Fundamenta Informaticae - Volume 172, issue 3

Authors: Badica, Costin | Ivanovic, Mirjana | Manolopoulos, Yannis | Rosati, Riccardo | Torroni, Paolo

Article Type: Other

DOI: 10.3233/FI-2020-1903

Citation: Fundamenta Informaticae, vol. 172, no. 3, pp. i-ii, 2020

Authors: Zupanc, Kaja | Bosnić, Zoran

Article Type: Research Article

Abstract: Automated essay evaluation is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, where grading models are learned on essays datasets scored by different graders. Despite the definition of the standardized grading rules, human graders can unintentionally introduce subjective bias into scores. Consequently, a grading model has to learn from data that represents a noisy relationship between essay attributes and its grade. We propose an approach for partitioning a set of essays into subsets that represent similar graders, which uses an explanation methodology …and clustering. The results confirm our assumption that learning from the ensemble of separated models can significantly improve the average prediction accuracy on artificial and real-world datasets. Show more

Keywords: automated essay evaluation, explanations of predictions, clustering, PCA

DOI: 10.3233/FI-2020-1904

Citation: Fundamenta Informaticae, vol. 172, no. 3, pp. 239-259, 2020

Authors: Flisar, Jernej | Podgorelec, Vili

Article Type: Research Article

Abstract: 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. Show more

Keywords: short text classification, DBPedia, ontology, text enrichment

DOI: 10.3233/FI-2020-1905

Citation: Fundamenta Informaticae, vol. 172, no. 3, pp. 261-297, 2020

Authors: Karampelas, Andreas | Vouros, George A.

Article Type: Research Article

Abstract: This paper proposes and evaluates time and space efficient methods for matching entities in large data sets based on effectively pruning the candidate pairs to be matched, using edit distance as a string similarity metric. The paper proposes and compares three filtering methods that build on a basic blocking technique to organize the target data set, facilitating efficient pruning of dissimilar pairs. The proposed filtering methods are compared in terms of runtime and memory usage: the first method clusters entities and exploits the triangle inequality using the string similarity metric, in conjunction to the substring matching filtering rule. The second …method uses only the substring matching rule, while the third method uses the substring matching rule in conjunction to the character frequency matching filtering rule. Evaluation results show the pruning power of the different filtering methods used, also in comparison to the string matching functionality provided in LIMES and SILK, which are state of the art frameworks for large scale link discovery. Show more

Keywords: Link discovery, string matching, edit distance, filtering rule, frequency matching

DOI: 10.3233/FI-2020-1906

Citation: Fundamenta Informaticae, vol. 172, no. 3, pp. 299-325, 2020