Besides tutoring and consultancies, the development of academic and scientific documents in universities evidenced collaborative work. This paper presents an ontology-based approach to describe different modes of collaboration by reusing and enriching data from an institutional repository, from a collection of posters. The approach uses an application ontology that makes explicit the relationships among authors and posters. The paper presents a list of competency questions that are answered in natural language and by the ontology terminology. The proposed approach is of value as this offers machine-readable data to support further analysis and inference mechanisms.
Besides tutoring and consultancies, the development of academic and scientific documents in universities are evidences of collaborative work that can be used for supporting management decisions. At present, the Universidad Politécnica de Puebla (UPPue) distributes open-access documents such as articles, master’s thesis and posters by using the infrastructure of its institutional repository (IR), from now on, UPPue-IR.
Posters are documents written by graduate students of different academic programs
where they report partial results of research activities; posters are often presented at
symposiums or congresses. UPPue-IR is a documental database that allowCsopuysreigrhstt©o 2019 for this paper by its authors.
retrieve validated documents frequently produced between teachers, studeAnttstriobrutbioonth4.0 International (CC BY 4.0)
of them. From a technical point of view, this repository implements the Open
Archives Initiative Protocol (OAI-PMH protocol) (Lagoze and Van de Sompel, 2001)
to interoperate with the National Repository (RN, 2019); this protocol is also used to
export descriptive data of documents, commonly refered as metadata. The
implementation of this protocol implies that documents are depicted by using the
Dublin Core Metadata Element Set as the default metadata standard
The elements of this standard related with collaborative work among authors of posters are creator and contributor, the first one stores the name of a student name, while the second one refers to his/her advisor; if there is a third or fourth author, their names are also stored in multiples instances of the contributor element. Unlike posters are retrieved by search engines, the order in a list of authors for posters or other types of academic documents neither their contribution are taken into account.
This paper presents an ontology-based approach to describe collaboration among authors of posters by reusing and enriching data from the UPPue-IR. The approach uses an application ontology that makes explicit the relationships among students, teachers and posters.
The paper is organized as follows. Section 2 presents user types and their competency questions (CQs). Section 3 describes the main ontology components.
Section 4 contains the answer for CQs. Section 5 enumerates implicit information that is derived from the ontology. Finally, we conclude in Section 6 with a summary of the present work along with further research perspectives. 2
According to (Gruber, 1995), an ontology is a “specification of a shared
conceptualization”; in computer and information sciences, ontologies are formal
definitions of types, properties and relationships between entities that exist in a
particular domain of interest (Ecured, 2019). Ontologies are knowledge models
composed by instances, concepts, rules and relationships that have a unique
representation for a group of people or computers.
The scope of the ontology proposed is determined by the Competency Questions
(CQs) of Table 2, more information about CQs can be found in
The main class of the proposed ontology is called University, the purpose is to have a general concept that refers to the context of use for the proposed ontology. Table 3 shows the names and descriptions of three classes at the second level of the ontology, remaining concepts are obtained by generalization and specialization and distribute between the third or fourth level in the class hierarchy. By convention, class names starts with a capital letter.
Description Department
This class refers to the adscription of a student or teacher.
A document written by a student where he/she reports partial results of his/her research activities.
The User class integrates user types, (advisor, manager, student and teacher. An advisor is a type of teacher. 3.2
The classes at the second level of the hierarchy are described by using data properties. For example, the name, last name or gender of a User, the title and date of a Poster are modeled as data properties. All interoperability aspects that correspond to the implementation of OAI-PMH protocol and the DC elements can be represented as data properties that link posters and users with data values from an XML Schema Datatype or an RDF literal (RDF, 2001). 3.3
Collaborative work between authors to produce posters are modeled in the ontology as object properties, they are associated with domain and range restrictions as is illustrated in Table 4.
Range Department
Teacher Department
Poster
Student Department
Student Poster Poster Poster Poster Manager Posters and user types are modeled as ontology instances. A semi-automatic process has been designed in order to transform metadata from UPPue-IR into ontology instances. As a way of illustration, Figure 1 shows how the 133 posters that form the posters’ collection are distributed by year. apellidoMaterno, second last name nombreDePila, name Autor, Author, a subclass of the User class esAutorDe, isAuthorOf cartel1, poster1 genero, gener apellidoPaterno, first last name
Figure 4 shows the ontology metrics for the posters’ collection. Note that the number of axioms is 2985 and that there are 396 ontology instances (individual account).
CQs are used as guidelines for ontology evaluation. This section presents the answers to CQs in natural language and using formal concepts. An excerpt of the usage information of the ontology elements are described in this section as formal answer.
CQ1: What is a poster?. A poster is a document written by a graduate student where he/she report partial results of his/her research activities. .
Formal answer: 1. Annotation property: rdf:isDefinedBy for Poster 2. Data type property: posterData for Poster 3. Object property: wasProducedIn, isManagedBy, isFirstAuthorOf, isSecondAuthorOf, isThirdAuthorFor, isFourthAuthorOf CQ2: What is a poster for?. A poster is a document to report advances or partial results of reserarch activities.
Formal answer: 1. Class: Poster 2. Poster SubClassOf University 3. Object properties: wasProducedIn, hasPoster CQ3: What kind of DC elements are used to describe a poster?. Title, date, year, subject (for the department) and a list of authors (creator and contributor elements). Formal answer: 1. Class: Poster 2. Poster SubclassOf University 3. Data property: title, (functional) 4. Data property: year, (functional) 5. Data property: subject, (functional) 6. Date property: date, (functional) CQ4: Who use a poster?. UPPue-IR user types are advisor, manager, student and teacher.
Formal answer: 1. Class: User 2. (Advisor, Manager, Student, Teacher) SubClassOf User 3. Annotation property: rdf:isDefinedBy for Advisor, Manager, Student,
Teacher CQ5: Which are mandatory metadata to deposit a poster into the UPPUE-IR?. Formal answer: 1. Data property: title, string or RDF literal 2. Data property: year, integer
Data property: subject, string or RDF literal
Date property: date, date CQ6: How posters are introduced into the ontology?. Posters are introduce into the ontology as instances, the information about collaborative work of authors is represented in object properties.
Formal answer:
1. Class: Poster
2. Poster SubClassOf University
3. Object properties: see Table 4
CQ7: Who forms the list of authors in a poster?. A graduate student (the first author),
an advisor (the second author) and two teachers (the third and fourth author).
Formal answer:
1. Object properties isFirstAuthorOf, isSecondAuthorOf, isThirdAuthorOf,
isFourthAuthorOf.
2. isFirstAuthor, domain (Student)
3. isSecondAuthor, domain (Advisor)
4. isThirdAuthor, domain (Teacher)
5. isFourthAuthor, domain (Teacher)
In summary, although the ontology is simple in terms that this represents the addition
of semantic information to a particular collection of data from an IR, this is able to
represent CQs and their answers using its own terminology. All the inconsistencies
were corrected before release. Hermit and Pellet reasoners were used for validation of
logical consistency. The ontology can be exported to different semantic web
languages such as RDF (RDF, 2001) or the Ontology Web Language
The formal features of the ontology enables to extract implicit knowlegde as the following: • • • • • • •
If the second author of a poster is a teacher, then he/she is considered an advisor If a student is the first author of a poster, then he/she is a graduate student If a poster only has two authors, the first one is a graduate student and the second one his/her advisor A department has many teachers but a teacher is assigned only to a department The Poster and User are disjoint classes A user can not be a Student and a Teacher at the same time If a teacher is an advisor, that means that at least his/her name appears in the second place of an authors’ list The establishment of axioms, cardinality, domain and range restrictions as well as the definition of object properties, enables the formal representation of knowledge useful to discover possible data inconsistencies. For example, cardinality restrictions can be inserted into the ontology in order to establish a minimum, exactly or maximum number of authors for each poster. Ontologies as the described in this paper can be used to represent collaborative work of other types of documents according to the interests of potential users. 6
This paper presented an ontology-based approach to describe collaborative work by reusing and enriching data from an institutional repository. Ontology instances were obtained by exporting metadata of a posters’ collection. The approach uses the ontology to formally represent relationships among users and posters.
The paper used a list of CQs that are answered by the ontology terminology in natural language and formal answers. The natural language answers are stored as definitions in the RDF language, while the formal answers are extracted from the usage dialogs from the Protégé ontology editor. Ontology information is used by reasoners to infere new knowledge as well as to discover possible data inconsistencies, the last feauture add value to data from IRs.
The ontology itself and their instances form a machine-readable dataset that can be explote by semantic technologies. As future work, we plan to work in the design of an ontology assessment process to get feedback from the constructed ontologies.