ITKS5440 Semantic Web and Linked Data (5 cr)

Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Faculty of Information Technology
Curriculum periods:
2017-2018, 2018-2019, 2019-2020

Description

Content

This course (ITKS5440: Semantic Web and Linked Data, 5 ECTS) is an updated version of the former course (ITKS544: Semantic Web and Ontology Engineering, 5 ECTS). It includes an introduction and practical tutorial on the Semantic Web, ontology engineering; and also reviews some modern applications of these methods and techniques for Web-based intelligent applications and services. You will learn Semantic Web mission; concepts of semantic interoperability, integration and automation; concept of metadata and ontology; Semantic Web standards; RDF (Resource Description Framework); Linked Data; Ontology Engineering; OWL (Web Ontology Language); Rules for inferring knowledge; SWRL (Semantic Web Rules Language); Semantic Technology; Semantic (Web) Applications and Services. Course has 1 assignment (the basis for the grade) and no exam. Remote mode of study is possible. Some background info: The Semantic Web is originated from Semantic Computing which is an emergent field of Computing. It is a collaborative ongoing activity led by the World Wide Web Consortium (W3C) to promote common data formats on the World Wide Web specifically for machine-processable and machine understandable data aiming to convert the current web, dominated by unstructured and semi-structured documents, into a "web of data" (often referred as Web 3.0). The Semantic Web stack builds on the W3C's Resource Description Framework (RDF). Publishing machine-understandable data on the web is going as a mainstream. Linked Data (the activity originated from the Semantic Web vision) has seen explosive growth over the past few years. Linked Data assumes publishing structured data so that it can be interlinked with standard Web technologies such as HTTP, RDF and URIs, aiming to share information in a way that can be read automatically by computers. This enables data from different sources to be connected and queried. For example, DBPedia is a collection of data structured in RDF after being extracted from the Wikipedia, which allows Semantic Web-based applications to automatically infer implicit or new data and make advanced queries over the Wikipedia-derived dataset. The FOAF (Friend-of-a-Friend) is another example of how the Semantic Web attempts to make use of the data about people and their relationships within a social context. Organization of data based on RDF (graph) model makes it possible to connect data from distinct heterogeneous sources, organize and query huge volumes (Big Data challenge) of data. Ontologies are helpful to provide interoperability among various schemas used in the data and enable applications automatically discover and explore new previously unknown sources of data. Semantic-Web-standards-driven so-called Semantic Technology as a software technology allows the meaning of information to be known and processed at execution time of various applications making them naturally interoperable in the Web and within various digital ecosystems and clouds. Therefore as a summary: the Semantic Web is an evolving development of the World Wide Web in which the meaning (semantics) of information and services published on the Web and their inter-relationships are explicitly defined, making it possible for the Web-based software tools, agents, applications and systems to discover, extract and “understand” Web information resources and capabilities and automatically utilize it. Related to these, the Linked Data activity aims to expose, share, and connect distributed pieces of data, information, and knowledge; to extend the Web by publishing various open datasets and by setting semantic links between data items from different data sources. The Semantic Web vision assumes annotating Web resources with machine-interpretable descriptions (metadata) referred to shared conceptual vocabularies (ontologies), and provides mechanisms for automated reasoning about them.

Completion methods

Assignment (see details here: http://www.cs.jyu.fi/ai/vagan/itks544.html)

Assignment instructions:

· create your CV as part of your personal Web page preferably in your personal Web space provided by the university account, for example: http://users.jyu.fi/~abcdefgh/cv.html;

· create ontology with OWL (using Protégé ontology editing tool) needed for describing humans, entities, organizations, events, records, abstractions, etc., mentioned in your CV (or CVs like yours);

· semantically describe (annotate) yourself as a Web resource (with unique URI) following the story presented in your CV (together with other resources mentioned in it: people, universities, schools, companies, places, skills, files, documents, records, etc.) using RDF (link yourself with other relevant Web resources or physical World resources according to the ontology created in Protégé). In Protégé, the semantic annotation process means just creating a new instance in appropriate class and feeling with data (put values to slots) the form prepared by the ontology;

· please, do not provide any private/sensitive information, which is not meant to be shared in the Web;

· it will be appreciated if some (the more – the better) of these “other resources” in the neighbourhood of the target person will be found in and connected with other well-known open metadata repositories, such as, e.g., DBPedia, FOAF, etc.;

· it is supposed also that the group of “other resources” will include various types of media files (relevant texts, photos, videos, etc.) available in the Web;

· for doing the task above, please download and install version Protégé 3.5 from: http://protege.stanford.edu including Java VM;

· when creating a new project with Protégé select OWL/RDF files (by this way Protégé will combine in the same OWL file both: the ontology and the RDF semantic annotations);

· (!) please be very careful by specifying your ontology URI. It should correspond exactly to the Web URL of the ontology (depends on your personal Web space). For example: http://users.jyu.fi/~abcdefgh/cv-ontology.owl;

· when you save your project for the first time make sure that the name of the file will correspond to the one from the URI (e.g.: cv-ontology.owl for the example above);

· notice that Protégé in addition to OWL file (e.g., cv-ontology.owl ) will also create Protégé-specific files PPRJ (cv-ontology.pprj) and REPOSITORY (cv-ontology.repository);

· when finished working with Protégé do not forget to upload all three files (cv-ontology.owl , cv-ontology.pprj and cv-ontology.repository ) to your personal Web space so that their URLs will look like: http://users.jyu.fi/~abcdefgh/cv-ontology.owl ; http://users.jyu.fi/~abcdefgh/cv-ontology.pprj and http://users.jyu.fi/~abcdefgh/cv-ontology.repository ;

· provide report (e.g. in DOC file, where name of file is student’s capitalized family name), which consists at least of: (A) full name of the student; (B) name of the course; (C) the URL of the original CV (e.g., http://users.jyu.fi/~abcdefgh/cv.html); (D) the 3 links (e.g., http://users.jyu.fi/~abcdefgh/cv-ontology.owl ; http://users.jyu.fi/~abcdefgh/cv-ontology.pprj and http://users.jyu.fi/~abcdefgh/cv-ontology.repository) to your Protégé files; and (E) the Conclusion;

· In “Conclusion” part of the report please write your opinion, by which possible way (by what kind of applications) the semantic annotation of yourself according to the CVs and the appropriate ontology can be used;

· Do not remove appropriate files from the Web until final decision will be made;

· Files with report should be sent by e-mail to Vagan Terziyan until 15 November;

· Notification of evaluation - until 25 November.

Assessment details

(see details in: http://www.cs.jyu.fi/ai/vagan/itks544.html)

(see details in: http://www.cs.jyu.fi/ai/vagan/itks544.html)

Learning outcomes

Knowledge on Semantic Web, Linked Data, Ontology Engineering standards, technology and tools;
Capability to design Linked Data, metadata and ontologies;
Capability to structure, query and integrate data

Additional information

Study materials

Completion methods

Method 1

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