The course aims at covering the teaching needs of undergraduate students with Information Science orientation. According to the above, three areas were emphasized. In the first section of the introduction, the concept of information and the methodological approach of its elaboration are described, namely the syntactic and the statistical one, while in the first chapter the current practices of its recovery are developed. In the second field, especially in chapter (2), there is an extensive reference to the editorial processing of information, on information technology issues such as metadata formats, conclusions, search engines using semantic languages and the fundamental development of reasoning and machine deduction. Also in the fourth (4th) chapter the concept of ontology is expanded as well as the ontology writing languages. In the third field, namely in the third (3rd) chapter, there are developed areas which a priori solve linguistic interoperability problems in the methodologies of editorial processing of information and these are computational and quantitative linguistics. Finally, in the third field and especially in chapters (5) and (6) six, the taxonomic methodologies of information are developed by statistical approach followed by the respective applications.

The aim of the course is to give students a complete and comprehensive picture of the current reality and trends in the field of technologies that support the task of extracting knowledge and especially the aim of the above-mentioned modules was to provide a comprehensive map of semantics information.

The main objective of the course is to enable future scientists and information professionals to be able to properly manage the issues of choosing, introducing and using new information technologies and, on the other, to develop future research activities in these areas.

Upon successful completion of the course the student will be able to: understand and handle applications related to knowledge extraction.

Week #1: Introduction to Semantic Processing of Information

Week #2: Syntactic and Statistical Approach to Semantic Information Processing

Week #3: Information Retrieval Issues

Week #4: IR models and ranking

Week #5: Semantic Models (XML, DTD, RDF)

Week #6: Representation of Knowledge

Week #7: Inference Mechanisms

Week #8: Web search engines based on semantics

Week #9: Digital Libraries based on semantics

Week #10: Quantitative & Computational Linguistics

Week #11: Principles of textuality

Week #12: Ontological Approaches - Agents

Week #13: Στατιστική Επεξεργασία της Πληροφορίας (Νευρωνικά Δίκτυα)

• Baixeries J., Hernández-Fernández A., Ferrer-i-Cancho R. (2012), Random models of Menzerath–Altmann law in genomes. Biosystems. 107 (3), 167–173.
• Carstens W. (2001). Text Linguistics: relevant linguistics? Poetics and linguistics; discources of war and conflict Conference. 588-595.
• Cutts, M. (2009). Oxford guide to plain English. 3rded. Oxford: Oxford University Press.
• Harispe S. et al. (2013). Semantic measures for the comparison of units of language, concepts or entities from text and knowledge base analysis. Arxiv. 1310. 1285. 1-159.
• Harispe, S. et al. (2013). The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics. 30 (5). Oxford: Oxford University Press.
• Kornai A. (2008). Mathematical linguistics. [online]. Advanced Information and Knowledge Processing.
• White, A. S., Hacquard, V., & Lidz, J. (2018). Semantic information and the syntax of propositional attitude verbs. Cognitive science, 42(2), 416-456.

Lectures with ppt presentations.

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Written exam.