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Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing:
- solutions by mathematical methods of problems emerging in computer science
- solutions of mathematical problems inspired by computer science.
Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, & algebraic and categorical methods.
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
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