A neural network for semantic labelling of structured information. (1st April 2020)

• Record Type: Journal Article
• Authors: Ayala, Daniel; Borrego, Agustín; Hernández, Inma; Ruiz, David
• Is Part Of: Expert systems with applications. Volume 143(2020)
• Publication Date: 2020-04-01
• Subjects: Semantic labelling -- Information integration -- Neural networks; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113053
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel approach to perform semantic labelling with neural networks is presented. The existing proposals focus on features engineering instead of classification techniques. Neural networks handle well the large number of features and allow nonlinearity. Experimental results show consistent improvement in every tested scenario. Tests with different subsets of features compare their usefulness and impact. Abstract: Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.