EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook

Management Service for Greek Universities

Nick Bassiliades

School of Informatics, Aristotle University of Thessaloniki, Greece

Keywords: Linked Open Data, Knowledge Graphs, Ontologies, DBpedia, SWI-Prolog.

Abstract: Evdoxus is a web information system for the management of the total ecosystem for the free provision of

textbooks to the undergraduate students at the Greek Universities. Among its users are book publishers that

university departments that register the relevant textbooks for each module of the curricula (course), and

finally, students that select one book per module that they attend. All the above information (except for which

students selected which books) is freely available at the Evdoxus site in the form of HTML web pages. In this

paper, we present how we extracted this information and converted it into an open Knowledge Graph in RDF

that can be used to generate several interesting reports and answer statistical analysis questions in SPARQL.

The KG is backed by a simple ontology which is aligned with some well-known ontologies. The extraction /

conversion application has been developed using SWI-Prolog’s XPath and Semantic Web libraries. The KG

encompasses the Linked Open Data initiative by linking University instances with their corresponding DBpe-

dia entries, employing the Wikipedia search engine and the DBpedia SPARQL endpoint.

1 INTRODUCTION

Evdoxus

(or Eudoxus) is an online service (web in-

formation system) for the management of the total

ecosystem of the free provision of university text-

books to undergraduate students at the Greek Univer-

sities. It was launched in the academic year 2010-

2011 and it offers: a) accurate online information

about the textbooks that are available for each course

/ module; b) quick delivery of the books to the stu-

dents; c) effective mechanisms for publishers’ com-

pensation; d) parallel distribution of free e-books and

notes; e) public resources’ abuse prevention; and f)

more transparency and less bureaucracy. Its users in-

clude book publishers that register their textbooks,

faculty members that search for appropriate textbooks

for their courses, administration staff of departments

that register the textbooks selected by the professors

for each module of the curricula (course), and finally,

students who, after registering at their University’s

Student Information System for the modules they will

attend for each semester, they select one book per

module that they attend. All the above information

https://orcid.org/0000-0001-6035-1038

https://eudoxus.gr/

(except for which students selected which books) is

freely available at the Evdoxus site in the form of

HTML web pages.

Knowledge Graphs (KGs) are a powerful way of

representing and integrating structured knowledge

from either a single or various sources (Hogan, et al.,

2022). They consist of nodes that represent entities,

and edges that represent the relationships between

them. KGs can be used to support various tasks, such

as data analytics, information retrieval, question an-

swering, recommendation, natural language under-

standing and computer vision. One of the key aspects

of KGs is their use of ontologies to provide a formal

representation of the entities and their relationships.

Ontologies enable logical inference and reasoning

over the KG, as well as consistency checking and val-

idation. Ontologies also facilitate the interoperability

and integration of different knowledge sources, by

providing a common vocabulary and schema.

Linked open data (LOD), which is an older, alter-

native term for KGs, is a vision of making data on the

web accessible, interoperable, and reusable. LOD is

based on the principles of linked data, which use

Bassiliades, N.

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities.

DOI: 10.5220/0012153600003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 2: KEOD, pages 17-28

ISBN: 978-989-758-671-2; ISSN: 2184-3228

URIs to name and identify things, HTTP to allow

these things to be looked up and dereferenced, and

RDF to provide structured data using controlled vo-

cabularies (Bizer, Heath, & Berners-Lee, 2009). LOD

enables data from different sources and domains to be

connected and queried in a semantic way. This can

create new insights and value from the data, as well

as foster innovation and collaboration. Tim Berners-

Lee has proposed a 5-star scheme for grading the

quality of open data on the web, where the highest

ranking is given to LOD (Berners-Lee, 2009). Ac-

cording to this scheme, 5-star LOD meets the follow-

ing criteria: a) Data is openly available in any format

(1 star); b) Data is available as machine-readable

structured data (2 stars); c) Data uses a non-proprie-

tary format (3 stars); d) Data uses URIs to identify

things (4 stars); e) Data is linked to other data to pro-

vide context (5 stars). Some examples of large LOD

datasets are DBpedia

, Wikidata

, and GeoNames

In this paper, we present how we have managed

to extract all the information related to Greek univer-

sities, departments, study programs, courses and text-

books used in the courses, from the Evdoxus web site

and then convert it into an open KG in RDF that can

be used to generate several interesting reports and sta-

tistical analyses, through SPARQL queries. The KG

is backed by a simple ontology which is aligned with

some well-known ontologies. The extraction / con-

version application has been developed using SWI-

Prolog’s XPath and Semantic Web libraries. The Ev-

doxus KG encompasses the LOD initiative (5 stars)

by linking University instances with their correspond-

ing DBpedia entries, employing the Wikipedia search

engine and the DBpedia SPARQL endpoint.

In the rest of the paper, we briefly review related

work in Section 2 and we present the KG generation

methodology in Section 3. Section 4 presents various

competency questions we have used to build the on-

tology and evaluate the KG, as well as some other in-

teresting SPARQL queries. Finally, Section 5 con-

cludes the paper with some future research directions.

2 RELATED WORK

There are several ontologies about Academia, includ-

ing education, research, and publications (Stancin,

Poscic, & Jaksic, 2020). The most influential ones can

https://www.dbpedia.org/

https://www.wikidata.org/

http://www.geonames.org/

https://lov.linkeddata.es/dataset/lov

https://schema.edu.ee/

http://ns.inria.fr/semed/eduprogression/

be found at the Linked Open Vocabularies (LOV) re-

pository

, a popular catalogue of reusable ontologies

(Vandenbussche, Atemezing, Poveda-Villalón, &

Vatant, 2017) that currently contains 782 ontologies

in total. At LOV, we found 16 ontologies tagged with

the keyword “Academy”. However, only 6 of them

deal with purely educational concepts, whereas the

rest deal with research and publication activities. Out

of the 6, we excluded those that do not have classes /

properties in the English language (e.g., Education

Ontology

), or they deal with country-specific educa-

tional systems (e.g., EduProgression Ontology

). In

the following paragraphs, we briefly review the re-

maining ones, commenting also on how much they

cover the needs of the Evdoxus KG.

The VIVO core ontology

focuses on the general

domain of academia and researchers and their re-

search-related activities and relationships. The ontol-

ogy is independent of knowledge or creative domain

(Corson-Rikert, et al., 2012). The VIVO ontology is

mainly focused on the research and publications ac-

tivities of a university and not education, so it partly

covers the structure of the University and Courses /

Modules, without however gluing Courses / Modules

into Study Programs and also not hierarchically con-

necting the Academic Units to each other. For Books,

it adheres to the BIBO ontology

The Academic Institution Internal Structure On-

tology (AIISO)

provides classes and properties to

describe the internal organizational structure of an ac-

ademic institution. AIISO captures the part of the Ev-

doxus ontology that deals with the structure of Uni-

versities, Departments, Study Programs and Courses.

However, it does not cover Books and their relation-

ship to Courses / Modules and some properties for

Courses / Modules are also missing.

The Teaching Core Vocabulary Specification

(TEACH)

is a lightweight vocabulary providing

terms to enable teachers to relate things in their

courses together. TEACH is based on practical re-

quirements set by providing seminar and course de-

scriptions as Linked Data. The TEACH ontology co-

vers mostly the lower classes of the Evdoxus ontol-

ogy, namely Courses/Modules and teaching material

(e.g., Books). However, it does not cover the Univer-

sity organization in Universities / Departments.

Finally, the ReSIST Courseware Ontology

rep-

resents the various educational courses and resources

http://vivoweb.org/ontology/core

https://www.dublincore.org/specifications/bibo/bibo/

https://vocab.org/aiiso/schema-20080925.html

https://lov.linkeddata.es/dataset/lov/vocabs/teach

https://lov.linkeddata.es/dataset/lov/vocabs/crsw

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

within the ReSIST project. Its focus is mainly on the

internal structure of a course / module and the activi-

ties / courseware involved, so it has too little overlap

with the Evdoxus KG; thus, it was excluded from be-

ing aligned with the Evdoxus ontology.

One quite popular educational ontology, not in-

cluded at LOV, is the Bowlogna ontology

that mod-

els an academic setting as proposed by the Bologna re-

form (Demartini, Enchev, Gapany, & Cudré-Mauroux,

2013), that gave rise to new administrative procedures

at European universities and new concepts for the de-

scription of curricula. This ontology partly covers the

concepts of the Evdoxus ontology but scarcely its prop-

erties. Nevertheless, we included it in the alignment

process due to its impact in the development of educa-

tional ontologies.

Besides domain-dependent academic ontologies,

there are several general-purpose ones, such as Sche-

ma.org, DBpedia and Wikidata, that include several

classes / properties that may cover the Evdoxus ontol-

ogy. Based on their popularity, we selected the above

three to discuss here and include in the alignment pro-

cess.

Schema.org is a collaborative, community activ-

ity that maintains schemas for structured data on the

web. The Schema.org vocabulary

currently consists

of 797 types and 1457 properties, covering entities,

relationships between entities and actions, and can

easily be extended. It is used by over 10 million sites

to markup web pages and email messages. Many ap-

plications from Google, Microsoft, and others already

use these vocabularies. The Schema.org vocabulary is

so rich that actually covers the Evdoxus ontology

even more than the aforementioned domain-specific

ontologies. However, its alignment coverage is loose,

meaning that it does not provide many equivalences,

but rather subclasses and sub-properties.

The DBpedia Ontology

is a shallow, cross-do-

main ontology, which has been manually created based

on the most commonly used infoboxes within Wikipe-

dia. The ontology currently covers 685 classes which

form a subsumption hierarchy and are described by

2,795 different properties, being also one of the biggest

general-purpose ontologies. However, it only mini-

mally covers the Evdoxus ontology; we have included

it in the alignment mainly because the DBpedia dataset

is the central hub of the Linked Open Data cloud and

we have linked the University instances of the Evdoxus

KG with their DBpedia counterparts.

https://gist.github.com/lsarni

https://schema.org/docs/schemas.html

http://mappings.dbpedia.org/server/ontology/classes/

Wikidata (Vrandečić & Krötzsch, 2014) is the cen-

tral data management platform of Wikipedia, capturing

structured data on several subject domains, managing,

among others, the information underlying Wikipedia

and other Wikimedia projects. By the efforts of thou-

sands of volunteers, the project has produced a large,

open knowledge base with many interesting applica-

tions. The data is highly interlinked and connected to

many other datasets. The Wikidata repository consists

mostly of items and statements about these items.

Items are used “to represent all the things in human

knowledge, including topics, concepts, and objects”,

and are given a unique identifier, a label, and a descrip-

tion. Statements are used “for recording data about an

item”, and “consist of (at least) one property-value

pair”; they serve to “connect items to each other, re-

sulting in a linked data structure”. To organize Wiki-

data’s content, some items (classes) are used to classify

other items through the “instance of” property. Further,

classes are related through the “subclass of” taxonomic

property, defining thus hierarchies of classes, from

more general to more specific ones.

The Wikidata ontology (called WikiProject On-

tology

) consists of a few upper-level classes and

properties that aim to (a) support a broad semantic in-

teroperability between notable ontologies like

DOLCE, BFO, SUMO, Lemon, RDA, etc.; and (b)

build consensus around the main branches of Wiki-

data core concept tree and how they relate to each

other.

Most Academic KGs relate to scientific research

and publications, such as the Microsoft Academic KG

(Färber, 2019) or Open Research KG (Jaradeh, et al.,

2019). There are few KGs related to the edu-cational

aspect of academia, dealing with teaching and class-

room resources, education management and educa-

tional technologies (Abu-Salih, 2021). However, none

of the ones reported in the above survey relate to the

content and the size of the Ev-doxus KG.

To the best of our knowledge there are no services

like Evdoxus nor any datasets similar to the Evdoxus

KG. Searching at the LOD Cloud repository

, we

have found 12 datasets related to the university do-

main, 4 of them being University reading lists

which is quite close to the Evdoxus KG. The largest

of them is supposed to consist of 4 million triples.

However, none of these LOD datasets is accessible

anymore.

https://www.wikidata.org/wiki/Wikidata:WikiProject_

Ontology

https://lod-cloud.net

https://lod-cloud.net/datasets?search=reading

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities

Figure 1: Knowledge Graph generation workflow.

Figure 2: Structure of the Evdoxus site.

Furthermore, the data portal of the Greek Govern-

ment contains one dataset (CSV and JSON) about sta-

tistics of requests (from students) and deliveries for

books made through the Evdoxus system

, without

any details about Departments, Courses, Modules,

and specific books, whereas the old data portal

does

contain some partial older datasets, in tabular format.

3 METHODOLOGY

In Figure 1 we can see the workflow of our method-

ology for creating the Evdoxus KG. Central role plays

https://data.gov.gr/datasets/grnet_eudoxus/

https://repository.data.gov.gr/dataset?tags=ΕΥΔΟΞΟΣ

the EvdoGraph application which has been devel-

oped

in SWI-Prolog (Wielemaker, Schrijvers,

Triska, & Lager, 2012). The web pages that constitute

the Evdoxus web site repository (Figure 2) are first

cached in the local disk, to speed up the graph gener-

ation process later. This is done by extracting from

the entry page

, which contains all the Greek Univer-

sities (46), all their departments (732) and all their

study programs throughout the years Evdoxus oper-

ates (13 years, from 2010 to 2023), all the linked

pages, namely the pages that contain all the courses

of the curricula (per year), plus the textbooks sug-

gested per course, a total of 9516 pages.

https://github.com/nbassili/EvdoGraph

https://service.eudoxus.gr/public/departments

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

Then, all these HTML pages are used for extract-

ing information about Universities, Departments,

Study Programs per year (called “courses” in Ev-

doxus), Courses per Study Program (called “modu-

les”), and finally, Books (textbooks) suggested per

module. Notice that only the printed books are ex-

tracted and not any additionally suggested eBooks.

The extraction is based on the http, sgml and

xpath libraries of SWI-Prolog. Notice that since these

pages are automatically generated from the Evdoxus

internal DB, they follow a consistent template and

thus their parsing and information extraction is 100%

successful. This information is used for generating

the KG, in RDF, inside SWI-Prolog’s knowledge

base, using the semweb library of SWI-Prolog.

The KG is structured according to the Evdoxus

ontology we have developed

, in RDF Schema. The

ontology is minimal, with 7 classes and 10 properties

(Table 1), inspired by the hierarchical information

structure of the Evdoxus site (Figure 2). Figure 3

shows the class hierarchy of the Evdoxus ontology,

while Figure 4 shows the relationships between the

concrete ontology classes. Notice that classes

AcademicEntity and LearningEntity are abstract ones,

i.e., they do not have direct instances but rather serve

as property placeholders for inheritance purposes.

The University class instances are linked to their

corresponding DBpedia entries (where possible), via

a methodology that combines searching in Wikipedia,

using the search API, and DBpedia, using its

SPARQL endpoint, discussed in Section 3.1.

The Evdoxus ontology has been aligned with var-

ious external ontologies for interoperability purposes

(VIVO, AIISO, teach, Bowlogna, Schema.org, DB-

pedia, and Wikidata). The alignments are shown in

Table 1, both for classes (using rdfs:subClassOf or

owl:equivalentClass) and properties (using

rdfs:subPropertyOf, owl:equivalentProperty or

owl:inverseOf).

Table 2 shows alignment statistics for the various

ontologies. By “strict” alignments, we denote align-

ments through owl:equivalentClass and owl:equiva-

lentProperty, whereas by “loose” alignments, we

mean alignments through rdfs:subClassOf, rdfs:sub-

PropertyOf and owl:inverseOf. It is evident that

VIVO provides the most strict alignments, whereas

Wikidata, Schema.org and AIISO provide the most

total aligments.

In Listing, we quote an example of the Evdoxus

KG that includes a University (Aristotle University of

Thessaloniki), a Department (Informatics), a Course

(BSc of Informatics, 2022-2023), a Module (Know-

https://w3id.org/evdoxus

ledge Systems) and one of the suggested Books (Vla-

havas, Kefalas, Bassiliades, Kokkoras, & Sakellariou,

2020).

Figure 3: Class hierarchy of the Evdoxus ontology.

Figure 4: Relationships between concrete classes.

The whole Evdoxus KG is, finally, stored at a tri-

plestore (GraphDB) and can be found and queried

here

. Table 3 shows statistics about the Evdoxus KG.

Notice that there might be more books registered at

Evdoxus; however, in the Evdoxus KG we only in-

clude those that are being used in some module.

Books are uniquely identified via their Evdoxus code.

Also, modules are uniquely identified within Depart-

ments via their code. However, there is no single

identification scheme across departments.

The entire extraction (from the local cache) and

generation of the KG takes ~230 sec on a i7-11700 @

2.50GHz PC, with 16GB memory, using SWI-Prolog

(64 bits, v. 9.0.3), whereas storing the KG at the SSD

takes ~28 sec (~3.9 million triples). Loading the KG

at GraphDB, using RDFS Plus semantics (total ~17

million triples), takes ~9 min.

http://lod.csd.auth.gr:7200/sparql; repository Evdoxus.

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities

Table 1: Alignments of the Evdoxus Ontology with external ontologies.

Evdoxus Ontology Alignments

Classes

evdx:AcademicEntity -

evdx:LearningEntity -

evdx:University owl:equivalentClass vivo:University, dbo:University, wikidata:Q3918;

rdfs:subClassOf aiiso:Institution, schema:CollegeOrUniversity

evdx:Department owl:equivalentClass vivo:AcademicDepartment, aiiso:Department,

bow:Department, wikidata:Q2467461;

rdfs:subClassOf schema:School

evdx:Course owl:equivalentClass aiiso:Programme, teach:StudyProgram, bow:Study_Program;

rdfs:subClassOf schema:EducationalOccupationalProgram, wikidata:Q207137

evdx:Module owl:equivalentClass vivo:Course, aiiso:Course, teach:Course, schema:Course,

ow:Module, wikidata:Q600134

evdx:Book owl:equivalentClass bibo:Book, schema:Book, dbo:Book, wikidata:Q571;

rdfs:subClassOf teach:Material

Properties evdx:hasDepartment rdfs:subPropertyOf aiiso:organization, schema:department, wikidata:P527

evdx:hasCourse rdfs:subPropertyOf aiiso:teaches;

owl:inverseOf schema:provider, wikidata:P137

evdx:hasModule rdfs:subPropertyOf aiiso:knowledgeGrouping, wikidata:P527;

owl:inverseOf teach:studyProgram;

owl:equivalentProperty schema:hasCourse

evdx:hasBook rdfs:subPropertyOf teach:reading;

owl:inverseOf wikidata:P366

evdx:hasCode rdfs:subPropertyOf aiiso:code, wikidata:P3295

evdx:hasURL owl:equivalentProperty vCard:hasURL, schema:url;

rdfs:subPropertyOf wikidata:P2699

evdx:name owl:equivalentProperty vcard:hasOrganizationName;

rdfs:subPropertyOf foaf:name, bow:hasName, wikidata:P2561

evdx:semester rdfs:subPropertyOf teach:academicTerm

evdx:title owl:equivalentProperty vCard:title;

rdfs:subPropertyOf teach:hasTitle, bow:hasName, wikidata:P1476

evdx:year rdfs:subPropertyOf bow:beginsToApplyOnDate, schema:startDate, dbo:startYear,

wikidata:P571

Listing 1: Excerpt from the Evdoxus KG.

evdx:university_8

a evdx:University ;

evdx:hasDepartment ... , evdx:dept_1596 , ... ;

evdx:name "ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣ/ΝΙΚΗΣ" ;

owl:sameAs dbr:Aristotle_University_of_Thessaloniki ,

dbpedia-el:Αριστοτέλειο_Πανεπιστήμιο_Θεσσαλονίκης .

evdx:dept_1596

a evdx:Department ;

evdx:hasCode "1596" ;

evdx:hasCourse ... ,

evdx:course_1596_2021 , evdx:course_1596_2022 ;

evdx:name "ΠΛΗΡΟΦΟΡΙΚΗΣ" .

evdx:course_1596_2022

a evdx:Course ;

evdx:hasModule ... ,

evdx:module_1596_2022_61 ,

... ;

evdx:hasURL "https://service.eudoxus.gr/public/departments/courses/1596/2022"^^xsd:anyURI ;

evdx:title "Πρόγραμμα Σπουδών (2022 - 2023)" ;

evdx:year 2022 .

evdx:module_1596_2022_61

a evdx:Module ;

evdx:hasBook evdx:book_50656852 ,

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

evdx:book_94700120 ;

evdx:hasCode "NIS-07-02" ;

evdx:semester 7 ;

evdx:title "ΣΥΣΤΗΜΑΤΑ ΓΝΩΣΗΣ" .

evdx:book_94700120

a evdx:Book ;

evdx:hasCode "94700120" ;

evdx:hasURL "https://service.eudoxus.gr/search/#a/id:94700120/0"^^xsd:anyURI ;

evdx:title "ΤΕΧΝΗΤΗ ΝΟΗΜΟΣΥΝΗ - 4η ΕΚΔΟΣΗ, ΒΛΑΧΑΒΑΣ Ι./ΚΕΦΑΛΑΣ Π. / ΒΑΣΙΛΕΙΑΔΗΣ Ν. /

ΚΟΚΚΟΡΑΣ Φ./ ΣΑΚΕΛΛΑΡΙΟΥ Η." .

Algorithm 1: Function link-university.

10.

11.

Function link-university(UniversityName)

Result := xpath(Wikipedia-search(UniversityName), //ul[@class='mw-search-results']/li[1])

AltNames := { xpath(Result, //a/@title),

xpath(Result, //span[@class='searchalttitle']/a[@class='mw-redirect}/@title) }

ELWikiURL := xpath(Result, //a/@href)

ELDBpediaURI := deref(ELWikiURL)

ENDBpediaURI := find-en-dbpedia(ELWikiURL, ELDBpediaURI)

AltNames := AltNames ∪ find-alt-names(ENDBpediaURI)

if max

 ∈ 

𝑠𝑡𝑟_𝑑𝑖𝑠𝑡



𝑥,UniversityName



 > 0.75

then Return({ELDBpediaURI, ENDBpediaURI })

else Return(∅)

Algorithm 2: Function find-en-dbpedia.

Function find-en-dbpedia(ELWikiURL, ELDBpediaURI)

R := Execute-sparql(“https://dbpedia.org/sparql”,

'select ?u where { ?u rdf:type dbo:University ; owl:sameAs <ELDBPediaURL> . }')

if R=∅

then R := deref(xpath(ELWikiURL, //a[@class='interlanguage-link-target' and

@hreflang='en']//@href))

Return(R)

Algorithm 3: Function find-alt-names.

Function find-alt-names(ENDBpediaURI)

If ENDBpediaURI≠∅

then Result := Execute-sparql((“https://dbpedia.org/sparql”,

'select distinct str(?n) where { <ENDBPediaURI> (rdfs:label |foaf:name | dbp:nativeName) ?n . }')

else Result := ∅

Return(Result)

Table 2: Alignment statistics.

Ontology

Ali

nments

Strict Loose Total

VIVO 7 0 7

AIISO 3 6 9

TEACH 2 5 7

Bowlogna 3 3 6

Schema.or

4 6 10

edia 2 1 3

Wikidata 4 10 14

Table 3: Evdoxus KG statistics.

Class Instance count

Universit

(

linked to DB

edia

)

(

)

artment 732

Course 9516

Module 535143

Boo

40529

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities

Table 4: Description of auxiliary functions.

Function Description

Wikipedia-search(InputString) Returns the HTML page contents of the Wikipedia search results

xpath(HTMLPage,Xpath-expr) Returns the content of the HTML page according to the Xpath-expr

deref(WikiURL) Replaces “http://el.wikipedia.org/wiki/” with “http://el.dbpedia.org/resource/”, or

“https://en.wikipedia.org/wiki/” with “http://dbpedia.org/resource/”

3.1 DBpedia Linking

All the University class instances of the KG are linked

to their DBpedia counterparts, if possible. The pseu-

docode for our linking methodology is shown at Al-

gorithm 1, Algorithm 2 and Algorithm 3. Table 4 de-

scribes the input/output of some trivial auxiliary pred-

icates used in the previous algorithms. Our methodol-

ogy is based on previous experience in linking Uni-

versity instances with DBpedia (Bassiliades, 2014).

Initially, the full University name is used to search

at Wikipedia the corresponding lemma, using its

search API (Line 2 at

Algorithm 1, see example

for

Aristotle University of Thessaloniki). Then, the first

returned result is extracted from the HTML result

page (using Xpath, Lines 2-5). The URL of the result

Wikipedia lemma page is de-referenced to its corre-

sponding DBpedia URI (Line 6). Since University

names are in Greek, the resulting lemmas are from the

Greek Wikipedia, and the corresponding DBpedia in-

stances are from the Greek DBpedia

. Using the

SPARQL endpoint of the English DBpedia, the cor-

responding URI at the English DBpedia is retrieved

(using owl:sameAs, Line 7 at Algorithm 1 and Lines

2-3 at Algorithm 2). Alternatively, the URI at the

English DBpedia can be discovered by de-referencing

the URI of the English Wikipedia, which is usually

“hidden” in title of the Greek Wikipedia lemma (p-

lang-btn, Lines 4-5 at Algorithm 2). Both URIs are

linked to the Evdoxus KG University entry, as shown

in the example at Listing .

All the above are the straightforward steps, which

usually discover the correct Wikipedia lemmas and

DBpedia entries. In order to check if the retrieved in-

formation is the correct one, the title of the retrieved

Wikipedia entry is compared with the University

name extracted from the Evdoxus site, using the

string-matching metric of (Stoilos, Stamou, &

Kollias, 2005), with a threshold of 0.75 (Line 9 at Al-

gorithm 1), discovered via experimentation. If the

matched lemma / entity has a smaller string similarity,

then alternative University names for comparison are

sought. One such source is alternative or redirected

https://el.wikipedia.org/w/index.php?fulltext=1&search=ΑΡΙΣ-

ΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣ/ΝΙΚΗΣ&ns0=1

http://el.dbpedia.org/

Wikipedia lemmas; a few years ago, Greek Universi-

ties have undergone a partial reformation (Polytech-

nics either reformed to or merged into universities or

they have joined with existing Universities). In this

case, alternative or redirected Wikipedia lemma titles

contain the older University title (Line 4 at Algorithm

1), which is still mentioned at the Evdoxus site. An-

other source of alternative names is DBpedia itself;

the dereferenced DBpedia URI is queried for alterna-

tive University titles (Line 8 at Algorithm 1 and Al-

gorithm 3). If none of the above alternative names has

a similarity score greater than the threshold, then the

University entity is not linked to DBpedia (Lines 9-

11 at Algorithm 1). This occurs for only 3 out of the

46 University instances (Table 3). In general, our

methodology achieves 100% precision, since all the

linked DBpedia entries are correct, and 100% recall,

since the non-linked University entries do not have a

DBpedia/Wikipedia entry. The confirmation was per-

formed via manual inspection.

4 COMPETENCY QUESTIONS

One of the main reasons for building the Evdoxus On-

tology and KG was to be able to generate various re-

ports and statistics concerning the Universities, De-

partments and Modules (Courses) that a certain text-

book is used, either in one academic year or through-

out the years, alone or in comparison to other compet-

itive textbooks, etc. Initially, these reports / statistics

were generated (for personal use) through a Prolog

application called EvdoStats

. The predicates that

generate these reports / statistics play the role of com-

petency questions that the Evdoxus Ontology and KG

should be able to answer.

Table 5 shows some the competency questions

handled by the Evdoxus KG.

https://github.com/nbassili/EvdoStats

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

Table 5: Competency questions.

CQ1 Return all modules that use the book, for a specific year, along with the department and the university.

CQ2 Return how many modules and all module names (in a string), that the book is used, for a specific year, along

with the department and the university, grouped by the department.

CQ3 Return how many modules, departments and universities use the book, for a specific year.

CQ4 Return how many modules, departments and universities use the book, per year, for a range of years.

CQ5 Which departments (including details about university/modules) additionally use the book in a subsequent aca-

demic year, compared to a previous one.

CQ6 Which universities (including details about departments/modules) additionally use the book in a subsequent ac-

ademic year, compared to a previous one.

CQ7 Which modules (including details about departments/universities) additionally use the book in a subsequent ac-

ademic year, compared to a previous one.

CQ8 Return comparison details and statistics for multiple books, for a specific academic year.

CQ9 Which modules (including details about departments/universities) use only the first book and not the second, for

a specific academic year.

CQ10 Which departments (including details about university/modules) use only the first book and not the second, for

a specific academic year.

CQ11 Which universities (including details about departments/modules) use only the first book and not the second, for

a specific academic year.

Table 6: Results for competency question CQ4.

?year ?Univs ?Depts ?Mods

2019 20 42 59

2020 21 49 73

2021 20 54 83

2022 21 58 83

Table 7: Results for competency question CQ8.

?Book ?Univs ?Depts ?Mods

"94700120" 21 58 83

"102070469, 13909" 19 56 82

Listing 2 and Listing 3 show the SPARQL queries

implementing the competency questions CQ4 and

CQ8, respectively, whereas Table 6 and Table 7 show

the corresponding results. Notice that there are situa-

tions where multiple editions of the same textbook are

available through Evdoxus. To this end, in order to

correctly calculate book statistics, the VALUES con-

struct is used to aggregate different editions of the

same book into a single result. E.g. at Listing 2, the

book codes "94700120" and "12867416" denote the

(most recent) and the 3

editions, respectively, of

the book at reference (Vlahavas, Kefalas, Bassiliades,

Kokkoras, & Sakellariou, 2020), whereas at Listing

3, the book codes "102070469" and "13909" corre-

spond to the Greek translation of the 4

and 2

edi-

tions, respectively, of the book at reference (Russell

& Norvig, 2020). The SPARQL queries for all the

competency questions can be found at the GitHub re-

pository for EvdoGraph

https://github.com/nbassili/EvdoGraph

In addition to the competency questions, which

are inspired by the EvdoStats application and revolve

around reports and statistics about a single or a couple

of books, the Evdoxus KG can provide answers to

more general queries. For example, in Listing 4 two

such queries are shown; the left one (AQ1) returns the

names of the departments (and the name of their uni-

versity) that are not “active” at Evdoxus during a spe-

cific academic year, i.e., they have a course that does

not have any modules. The latter is true mainly for

departments that have been discontinued in previous

years, but the Evdoxus site still includes them.

The query on the right (AQ2) reports the top-6

books, concerning the number of departments and

modules that they are used in, sorted in descending

order, first on departments and then on modules. Re-

sults are shown in Table 8. Such reports are not easy

to be generated by the EvdoStats application because

the latter is based on searching for specific book

codes in the course pages and not on collecting infor-

mation about all books.

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities

Listing 2: SPARQL query for competency question CQ4.

PREFIX evdx: <https://w3id.org/evdoxus#>

select ?year (count(DISTINCT ?u) as ?Univs) (count(DISTINCT ?d) as ?Depts) (count(DISTINCT ?m) as ?Mods)

where {

?s a evdx:Book .

VALUES ?code { "94700120" "12867416" }

?s evdx:hasCode ?code .

?m a evdx:Module ; evdx:hasBook ?s .

?c a evdx:Course ; evdx:year ?year.

FILTER ((?year>=2019) && (?year<2023)) .

?c evdx:hasModule ?m .

?d a evdx:Department ; evdx:hasCourse ?c .

?u a evdx:University ; evdx:hasDepartment ?d .

} group by ?year

order by ?year

Listing 3: SPARQL query for competency question CQ8.

PREFIX evdx: <https://w3id.org/evdoxus#>

select (group_concat(DISTINCT ?code;separator=", ") as ?Book) (count(DISTINCT ?u) as ?Univs) (count(DISTINCT

?d) as ?Depts) (count(DISTINCT ?m) as ?Mods) where {

?s a evdx:Book .

VALUES (?bcount ?code) { (1 "94700120") (1 "12867416") (2 "102070469") (2 "13909")}

?s evdx:hasCode ?code .

?m a evdx:Module ; evdx:hasBook ?s .

?c a evdx:Course ; evdx:year 2022 ; evdx:hasModule ?m .

?d a evdx:Department ; evdx:hasCourse ?c .

?u a evdx:University ; evdx:hasDepartment ?d .

} group by ?bcount

Listing 4: Additional SPARQL queries.

AQ1 AQ2

PREFIX evdx: <https://w3id.org/evdoxus#>

select ?dn ?un where {

?c a evdx:Course ; evdx:year 2022 .

?d evdx:hasCourse ?c ; evdx:name ?dn .

?u evdx:hasDepartment ?d ; evdx:name ?un

FILTER NOT EXISTS {

?c evdx:hasModule ?m .

}

PREFIX evdx: <https://w3id.org/evdoxus#>

select ?book (count(DISTINCT ?d) as ?Depts) (count(DISTINCT

?m) as ?Mods) where {

?s a evdx:Book ; evdx:title ?book .

?m a evdx:Module ; evdx:hasBook ?s .

?c a evdx:Course ; evdx:year 2022 ; evdx:hasModule ?m .

?d a evdx:Department ; evdx:hasCourse ?c .

} group by ?s ?book

order by desc(?Depts) desc(?Mods) limit 6

Table 8: Results for additional question AQ2.

?book ?Depts ?Mods

THOMAS ΑΠΕΙΡΟΣΤΙΚΟΣ ΛΟΓΙΣΜΟΣ, [George B. Thomas], Jr., Joel Hass, … 79 142

Εισαγωγή στην πληροφορική, Evans Alan, Martin Kendall, Poatsy Mary Anne (Συγγρ.) … 74 96

Πώς γίνεται μια επιστημονική εργασία;, Ζαφειρόπουλος Κώστας 70 102

Επιχειρηματικότητα και μικρές Επιχειρήσεις 2η Έκδoση, David Deakins, Mark Freel 62 73

ΤΕΧΝΗΤΗ ΝΟΗΜΟΣΥΝΗ - 4η ΕΚΔΟΣΗ, ΒΛΑΧΑΒΑΣ Ι./ΚΕΦΑΛΑΣ Π./ ΒΑΣΙΛΕΙΑΔΗΣ … 58 83

ΕΙΣΑΓΩΓΗ ΣΤΗΝ ΕΠΙΣΤΗΜΗ ΤΩΝ ΥΠΟΛΟΓΙΣΤΩΝ, BEHROUZ FOROUZAN 58 72

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development

5 CONCLUSIONS

In this paper, we have presented the methodology for

generating the Evdoxus Knowledge Graph, that con-

sists of information about the structure of Greek Uni-

versities, including their Departments, Study Prog-

rams, Courses, and the textbooks that are used and

freely provided to the undergraduate students. This

information was extracted from the Evdoxus site, an

online system for the management of the total ecosys-

tem for the free provision of textbooks to the under-

graduate students at the Greek Universities. The ex-

traction / conversion application, called EvdoGraph,

has been developed using SWI-Prolog. The KG is us-

ing the vocabulary of a simple ontology we have de-

veloped, which has been also aligned with some well-

known ontologies for interoperability. Moreover, the

KG fully endorses the Linked Open Data initiative by

linking University class instances with their corre-

sponding DBpedia entries. The final result is a quite

rich KG with almost 4 million explicit triples that is

freely available through a SPARQL endpoint.

The possible uses for the KG are countless. In the

paper we have demonstrated several competency

questions that can be answered via SPARQL queries

that generate detailed reports or aggregate statistical

analyses concerning the “performance” (popularity

among the Greek Universities) of either one book or

several books in comparison. More ideas for using the

KG could be for marketing purposes, i.e., publishers

could have an instant clear picture of the University

market in order to strategically decide for new books

or promotion targets, or faculty researchers could an-

alyse the Greek Higher Education landscape, i.e., an-

alyse what kind of courses are taught at various disci-

plines, or compare study programs at different Uni-

versities / Departments. And, of course, according to

(European Data Portal, 2020) opening up official in-

formation can support technological innovation and

economic growth by enabling third parties to develop

new kinds of digital applications and services.

Ideas for future work could include the more fine-

grained treatment of textbooks, as currently their title

is actually the whole citation of the book. This will

allow statistics about authors and publishers, as well

as possibility to further link the KG to external bibli-

ographic LOD datasets. Another option would be to

link Study programs and modules to their syllabus de-

scription at various University repositories or open

data APIs, such as the one of the Aristotle University

https://ws-ext.it.auth.gr/swagger/

of Thessaloniki

. Finally, the University / Depart-

ment instances could be linked to more LOD datasets,

such as Wikidata, even though this can already be in-

directly (albeit partially) provided via DBpedia’s in-

terlinking to several other LOD datasets

REFERENCES

Abu-Salih, B. (2021). Domain-specific knowledge graphs:

A survey. Journal of Network and Computer

Applications, 185. doi:10.1016/j.jnca.2021.103076

Bassiliades, N. (2014). Collecting University Rankings for

Comparison Using Web Extraction and Entity Linking

Techniques. In ICT in Education, Research and

Industrial Applications, CCIS (Vol. 469, pp. 23-46).

Springer-Verlag. doi:10.1007/978-3-319-13206-8_2

Berners-Lee, T. (2009). Linked data. Retrieved from

WWW Consortium: http://www.w3.org/DesignIssues/

LinkedData.html

Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data-

the story so far. International journal on semantic web

and information systems, 5(3), pp. 1-22.

doi:10.4018/jswis.2009081901

Corson-Rikert, J., Mitchell, S., Lowe, B., Rejack, N., Ding,

Y., & Guo, C. (2012). The VIVO Ontology. In VIVO,

Synthesis Lectures on Data, Semantics, and Knowledge

(pp. 15–33). Springer, Cham. doi:10.1007/978-3-031-

79435-3_2

Demartini, G., Enchev, I., Gapany, J., & Cudré-Mauroux,

P. (2013). The Bowlogna ontology: Fostering open

curricula and agile knowledge bases for Europe's higher

education landscape. Semantic Web, 4(1), pp. 53-63.

doi:10.3233/SW-2012-0064

European Data Portal. (2020). The Economic Impact of

Open Data: Opportunities for value creation in Europe.

European Union. doi:10.2830/63132

Färber, M. (2019). The Microsoft Academic Knowledge

Graph: A Linked Data Source with 8 Billion Triples of

Scholarly Data. ISWC 2019, LNCS. 11779, pp. 113-

129. Springer. doi:10.1007/978-3-030-30796-7_8

Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo,

G., Gutierrez, C., . . . Zimmermann, A. (2022).

Knowledge graphs. ACM Computing Surveys, 54(4),

pp. 1–37. doi:10.1145/3447772

Jaradeh, M. Y., Oelen, A., Farfar, K. E., Prinz, M.,

D’Souza, J., Kismihók, G., . . . Auer, S. (2019). Open

research knowledge graph: Next generation

infrastructure for semantic scholarly knowledge. K-

CAP 2019 (pp. 243-246). ACM. doi:10.

1145/3360901.3364435

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A

Modern Approach, 4th edition. Pearson.

Stancin, K., Poscic, P., & Jaksic, D. (2020). Ontologies in

education - state of the art. Education and Information

http://wikidata.dbpedia.org/services-resources/interlink-

ing

EvdoGraph: A Knowledge Graph for the EVDOXUS Textbook Management Service for Greek Universities

Technologies, 25(6), pp. 5301-5320. doi:10.

1007/s10639-020-10226-z

Stoilos, G., Stamou, G., & Kollias, S. (2005). A String

Metric for Ontology Alignment. ISWC 2005 (pp. 624-

637). Springer. doi:10.1007/11574620_45

Vandenbussche, P.-Y., Atemezing, G. A., Poveda-Villalón,

M., & Vatant, B. (2017). Linked Open Vocabularies

(LOV): A gateway to reusable semantic vocabularies

on the Web. Semantic Web, 8(3), pp. 437-452.

doi:10.3233/SW-160213

Vlahavas, I., Kefalas, P., Bassiliades, N., Kokkoras, F., &

Sakellariou, I. (2020). Artificial Intelligence, 4th

edition. Thessaloniki: University of Macedonia Press.

Vrandečić, D., & Krötzsch, K. (2014, October). Wikidata:

a free collaborative knowledgebase. Communications

ACM, 10, pp. 78–85. doi:10.1145/2629489

Wielemaker, J., Schrijvers, T., Triska, M., & Lager, T.

(2012). SWI-Prolog. Theory and Practice of Logic

Programming, 12(1-2), pp. 67-96. doi:10.1017/

S1471068411000494.

KEOD 2023 - 15th International Conference on Knowledge Engineering and Ontology Development