Structural disambiguation is acknowledged as a very real and frequent problem for many semantic-aware applications. In this paper, we propose a unified answer to sense disambiguation on a large variety of structures both at data and metadata level such as relational schemas, XML data and schemas, taxonomies, and ontologies. Our knowledge-based approach achieves a general applicability by converting the input structures into a common format and by allowing users to tailor the extraction of the context to the specific application needs and structure characteristics. Flexibility is ensured by supporting the combination of different disambiguation methods together with different information extracted from different sources of knowledge. Further...
This work proposes a basic framework for resolving sense disambiguation through the use of Semantic ...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
There has been a tradition of combining different knowledge sources in Artificial Intelligence resea...
Structural disambiguation is acknowledged as a very real and frequent problem for many semantic-awar...
In this paper, we summarize the features of the versatile disambiguation approach we recentlty prese...
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in t...
In this paper, we propose a versatile disambiguation approach which can be used to make explicit the...
In this paper, we propose a versatile disambiguation approach which can be used to make explicit the...
We present results that show that incorporating lexical and structural semantic information is effec...
We present results that show that incorporating lexical and structural semantic information is effec...
Due to the complexity of natural language, sufficiently reliable Word Sense Disambiguation (WSD) sys...
Due to the complexity of natural language, sufficiently reliable Word Sense Disambiguation (WSD) sys...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depe...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
This work proposes a basic framework for resolving sense disambiguation through the use of Semantic ...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
There has been a tradition of combining different knowledge sources in Artificial Intelligence resea...
Structural disambiguation is acknowledged as a very real and frequent problem for many semantic-awar...
In this paper, we summarize the features of the versatile disambiguation approach we recentlty prese...
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. A break-through in t...
In this paper, we propose a versatile disambiguation approach which can be used to make explicit the...
In this paper, we propose a versatile disambiguation approach which can be used to make explicit the...
We present results that show that incorporating lexical and structural semantic information is effec...
We present results that show that incorporating lexical and structural semantic information is effec...
Due to the complexity of natural language, sufficiently reliable Word Sense Disambiguation (WSD) sys...
Due to the complexity of natural language, sufficiently reliable Word Sense Disambiguation (WSD) sys...
We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on ...
Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depe...
Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowled...
This work proposes a basic framework for resolving sense disambiguation through the use of Semantic ...
Understanding the user's intention is crucial for many tasks that involve human-machine interaction....
There has been a tradition of combining different knowledge sources in Artificial Intelligence resea...