BUILDING AND VALIDATING AN ONTOLOGY
FOR PUBLIC PROCUREMENT: THE CASE OF
CAMEROON

Jules Kouamo, Etienne Kouokam, Ghislain Auguste Atemezing

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Jules Kouamo, Etienne Kouokam, Ghislain Auguste Atemezing. BUILDING AND VALIDATING AN ONTOLOGY FOR PUBLIC PROCUREMENT: THE CASE OF CAMEROON. Conference on Research in Computer Science (CRI 2023), Dec 2023, Yaounde, Cameroon. hal-04420667

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<figure><img src="image_1.png" /></figure>
BUILDING AND VALIDATING AN
ONTOLOGY FOR PUBLIC PROCUREMENT:
THE CASE OF CAMEROON

Jules KOUAMO¹, Etienne KOUOKAM¹, and Ghislain ATEMEZING²[0000-0003-1562-6922]

¹ Computer Science Department, University of Yaounde I, Yaounde, Cameroon
secretariat@facsciences.uy1.cm
https://facsciences.uy1.cm/
² Mondeca, 75009 Paris, France
ghislain.atemezing@mondeca.com
https://mondeca.com

**Abstract.** Public procurement or tendering generally refers to the process followed by public authorities for the purchase of goods and services. In this process, the publication of tender notices online has become an essential and mandatory step in many countries, including in Cameroon. However, this step produces a huge amount of data, which makes the search for information tedious. Several solutions have been explored to overcome this problem, including the use of the semantic web, which allows web resources to be understood by machines. This paper presents the APCOontology, a public procurement ontology that semantically describes the publication stage of the public procurement process. We use the APCOontology to generate a knowledge graph for Cameroonian public procurement that will facilitate information retrieval and data analysis in Cameroonian public procurement.

**Keywords:** public market · tender · semantic web, ontology, open government data

# 1 Introduction

A public contract, whether awarded to a public entity or a private economic operator, meets the needs for works, supplies or services of the purchaser, whether it is a public entity (State, local authority, hospital, etc.) or a private entity. Public procurement legislation governs the rules that apply to these contracts, and in Cameroon, it is Decree 2018/366 of 20 June 2018 on the Public Procurement Code<fnref n="3" /> which sets out the provisions.

The publication of public contracts is an essential and now mandatory step in many countries, including in Africa, where it has become digitised with the advent of the web. In Cameroon, for example, Decree N°2018/0001/PM of 05 Jan-

<footnote n="3">³ https://tinyurl.com/5ae29rt4, last consultation: July 2023</footnote>
uary 2018 authorises the creation of a specific e-procurement platform for pub-
lic contracts, named "Cameroon On Line E-procurement System" (COLEPS)<fnref n="4" />.
This transition to digital technology has considerably increased the amount of
data available, with around 11,600 documents relating to tender notices pub-
lished each year.

This overabundance of online data poses a challenge in terms of finding rele-
vant information. Traditional searches based on keywords are no longer sufficient
to sort and interpret these massive volumes of data. This is where the concept
of the Semantic Web, introduced by Tim Berners-Lee<fnref n="5" /> in the 90’s, comes in as
a solution.

This article seeks to leverage the Semantic Web's capacity to make web resources understandable for both humans and machines, enhancing the effective structuring and interpretation of data. The specific focus is on applying sound semantic web practices, particularly in the context of Cameroon, to harness the substantial data available in public contracts. The aim is to facilitate the retrieval and analysis of information related to public procurement in Cameroon.

The article is organised as follows: the second section presents the public
procurement domain, the third gives a state of the art of existing ontologies
for public procurement, the fourth describes the development stages of the
APCOontology.<fnref n="6" /> The fifth presents the validation procedures and principles, as
well as the knowledge graph generated from the ontology. Finally, we conclude
our work by making some recommendations.

The APCOontology is freely searchable and accessible online.<fnref n="7" />.

## 2 Public procurement

According to the World Bank<fnref n="8" />, public contracts are commercial transactions between governments and companies, in which governments buy goods, services or construction work from companies. It is a process that takes place in several stages.

2.1 Public procurement process

The public procurement process comprises several phases:

preparation phase: A public authority prepares the information and docu-
ments for the envisaged public procurement and a call for tenders. The public
authority can leverage available data sources to search for similar contracts. This

<footnote n="4">⁴ https://www.marchespublics.cm/index.do, last consultation: June 2023</footnote>

<footnote n="5">⁵ https://www.w3.org/DesignIssues/LinkedData.html, last consultation: January 2023</footnote>

<footnote n="6">⁶ https://ai4africa.github.io/apcoKG/documentation/index-en.html</footnote>

<footnote n="7">⁷ https://ai4africa.github.io/apcoKG/</footnote>

<footnote n="8">⁸ https://tinyurl.com/worldbanks</footnote>
is useful when the authority is preparing the contract. For instance, it can obtain
information on the usual prices of similar markets, constraints often associated
with this type of market, etc.

**publication phase:** During this phase, the call for tenders is published on various platforms. In Cameroon, tender notices are published in the Public Procurement Journal, on the websites of the Ministry of Public Procurement (MINMAP)<fnref n="9" />, and that of the Public Procurement Regulatory Agency (ARMP)<fnref n="10" />. The authority can also search for suitable suppliers in available data sources and invite them to submit their bids based on the type of tender. For example, searching for providers who have executed similar contracts with good evaluations. The authority then receives bids within a specified period and selects the best offer. Once the best offer is chosen, a contract is signed between the authority and the provider, and an award decision is published.

**evaluation phase:** Here, the public authority assesses the execution of the contract in relation to all given criteria. Additionally, the final and actual price of the contract is then known. The last two phases can be iterated until the contracting authority is satisfied.

The public procurement process involves a number of key players to ensure transparency and efficiency: the contracting authority, the procurement department, the tender committee, the candidates, the successful tenderers, the contracting authority, the prime contractor and the auditors. These players may vary according to the procedures and laws in force in each country.

The framework of our work being that of the semantic web, we are focusing
on the publication phase of calls for tender in the public procurement process.

## 2.2 Limitations in the public procurement process: the case of Cameroon

The publication phase of the public procurement process in Cameroon faces a
number of difficulties [5], including:

**Difficulty in finding relevant information:** With a large number of published tender notices, it can be challenging for bidders to identify those pertinent to their activities. Moreover, tender publication notices are often distributed across different sites, making information retrieval difficult. In Cameroon, tender notices are published on two different platforms: the public procurement journal's website and the website of the Ministry of Public Procurement of Cameroon;

**Difficulty in understanding the terms used:** technical or legal terms in tender notices can be challenging for bidders unfamiliar with the domain to comprehend;

<footnote n="9">⁹ https://minmap.cm/, lastconsultation:september2023</footnote>

<footnote n="10">¹⁰ https://www.armp.cm/, lastconsultation:october2023</footnote>
**Non-compliance of publication notices:** Notices may be poorly formulated, lack necessary information, or contain errors;

**Non-compliance of submission documents:** Submission documents may be incomplete or fail to meet the requirements of the tender;

**Difficulty in understanding technical specifications:** technical specifications can be challenging for candidates to understand, leading to inappropriate bids.

To address these shortcomings, various solutions have been proposed, including the ontology development. Ontologies help overcome these challenges by enabling better structuring of data in tender notices. By using a specific ontology, public entities can clearly describe their needs and employ standardized terms. This allows bidders to better comprehend the tender requirements and provide more precise and tailored bids. Ontologies can also facilitate the search for relevant tender notices, enabling bidders to navigate easily through different market categories and quickly find notices corresponding to their activities.

## 3 Ontologies and public procurement

An ontology is defined as a formal specification of a shared conceptualisation [12]. Ontologies help people and machines to communicate in a precise way to support the exchange of semantics. There are several in the field of public procurement.

PPROC (Public Procurement Ontology) [3] was created to contribute to the development of standards that can be used to publish all information deemed appropriate from a transparency perspective. It enables the provision of information recorded in the buyer profile of public entities and provides all relevant information regarding public procurement procedures.

The enumeration of terms relevant to the model was conducted with two sets of stakeholders: firstly, the company IASOFT, which developed buyer profiles for numerous Spanish administrations, and secondly, legal experts. Subsequently, the PPROC development team used these terms as a basis to prepare an initial list of entities, including cardinalities, domain, and range of properties.

The strategy employed for ontology development is BOTTOM-UP, and its implementation is done in OWL, following the method proposed by Noy et al [13]. PPROC uses a primary class (Contract) that serves as the entry point to connect with other classes. Several classes from the PCO ontology are reused, and it considers that a contract can be subdivided into lots, making each lot a separate market. A contract is subject to selection criteria, which can be objective (low price, delivery time, etc.) or subjective (reputation, determined by experts). To describe the organization (stakeholders), properties, and vocabulary of other ontologies were used, such as Schema.org, Friend Of A Friend (FOAF), or SKOS.
To define the content whose location or specific place must be known (e.g., the office of the contracting authority), the PLACE class was used along with Schema.org properties. To define the type of procedure and its urgency, SKOS was used.

In PPROC, the duration of a contract does not end with execution, meaning the moment when the procurement process is considered finished. Contracts are often subsequently modified through specific procedures, which often change aspects such as price or execution time. A block called ACCOMPLISHMENT has been created for this phase, due to the fact that regulations on contract publication vary by country, but for the sake of transparency, modifications are published.

**LOTED2** [8] is a legal ontology that supports the identification of legal concepts and, more generally, legal reasoning. It was developed to address the question: 'How to design a legal ontology that meets the dual demand for improved access to legal information while preserving the richness of legal content, constraints, and peculiarities of the legal domain?' LOTE2 represents a compromise between a precise representation of legal knowledge and requirements for the web of data. Its source of information is the website that publishes all European public procurement notices, TED, which gathers approximately 1500 notices per week. The ultimate goal of the ontology is to enable the construction of semantic legal web applications that support public procurement by matching supply and demand. Two main directives cover the domain of European public procurement: Directive 2004/18/EC and Directive 2004/17/EC. The first regulates the coordination of public procurement procedures for works, supplies, and public services by contracting authorities (i.e., authorities operating in the so-called 'ordinary' sectors); the second regulates procurement procedures for entities operating in the water, energy, transport, and postal services sectors (i.e., the 'public services' sectors). These two legal sources serve as the reference point for deriving the exact meaning of terms used to describe the domain of public procurement and for extracting the information necessary to construct any logical theory formalizing domain knowledge. The design of LOTE2 is based on both a bottom-up approach (extracting legal concepts from legal sources) and a top-down approach (analyzing standard forms).

**PCO** ontology (*Public Contract Ontology*)[9] was developed with the aim of demonstrating the application of linked data for publishing contract-related data in the public sector, emulating the market process of matching supply and demand to generate 'business impact.' It encompasses information related to public contracts published by contracting authorities during the procurement process, based on the analysis of existing public procurement portals and the information published by contracting authorities on these portals. Not all information is considered, but primarily those that are useful for connecting public contracts and potential suppliers. Therefore, a significant portion of the information produced during the tendering phase (description of the public contract, received
bids, and the ultimately accepted bid) is considered. Information resulting from other phases is only partially considered.

**OCDS** (*Open Contracting Data Standard*) [10] enables the publication of data and documents at all stages of the public procurement process by defining a common data model for structured publication. It was created to help organizations enhance contract transparency and enable in-depth analysis of contract data by a broad range of users. It is document-oriented and focuses on packaging and delivering relevant data iteratively and eventfully through a series of versions.

<table><caption>**Table 1.** Summary of ontologies for public procurement.</caption><thead><tr><th></th><th>PPROC</th><th>PCO</th><th>OCDS</th><th>LOTED 2</th></tr></thead><tbody><tr><td>Reviewed ontologies</td><td></td><td></td><td></td><td></td></tr><tr><td>required granularity</td><td>OK</td><td>X</td><td>OK</td><td>X</td></tr><tr><td>Complexity</td><td>X</td><td>OK</td><td>X</td><td>X</td></tr><tr><td>Objective</td><td>X</td><td>OK</td><td>OK</td><td>X</td></tr><tr><td>Context</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Number of classes - properties</td><td>78 - 130</td><td>22 - 51</td><td>24 - 142</td><td>22 - 101</td></tr></tbody></table>

Table1 present a comparison of four popular ontologies for tender on several criteria according to [13]. None of the ontologies studied is adaptable to an African context because the resources used to develop them are exclusively European, for example approximately 20% of the concepts manipulated by PPROC come from Spanish law, and the others from the TED public procurement platform. Which means that most of the concepts manipulated in these are not semantically close to African concepts. As an illustration, we have the concept of 'Project Owner' which, although similar in the way it is implemented, does not play the same role in both contexts. To improve the structuring of Cameroonian public procurement data, it is therefore necessary to develop a specific ontology.

## 4 APCO: An ontology for African public procurement

To build our domain ontology, the methodology developed by [7] has been used. It is an incremental methodology made up by 7 steps:

1. Define the domain and scope of the ontology;

2. Consider the potential reuse of existing ontologies;

3. List the important terms in the ontology;
4. Define the classes and the hierarchy of classes;

5. Define the properties of classes - attributes;

6. Define the facets of attributes;

7. Create the instances

The first step is to define the scope and domain of the ontology and therefore
answer the following questions:

* Q1 what domain will the ontology cover?

* Q2 for what purpose will we use the ontology?

* Q3 What types of questions will the ontology have to answer?

(Q1): The domain covered by the ontology is that of public procurement, aiming to provide consensual knowledge and an information model related to public procurement, specifically those in Cameroon.

(Q2) Starting from the observation that research is still conducted in a conven-
tional manner on various publication sites for documents such as tender notices
and other public procurement-related documents, which hinders the production
of relevant information. The ontology we are developing aims to search for per-
tinent information based on metadata describing documents related to public
procurement.

(Q3) To validate the process, competency questions<fnref n="11" /> were formulated and validated with the help of an expert in the field of public procurement. "Subsequently, we identify ontological and non-ontological resources. As non-ontological resources, we have Decree No. 2018/366 of June 20, 2018, establishing the Public Procurement Code1, which governs public procurement in Cameroon. It contains the vocabulary used for today's public procurement. To date, there is no RDF version of this vocabulary. We can also mention Order No. 004/CAB/PM of December 30, 2005, concerning the implementation of the Public Procurement Code. Finally, ontological resources include, among others, PCO, PPROC, Good Relation, Dublin Core, Geofla and DBpedia.

Following the definition of the domain and scope of the ontology, the next step
is to list the terms and concepts of the ontology. We use documents governing
public procurement in the Central African sub-region, as well as data from the
websites of various public procurement regulatory agencies, to list the terms
and concepts that should be used in the ontology. This step was carried out by
ontology developers and some stakeholders in public procurement. The output
of this step is a list of terms that will be used to successfully proceed to the next
step in the methodology, namely the possible reuse of ontologies.

Indeed, with the recent development of the semantic web, there is a multi-
tude of vocabularies available online, along with several web applications that

<footnote n="11">¹¹ https://github.com/AI4Africa/apcoKG/blob/main/spec/CompetencyQuestions.md</footnote>
catalog these different vocabularies. This is the case with LOV[1], which gathers millions of vocabularies available on the internet. We use this platform to explore potential ontologies that we will reuse for the development of our ontology. It is sufficient to enter a term into the platform and see if the term already exists in an ontology, and then check if its description aligns with the semantics desired in the ontology. For example, the term "VILLE" (CITY), which belongs to our initial list from the phase of establishing a list of terms. This term had already been described in a vocabulary (DBpedia), and its type and description had the same semantics that we wanted to highlight in the ontology. Therefore, the DBpedia ontology was reused. Table 2 presents all the concepts reused in various other ontologies.".

<table><caption>**Table 2.** Reused concepts and properties</caption><thead><tr><td>Ontologies</td><td>Concepts</td></tr></thead><tbody><tr><td>DBpedia</td><td>City, Department, Region, department, locationCity</td></tr><tr><td>FOAF</td><td>Person</td></tr><tr><td>GoodRealtion</td><td>BusinessEntity, Offering</td></tr><tr><td>Public Pontrat(PC)</td><td>bidder, contractactingAuthority, procedureType</td></tr><tr><td>geofla</td><td>region</td></tr><tr><td>dcterms</td><td>date, identifier, title</td></tr><tr><td>PPROC</td><td>numberOfLots</td></tr></tbody></table>

We then define the classes, their hierarchy and properties. The main class is **apco:Publication**, representing documents related to public procurement. We use a "TOP-DOWN" approach to make hierarchy of classes, starting with the most general ones and specifying their characteristics. Properties, such as **apco:contractingAuthority** linking a tender notice to the contracting authority, are also defined. The facets of the attributes and properties are established, such as the cardinality 0..1 of **apco:makesOffer** meaning that a bidder can make at most one offer. Figure 1 shows an overview of part of the ontology.

The ontology is available online and can be consulted and used in the Linked Open Vocabulary (LOV).<fnref n="12" /> [1].

# 5 Ontology validation and knowledge graph

## 5.1 Ontology validation

APCO has been developed following the ontology construction rules and principles defined by [2]: Completeness is ensured by definitions associated with con-

<footnote n="12">¹² https://lov.linkeddata.es/dataset/lov/vocabs/apco, last consultation: May 2023</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Ontology visualisation</figcaption></figure>

cepts and relations, using necessary conditions, or even necessary and sufficient
conditions where possible. In addition, ontological extensibility is maximised,
ensuring that each term has a specific definition that distinguishes it from more
general or specialised terms. This enables a robust and accurate ontology to be
established, promoting a comprehensive representation of the Cameroon public
procurement domain. To validate the structure[11] of our ontology, we are using
two tools, *OntoCheck* and *Pellet*. These extension modules for the PROTÉGE
ontology editor are designed to check compliance with naming conventions, meta-
data completeness, cardinalities, labels and ontology metrics.

Unlike the ontologies studied in 1, our approach for the APCOontology in-
volved the use of resources specific to the Cameroonian and African context. This
approach favours a more appropriate interpretation of the concepts and relation-
ships within the ontology by public procurement stakeholders in Cameroon.

The ontology was designed by answering all the competence questions in
the form of SPARQL queries<fnref n="13" />, demonstrating its effectiveness. In addition, its
design has been guided by the FAIR principles, guaranteeing that the data is
accessible, interoperable, reusable and easy to share.

<footnote n="13">13 https://github.com/AI4Africa/apcoKG/blob/main/spec/CQs.rq</footnote>
## 5.2 Knowledge graph

The aim is to use the ontology developed to generate a knowledge graph of public procurement in Cameroon. The experimental data used comes from ARMP and includes contract notices, award decisions and other publications on public procurement<fnref n="14" /> of Cameroon for the years 2016 to 2021. It consists of 57589 rows and 20 columns. The knowledge graph is generated in several phases, as shown in the figure. 2.

<figure><img src="image_3.png" /><figcaption>Fig. 2. Knowledge graph generation architecture</figcaption></figure>

**Data Acquisition:** During the data collection phase, we utilize information from the public procurement database as well as certain data retrieved from files, which are then transformed into the desired format using the PowerBI tool.

**Data Transformation:** The transformation of this data by formatting it according to specific needs, in particular using the OpenRefine tool to define data elements such as URIs and store them in a triple store (Ontolinga's GraphDB) growing from 57,589 rows at the outset to 778,688 corresponding to our triplets.

<footnote n="14">¹⁴ https://www.armp.cm/</footnote>
**Application:** In the application phase we use the generated knowledge graph<fnref n="15" /> in the form of triples to develop smart applications such as to improve searching information on public contracts, and visualise and analyse data.

The knowledge graph generated in this way will provide a structured representation of data relating to contracts, suppliers, calls for tender and so on. This makes it easier to navigate between the different entities and their relationships, helping public procurement players to discover related information more efficiently. In addition, thanks to the semantic links between concepts, the knowledge graph helps to resolve ambiguities and refine searches, improving the relevance of results.

# 6 Conclusion and future work

To conclude our work, let us first review our approach. Our aim was, among other things, to improve information retrieval in the field of public procurement, particularly in Cameroon, and to publish data in this field in accordance with good semantic web practice by building and validating an ontology for public procurement. Initially, we immersed ourselves in the field of public procurement, and then reviewed the state of the art of the various existing ontologies for public procurement. We then used the methodology proposed by [7] to develop our ontology called **APCO**. Once we had developed our ontology, we generated a knowledge graph using public procurement data from ARMP in Cameroon. We then had to validate the ontology. To do this, we used competency questions, inference tools, FAIR principles and a public procurement expert.

The prospects for our work include using the ontology in other African countries, improving and making available an interface for generating user queries, dereferencing the URIs generated to facilitate reuse in other applications, aligning the data with other existing data in the *Linked Open Data cloud*, as well as the incorporation of RDF resources representing the administrative division of our regions not yet available on the Web of Data. We hope that our results will help to improve information retrieval in the field of public procurement in the future.

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<footnote n="15">¹⁵ https://linkedvocabs.org/dataset/dump\textunderscoreapcoKG.ttl, last consultation: January 2023</footnote>
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