=Paper=
{{Paper
|id=Vol-2022/paper11
|storemode=property
|title=
Semantic Educational Web Portal
|pdfUrl=https://ceur-ws.org/Vol-2022/paper11.pdf
|volume=Vol-2022
|authors=Victor Telnov
|dblpUrl=https://dblp.org/rec/conf/rcdl/Telnov17
}}
==
Semantic Educational Web Portal
==
https://ceur-ws.org/Vol-2022/paper11.pdf
Semantic Educational Web Portal
© Victor Telnov
National Research Nuclear University MEPhI,
Obninsk, Russia
telnov@bk.ru
Abstract. The paper deals with the pilot project devoted to the application of the knowledge graphs in
the educational activities of the universities. The ontology of the curriculum and the training courses, as well
as the means of authoring, enrichment and adaptation of the learning objects are considered. The visual
navigation on the knowledge graphs is carried out by using the special searching widgets and smart RDF
browser. Working with semantic repository and text analytics is performed on the cloud platforms via
SPARQL queries and RESTful services. The software architecture in UML-notation are presented, examples
of real use of the educational portal are given.
Keywords: semantic web, educational portal, ontology, knowledge graph, triplestore, RDF storage,
graph database, cloud computing.
A recent authoritative overview [14] deals with the
1 Introduction Graph and RDF databases makes it possible to navigate
Students and professors spend a lot of time and among modern products and solutions in the field of the
efforts finding useful information, instead of having to Semantic Web, where the leaders are AllegroGraph,
comprehend and interpret the learning content. It was ArangoDB, BlazeGraph, Cray, DataStax, Ontotext
rightly observed that the traditional web technologies GraphDB, IBM Graph, MarkLogic, OrientDB, Neo4j,
(sometimes referred to as WEB 2.0) do not provide Stardog, Teradata, Aster, Virtuoso.
adequate search and navigation in the environment of It looks very promising the cooperative project
distributed knowledge at the semantic level. Ontotext and Impelsys on the joint using of the platforms
Naturally the thought came about some personal GraphDB and Dynamic Semantic Publishing for the
smart learning agents (software), which could identify development of personalized adaptive learning.
relevant information from any accessible data source and The pilot project [7] which is considered in this
provide an information synthesis tailored to personal article is based on the cloud semantic platform and uses
learning objective. The idea of semantic educational network RESTful services. The preferred repositories for
portals that could provide a meaningful integration of learning objects themselves are the remote data storages.
educational objects with the adaptation and The predecessor of this project is the Cloud cabinet of the
personalisation of training courses and curricula, Educational portal “Department online” [2]. The project
appeared almost simultaneously with the advent of the under consideration has been implemented in the
Semantic Web. educational practice of National Research Nuclear
During the semantization the data are combined into University MEPhI, Russia.
triplets in accordance with the RDF model and form a RDF browser is the main highlight of the Semantic
graph. If the data are the learning objects, than that form Educational Web Portal [7], which distinguishes it from
the so-called knowledge graph. It is obvious that the most most of the known solutions in the field of the Semantic
adequate repository for the knowledge graphs are the Web. Once being in the desired place of the knowledge
graph databases. graph via the corresponding widget, then you can to
The semantic graph database, also referred to as an perform a visual navigation in this graph, simply walking
RDF triplestore, stands out from the other types of graph along its nodes.
databases due to the possibility to support ontologies. There is a possibility to make a visual walk through
The semantic graph database is capable to integrate the knowledge graph as far as you want in any direction,
heterogeneous data from many sources and create scooping up the data that appears. By focusing on a
relationships between datasets. That database focuses on specific graph node, it is possible to obtain text metadata,
the relationships between entities and is able to infer new media content and hypertext links that are associated
knowledge out of existing information. It is a powerful with this node. Along with that the nearest neighborhood
tool to use in relationship analytics and knowledge of the node becomes visible and accessible for
discovery. navigation.
2 Related Work and Novelty 3 The Ontology
The fundamental technologies of the Semantic Web,
Proceedings of the XIX International Conference the knowledge graphs for example, are based on a set of
“Data Analytics and Management in Data Intensive universal standards, as set down by the World Wide Web
Domains” (DAMDID/RCDL’2017), Moscow, Russia, Consortium (W3C) international community [17]. From
October 10–13, 2017
50
the point of view of semantic technology, the key these terms, limiting their meaning within a particular
standards that apply are the Resource Description area. At the formal level, an ontology is a hierarchical
Framework (RDF) and OWL (Web Ontology system consisting of a set of concepts and a set of
Language). assertions about these concepts on the basis of which it
RDF(S) [18], or triplets, is the format uses to store is possible to describe classes, relations, functions, and
data in knowledge graphs. OWL [19] is based on the individuals (instances of classes).
Description Logics language which is designed to show In the language of Description Logics (DL) [4], a set
the data schema and to represent rich and complex of assertions of a general kind, or terminology, is called
knowledge about hierarchies of things and the relations TBox (intensional knowledge). It is TBox that forms an
between things. It is complementary to RDF and allows ontology in the proper sense of the word. In Description
for formalizing a data schema/ontology in a given Logics, sets of assertions of a individual kind – ABox
domain of knowlrdge, separately from the data itself. (extensional knowledge) are singled out separately.
In the general case an ontology is a formal TBox together with ABox forms a meaningful
specification that provides sharable and reusable knowledge base (knowledge graph).
knowledge representation. An ontology includes Below in Figure 2 is an example of the relationship
descriptions of concepts and properties in a concrete between the class and individuals. Here the individual
domain of knowlrdge, relationships between concepts, named Semantic_Web belongs to the class named
constraints on how the relationships can be used and Training_Course. In addition, this individual has a
occasionally individuals as instances of concepts. number of relations with individuals of other classes.
Figure 1 partially shows the ontology [11] that is used This can be a relations of different types and directions,
in the Semantic Educational Web Portal [7]. In Figure 1, as can be seen from the color and direction of the arrows
the Training_Course class is intentionally highlighted, in Figure 2.
because later this class and its individuals will be used as The very kinds of relations, like classes, are usually
explanatory examples. defined in TBox, whereas the facts of the existence of a
certain kind of relationship between concrete individuals
are intrinsically some RDF-assertion in ABox and each
assertion has a triplet appearance.
Below Figure 3 shows a diagram of the relationship
between classes from the ontology. This diagram
presents only the top-level relationships. Every beam of
particular color is a set of relations between individual
instances of two classes.
Each individual relation in the ontology (that is in the
knowledge graph) inherently is an RDF assertion where
the subject is an instance of one class, the object is an
instance of another class, and the reference is a predicate
in the RDF format.
Depending on the number of relations between
instances of two classes, every beam on diagram in
Figure 3 can be thicker or thinner and gets a color of the
class with a large number of incoming relations. These
relations can be in both directions (incoming,
outcoming). The number of relations (links) between
classes from the ontology is shown in the legend on the
diagram in Figure 3.
4 Knowledge Graphs
An ontology enriched with extensional knowledge
from a specific subject area is also called the knowledge
graph or knowledge base. Extensional knowledge forms
Figur e 1 The class hierarchy in the ontology the contents of ABox. Practically, knowledge graphs are
deployed in the graph database or in a different semantic
Often, ontologies are understood as special repository (triplestore or RDF store).
knowledge repositories that can be read and understood Specifically, the Semantic Educational Web Portal
both by people and computers, alienated from the [7] is located on the Ontotext S4 GraphDB cloud
developer and reused. Ontology in the context of platform [11] (physically on the Amazon Web Services
information technology is usually a hierarchical system – AWS cloud platform [1]).
of concepts and terms (structure, model) of a certain
subject area. Informally, an ontology is a description of
the world view as applied to a particular area of interest.
This description consists of terms and rules for the use of
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Figure 2 Individual of the class with neighborhood (example)
Figure 3 Relationships between classes in the ontology
Figure 4 Navigation on the knowledge graphs
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The current prototype of the Semantic Educational supplemented by the international knowledge bases
Web Portal [7] supports the curriculum presented in the DBpedia and Wikidata, as well as a number of more
Cloud cabinet of the Educational portal «Department specialized knowledge repositories.
online» [2]. Remote work with cloud version Ontotext Each of mentioned knowledge graphs contains tons
GraphDB is carried out through the provided REST API. of triplets. The widgets shown below in Figure 4 are
The most common operations are creating, reading, designed to allow a student or teacher easily get into the
loading, and deleting semantic data. For the practical right place of the right knowledge graph, where it is
implementation of network requests HTTP methods are likely find the required learning objects.
used, such as GET, POST, PUT, DELETE. These
The principle of working with these widgets is
network requests contains essentially automatically
largely similar to how information is searched through
generated SPARQL queries of the following types.
popular public search engines (Google, Yandex, etc.). As
• SELECT to fetch data from the knowledge graph. the user types the letters of the keyword in the input line,
• CONSTRUCT to create a new RDF graph. the system rolls out a list of relevant concepts from the
knowledge graph. User can choose the most suitable
• INSERT to add triples to a graph.
concept and dive directly into the desired area of the
• DELETE to remove triples from a graph. graph.
Figure 4 shows the user interface of the Semantic Then, more accurate visual navigation on the
Educational Web Portal, suitable for navigating through knowledge graph becomes possible, which is performed
the available knowledge graphs. Primary graph of in an intuitively clear manner using the RDF browser, as
knowledge contains the materials of the master's courses, described below.
which are taught at the NRNU MEPhI on the profile
“Computer networks and telecommunications”. It is
Figure 5 Fragment of the knowledge graph in the RDF browser (example)
5 RDF browser
the nearest neighborhood of the node becomes visible
RDF browser is the main highlight of the and accessible for navigation. This environment includes
Semantic Educational Web Portal, which nodes not only of that graph, through which you
distinguishes it from most of the known solutions in originally has come in the semantic web, but also the
the field of the Semantic Web. Having got to the right nodes of all other knowledge graphs of that are supported
place of the necessary graph of knowledge through by the system.
the corresponding widget, then you can perform a
In Figure 5, some elements of the node's
visual navigation in this graph, simply walking along
neighborhood that correspond to the Semantic_Web
its nodes.
individual are displayed, as well as some related
By focusing on a specific graph node, it is possible to metadata. If you focus on the next node that is displayed
obtain text metadata, media content and hypertext links by the RDF browser, it also becomes available with its
that are associated with this node. It is very important that neighborhood and metadata.
53
Thus you can to make a visual walk through the graph problem can be reduced to finding the shortest path in the
of knowledge as long as you like in any direction, knowledge graph. This question has been studied, for
scooping up the data that appears. In Figure 5, this is not example, in [9]. Neo4j Graph Database [10] has built-in
shown, but in reality, when you hover over different means for calculating the shortest paths in the graph.
sections of a particular node, pop-up menus, additional In real educational practice, the process of
information and prompts for various options for constructing a specific training course in the process of
continuing navigation through the knowledge graph formation of a curriculum largely is empirical procedure,
becomes availible. based on the experience and knowledge of the lecturer.
6 Adaptive Learning Technology In order to assess the training outcomes and learning
efficiency, the evaluation tools from the Cloud cabinet of
The main challenge of e-learning systems is to provide the Educational portal “Department online” are used, see
training courses tailored to different students with [2].
different learning rate and knowledge degree. Adaptive
learning technologies are based on the fact that each
student is unique, learns at varying rates and comes with
different levels of knowledge. Traditional methodology
of instruction may force the student down a learning path
that is either too elementary, resulting in lack of interest
or too heavy to grasp the nuances of the course. Adaptive
learning, aided by semantic technologies [8], considers
learner’s interaction with courses and assessment
modules to create personalized learning paths.
The adaptive learning system generally includes the
following three subsystems.
1. The subsystem of forming a model of the learner
(student model).
2. Learning planning subsystem (instructional model).
3. A subsystem for evaluating training outcomes.
For the student model the most popular means of
determining a student's skill level is the method
employed in CAT (computerized adaptive testing). In
Semantic Educational Web Portal «Department online»
[5] various, not just computerized means for measuring
a student's skill level are used. In fact, the same training
course should be built in different ways, depending not
only on the level of knowledge and abilities of students,
but also on the learning objectives. For example, a
training course in programming will look different for
students who concentrate in the field of business
informatics and in the field of computer networks.
To build the actual instructional model and to fill it
with learning objects, the Ontotext S4 Text Analytics
RESTful service [13] is actively used. The purpose of
text analysis is to create sets of structured data (machine-
readable facts) out of heaps of unstructured,
heterogeneous documents.Text analytics involves a set
of techniques and approaches towards bringing various
textual content to a point where it is represented as data
and then mined for insights/trends/patterns. Contextual
authoring provides lecturers with related texts, images
and concepts which enhance the training course, reduces
the time and costs of authoring and editing new learning
content. Automated content enrichment improves the Figur e 6 Software architecture
quality of curriculum and allows for continuous
authoring without interruption. 7 Software Architecture
When constructing adapted training courses, they are Figure 6 shows the Deployment Diagram for the
usually optimized according to two criteria: the Semantic Educational Web Portal, performed in
effectiveness and adaptability of training. From the accordance with the UML 2 standard [6]. This diagram
mathematical point of view, in the idealized case the
54
can also be considered as an enlarged Component existence of the Semantic Web and Linked Open Data.
Diagram for this software. As it can be seen from Figure They continue to use traditional Content Management
6, the component named “RDF Browser” does not have Systems (CMS), also known as Learning Management
its own server code (back-end). It interacts with two Systems (LMS) or Virtual Learning Environments
cloud RESTful services – Ontotext Text Analytics [13] (VLE), which are built primarily on simple taxonomies
and Ontotext GraphDB [11], both physically are and thesauruses.
deployed on the Amazon Web Services (AWS) [1] cloud Students and professors widely practice searching the
platform.Cloud service Text Analytics [13] provides information on the World Wide Web for keywords, using
tools for semantic annotation and update of educational public search engines for this purpose. Tradition plays a
objects during the creation and adaptation of curricula. significant role here, as well as the simplicity and high
Cloud service GraphDB [11] provides the semantic speed of the search query generation, in comparison with
storage for knowledge graphs and is mainly used as a the search queries to the Semantic Web.
SPARQL endpoint. As a universal repository for
Despite the growing commercialization of the public
educational objects of an arbitrary nature, Google Drive
search engines, it can be assumed with a great deal of
is used. The choice of this particular storage is not
certainty, that they, along with Wikipedia, will remain
principled, in parallel with it, arbitrary remote
the most accessible “universal textbooks” for the
repositories equipped with data display means, for
foreseeable future for that numerous category of students
example such as Microsoft OneDrive or Yandex.Disk
who not always demand the quality and completeness of
can be successfully applyed.
the training material. An exception to this situation could
The other two components, named “Wikidata Search be students (undergraduates) of universities who
Agent” and “DBpedia Search Agent” both are advanced specialize in computer science and informatics.
SPARQL endpoints to the corresponding international
knowledge bases. Both mentioned components are 10 Acknowledgements
provided with libraries of patterns of search queries,
The work was supported by the NBO “Vladimir Potanin
which largely facilitate the work of users, as well as are
Charity Fund”, project No ГК160001360.
capable to deliver and show the found content in a variety
of formats, including graphics. References
8 Discussion [1] Amazon Web Services (AWS) – Cloud Computing
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