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Improving Short Text Classification using Information from DBpedia Ontology

Flisar Jernej, Podgorelec Vili

Fundamenta Informaticae

2020

Vol. 172, nr 3

261--297

With the emergence of social networks and micro-blogs, a huge amount of short textual documents are generated on a daily basis, for which effective tools for organization and classification are needed. These short text documents have extremely sparse representation, which is the main cause for the poor classification performance. We propose a new approach, where we identify relevant concepts in short text documents with the use of the DBpedia Spotlight framework and enrich the text with information derived from DBpedia ontology, which reduces the sparseness. We have developed six variants of text enrichment methods and tested them on four short text datasets using seven classification algorithms. The obtained results were compared to those of the baseline approach, among themselves, and also to some state-of-the-art text classification methods. Beside classification performance, the influence of the concepts similarity threshold and the size of the training data were also evaluated. The results show that the proposed text enrichment approach significantly improves classification of short texts and is robust with respect to different input sources, domains, and sizes of available training data. The proposed text enrichment methods proved to be beneficial for classification of short text documents, especially when only a small amount of documents are available for training.

Evaluating semantic similarity with a new method of path analysis in RDF using genetic algorithms

Strobin Ł, Niewiadomski A.

Journal of Applied Computer Science

2013

Vol. 21, nr 2

137--152

This paper presents a novel method of evaluating semantic similarity by means of path analysis in RDF databases. Similarity is calculated by assignining each property (predicate in RDF terms) a weight, which is found using a genetic optimization algorithm. Presented method exhibits an advatage over existing methods, because of its ﬂexibility and the fact that no prior knowledge of a particular database is necessary. This paper also presents an exemplary application of the method - recommendation engine. Proposed method is applied to a well known problem - music recommendation based on DBPedia. Results obtained in the experiment positively verify its advanntages and usefulness.