Ontologies improve text document clustering

By Andreas Hotho, Steffen Staab, Gerd Stumme. Third IEEE International Conference on Data Mining, 2003

374 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey of the applications of text mining in financial domain (2016, background): “It contains the domain knowledge and these are in the form of relationships (Hotho et al. 2003) between the entities i.e. concepts and their level order.”
Clustering short texts using wikipedia (2007, background): “It has been observed previously that additional features from WordNet can improve clustering results [2].”
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis (2005, background): “Further, the fact that they allow to generalize over words has shown to provide benefits in a number of applications such as Information Retrieval (Voorhees, 1994) as well as text clustering ( Hotho, Staab, & Stumme, 2003 ) and classification (Bloehdorn & Hotho, 2004).”
A Brief Survey of Text Mining (2005, background): “Recently some progress was made by enhancing bag of words with linguistic feature for text clustering and classiﬁcation [HSS03, BH04].”
Data clustering: 50 years beyond K-means (2008, neutral): “However, these are mostly feature constraints and not constraints on the instances (Hotho et al., 2003; Liu et al., 2004; Banerjee et al., 2007b).”
QOM - Quick Ontology Mapping (2004, background): “The problem of mapping two ontologies effectively and efficiently arises in many application scenarios [4, 5].”