Semantic Information Processing
Teaching Staff: Poulos Marios
Course Code: ARC000478
Field: Library Science
Course Category: Specific Background
Course Type: Compulsory
Course Level: Undergraduate
Course Language: Greek
Delivery method: Face to face
Total Hours: 3
E Class Page: https://opencourses.ionio.gr/modules/auth/opencourses.php?fc=26
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.