Email: vagan.terziyan@jyu.fi
(Register for
the course in SISU
system)
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Schedule (Fall, 2024):
ITKS-5440: Semantic Web and Linked Data (5 ECTS)
Course Summary:
Artificial
Intelligence has two major sides, which complement each other: the Bottom-Up AI
driven by Machine (also Deep) Learning; and the Top-Down AI driven by
formalized human knowledge. This course (ITKS-5440: Semantic Web and Linked
Data) corresponds to the Top-Down AI family of approaches and includes an
introduction and practical tutorial on the RDF-based semantic annotation of Web
resources and services for the Semantic Web, Linked Data and Ontology
Engineering; and also review some modern applications of these methods and
techniques for Web-based intelligent applications and services.
Main Content Components
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; Relation to Big
Data and Industry 4.0.
Course-Related Context and Motivation:
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.
Relation of the course with Master Programs of the
Faculty of Information Technology:
Master
Program on Artificial Intelligence is the
natural place for such course because this Program explores different aspects
and services from Deep Learning, Big Data analytics and Web-based Cognitive
Computing, which require Semantic (Web) Technology to enable self-management
and to handle heterogeneity of information, technology capabilities and users.
Learning outcomes of this course are assumed to be an input to several other
courses of the AI, WISE and COIN programs (e.g., https://ai.it.jyu.fi/vagan/courses.html, Deep Learning for Cognitive Computing, SOA
and Cloud Computing; Design of Agent-Based Systems; Collective Intelligence and
Agent Technology; Interface of Things; Big Data Engineering and others).
Among
other Master programs the closest one is Data
Analysis (or similar) program as the course provides the framework and
advertises tools for machine-processable data in the Web.
The course is also suitable for the Cyber Security (or similar) Master
Program as it is known that the so-called "Web of Trust" is one of
the ultimate goals of the Semantic Web. Research on the topic of trust in this
domain has focused largely on digital signatures, certificates, and authentication
as well as trust in social networks.
The Software
Engineering Master Program can benefit from the semantic technology,
semantic programming, semantic applications, self-managed systems engineering,
and the open world assumption in software design originated from the Semantic
Web vision and based on appropriate standards.
The best evidence on having the
ITKS-5440 course naturally relevant to most of master programs of the MIT
department (e.g., Data Analysis, Cyber Security, variations of Computational
Science, and others) is given by Amit Sheth (h-index > 80) in http://amitsheth.blogspot.fi/2007/10/what-is-semantic-computing.html as follows: “Semantic computing is a vision of computing
based on semantics shared between machines and people. It supports and exploits
intrinsic, intended, and emergent meanings (content) in all aspects of
computing, encompassing programming, algorithms, information management, and
human interactions within devices, as part of communications, and across the
Web. Semantics involves the use of formal descriptions, languages, and models,
often encoded in metadata, knowledge, and representation of agreements (as in
ontologies) to capture the content of multimedia, texts, services, and
structured data so that it may be extracted, shared, synthesized and
transformed. Semantic techniques foster the development emerging forms of
computing, such as semantic Web, and entirely new forms, such as bio-inspired
computing, as well as enhance traditional techniques of information retrieval,
management of data (including multimedia and multimodal) and artificial
intelligence (e.g., natural language processing machine learning, and
computational intelligence), leading to more efficient and scalable information
processing and higher-quality computer-human interaction.”
Lectures 3-4-5-6:
Semantic Web Basics and Applications
Lectures
7-8-9-10: Tutorial on Designing Ontologies with Protégé (see also: Presentation
1 and Presentation
2), for the newer versions of Protégé, check Protégé
- tutorial Documentation
can be downloaded and
viewed from Moodle.
NOTE: if you have possibility to come
to the in-class lectures, choose this option, because lecture content is
constantly updated, and the recorded lectures from previous years could be
slightly outdated …
Exercise/Assignment
Collaborate
with our research group on developing stronger AI!