crucially depend on the availability of lexical and conceptual k...
The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one sti...