Abstract. This paper explores the possibility to exploit text on the world wide web in order to enrich the concepts in existing ontologies. First, a method to retrieve documents from the WWW related to a concept is described. These document collections are used 1) to construct topic signatures (lists of topically related words) for each concept in WordNet, and 2) to build hierarchical clusters of the concepts (the word senses) that lexicalize a given word. The overall goal is to overcome two shortcomings of WordNet: the lack of topical links among concepts, and the proliferation of senses. Topic signatures are validated on a word sense disambiguation task with good results, which are improved when the hierarchical clusters are used.
We describe the automatic construction of a semantic network1, in which over 3000 of the most freque...
This paper presents a new approach to identifying concepts expressed in a collection of email messag...
In this paper a novel technique for identifying lexical contexts in web resources is presented. The ...
In spite of the growing of ontological engineering tools, ontology knowledge acquisition remains a h...
As a well-known semantic repository, WordNet is widely used in many applications. However, due to co...
We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sit...
Topic signatures are context vectors built for word senses and concepts. They can be automatically a...
We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sit...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
We describe the automatic construction of a semantic net-work1, in which over 3000 of the most frequ...
As a well-known semantic repository, WordNet is widely used in many applications. However, due to co...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
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...
This paper presents a new approach to identifying concepts expressed in a collection of email messag...
In this paper a novel technique for identifying lexical contexts in web resources is presented. The ...
In spite of the growing of ontological engineering tools, ontology knowledge acquisition remains a h...
As a well-known semantic repository, WordNet is widely used in many applications. However, due to co...
We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sit...
Topic signatures are context vectors built for word senses and concepts. They can be automatically a...
We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from Web sit...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
We describe the automatic construction of a semantic net-work1, in which over 3000 of the most frequ...
As a well-known semantic repository, WordNet is widely used in many applications. However, due to co...
This paper presents deepKnowNet, a new fully automatic method for building highly dense and accurate...
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...
This paper presents a new approach to identifying concepts expressed in a collection of email messag...
In this paper a novel technique for identifying lexical contexts in web resources is presented. The ...