Data Science is the science that relates to the extraction of knowledge and information from data. As the amount of data we produce increases, data science projects have become a very popular endeavor in recent years, accompanied by an increased interest in research relating to data science resources such as the data sources, algorithms, technologies, and visualizations as well as the application domains of these data science resources. The amalgamation of the results gained by data science projects can be a complex process that can be time and labor-intensive. This research seeks to reduce the project complexity by proposing an ontology that can represent data science (research) project based on domain-specific (data science) project attributes that can represent all conceivable aspects of a data science project.
Data science aims to clean, prepare, and analyze different data sets to extract meaning from
data [
One solution for this problem is the use of a knowledge graph which is a knowledge
representation that can effectively organize and represent knowledge [
The star-like ontology was defined based on a triple structure that considers the subject
to be the data analytics project, the object to be the data analytics project attribute relating
to a specific aspect of a data science project, and the predicate to be the relationship type
defined based on the data analytics project attribute. This structure meant that each
attribute type needed to be represented as a class in the ontology with a corresponding
property defined for the class representing the data analytics project, relating it to the data
analytics project attribute value. This meant that when additional analytics project
attributes were defined, it increased the complexity (number of classes) of the ontology on
a class level, and when the ontology changed, the process related to changing the ontology
was complex (editing many “one-level” ontology classes and their properties). Additionally,
this ontology, like any other ontology, relied on a static representation of the data analytics
domain and assumed that the user is only interested in the aspects of the data analytics
project that are represented by the analytics project attribute types. This work seeks to
resolve the issue by making an ontology that can be relatively easily modified. The previous
ontology was constructed inductively by considering the data analytics projects. The one
proposed in this paper is built to represent what can be considered as the already
established(standard) aspects of a data science project defined based on the data science
body of knowledge; it also seeks to allow additional aspects to be introduced (on an instance
level) based on the users’ needs or the results produced by data science projects. We
accomplish this by proposing an ontology that does not seek to represent the domain of
data science but instead to represent projects within said domain. As there is no specific
body of knowledge in data analytics available, we chose the body of knowledge that
represents data science thus, we decided to increase the scope of the ontology to data
science with data analysis being considered a knowledge area under this domain as defined
in the Data Science Body of Knowledge (DS-BoK) by the EDISON project [
To ensure that the created ontology conforms to existing knowledge engineering
practices we followed the Ontology Definition 101 methodology [
The Ontology Definition 101 methodology [
In the remainder of this section, sub-sections are organized to represent the results related to each of the steps mentioned above, with Step 1 discussed in Section 2.1, Step 2 reflected in Section 2.2, and Steps 3-6 introduced in Section 2.3. Step 7 is presented in Section 2.4, which also presents the final version of the ontology.
The domain of the proposed ontology is projects within data science that aim to clean,
prepare, and analyze different data sets for extracting meaning from data [
To represent data science projects, the ontology will try to answer the following four competency questions (that are defined in the Ontology Definition 101 methodology as ‘One of the ways to determine the scope of the ontology is to sketch a list of questions that a knowledge base based on the ontology should be able to answer’):
2. What data science application domains have the data science projects been completed in? 3. What type of data science project attributes represent the resources that are used in data science projects? 4. What attributes represent a completed/ongoing data science project? Based on the competency question relating to the type of data science project attributes as well as the one relating to the application domains of data science resources, further competency questions such as ‘Given a data science application domain (representing the application domains of data science resources) what machine learning algorithms can be used(representing the resources that are produced within the data science domain)?’ can be inferred. Basing the ontology on data science project attribute types (e.g., algorithms used) and data science project attributes (e.g., Decision Trees) separately allows for more domain-specific knowledge to be inferred from any knowledge graph that utilizes this ontology as a schema.
In the authors’ previous work [
In the domain science data science, several ontologies have been proposed and some of them are considered in this section.
There is an ontology [
To compare with our ontology used in the previous work [
Thus, the question is about the granularity of the ontology. We might assume that higher granularity of ontology might give additional opportunities in knowledge amalgamation; however, as was already mentioned, the level of detail available in scientific works or project documentation does not always allow us to go to that level of detail. Also, the higher the level of detail, the more often reconsidering an ontology itself might be needed. Thus, the open question is what level of detail might be useful in amalgamating knowledge in the data science domain and what frameworks or initiatives might be used to maintain the ontology used to refer to the work in the respective domain.
As shown above, there are many applications of ontologies within the domain of data
science and the proposed ontologies have different purposes. To our knowledge, there have
not yet been ontologies proposed for the reuse of knowledge within the data science
domain. In this paper, we, based on our experience with scientific work in data analytics,
propose an ontology that is based on the skills and knowledge units defined within the EDSF
(EDISON Data Science Framework) Competency Framework for Data Science (CF-DS) [
Within the previous sections, the following terms have been recurrent:
1. Data Science Project – Projects that that aim to clean, prepare, and analyze different
data sets for extracting meaning from data
2. Data Science Project Attribute Type – Representation of the type of attributes that
would represent a data science project resource such as the data mining algorithm
used within the project [10]. When defining these attribute types, it is important to
consider those that have already been recognized as well as newer technologies
within the domain of data science. These two types of data science attributes can be
represented as standard and custom data science attribute types.
3. Data Science Project Attribute – An attribute that represents a single or multiple data
science project such as decision trees [11] which would be of the attribute type data
mining algorithms used.
4. Data Science Application Domain – This is the domain in which data science is being
applied such as social housing, shipping, and automotive retail[
These four classes (and two subclasses) will be sufficient for accumulating data science
knowledge based on data science project attributes and the class properties shown in Fig.
2.
OWL and RDFS are used for the definition of classes and class properties given that this
ontology is meant to be used for a knowledge graph, created using RDF, as was the case in
the authors' previous work [
Before the utility of the ontology can be demonstrated, the standard data science project attribute types need to be defined in such a way that the already recognized aspects of the data science domain must be represented; this is done in the next subsection.
The data science project attribute types definition process is done based on the Data Science
Competency Framework (CF-DS) Release Two [
Fig. 3 is a model that is meant to give the reader an understanding of elements within the CF-DS and to represent the knowledge and skills defined in the CF-DS. The CF-DS utilizes keywords to represent the skills and knowledge with keywords followed by a number; examples of the values are also shown in the diagram.
The base knowledge body does not directly address the research results in data science; rather, it reflects the knowledge that is needed to achieve them. Therefore, the attribute types of data science projects can be defined only indirectly through the concepts available in the chosen body of knowledge. In this work, we consider only the relationships between knowledge and skill (excluding those relating to the analytics languages, tools, platforms, and Big Data infrastructure) based on the preestablished notation that knowledge is a prerequisite of skillful action [12].
Unlike the previous work that limited the scope of the ontology to data analytics, in this work, the scope represented in the ontology is expanded upon by defining data science project attribute types in such a way that they map the skills defined in the CF-DF to the knowledge units defined in the CF-DF in a way that each knowledge area has at least one associated skill ensuring that all knowledge required for data science related skills are represented in the ontology. This would allow for the representation of the data science domain as a whole and the encapsulation of knowledge relating to all established aspects of a data science project.
To ensure that the defined data science project attribute types are accurate and are traceable to CF-DS knowledge units/topics and the skills, the data science project attributes are defined in an X_Y_Z format (Fig. 4), where X represents (Data Science)Domain Specific Key Words present found in both the Knowledge topics/units required and the Skills(such as Data Mining, Supervised Machine Learning, and Predictive Analytics), Y represents the (Data Science) Domain Specific Resources (such as Techniques, Tools, and Algorithms), and Z represents Actions Verbs(such as used, implemented, and developed) that are defined based on the action words(such a use, implement, and develop) that are mentioned in the CF-DS skills. This provides a formal meta-structure for data science project attribute types that were missing in the previously defined data analytics project attributes, and it enables the systemization of the data science attribute definition process. Based on the defined Data Science Project Attribute Type Structure the EDSF CF-DS Skills, and EDSF CF-DS Knowledge unit/topics were manually parsed to recognize the relevant Keywords and Action Verbs. An example of how this was accomplished can be seen in Fig. 5, which shows how three data science project attributes were defined to map the knowledge units KDSDA01, KDSDA02, and KDSDDA03 to the skill SDSDA01.
Fig. 5 shows that the defined DS Project Attribute Types have additional text within brackets; this text is introduced to provide additional specificity for the domain-specific keywords and was defined based on the Skills or Knowledge Areas/Topics.
Utilizing this Data Science (DS) Project Attribute Types Definition Process, 77 Data Science Attribute Types were defined, mapping all knowledge areas to at least one skill, thus representing all currently established aspects of data science projects and providing the possibility to define more data science attributes for capturing data science research results than were discovered by bottom-up approach in our previous work. All the skills themselves have at least one corresponding DS Project Attribute Type, with the only exception being SDSENG12 – Use of Recommender or Ranking system, but as this skill relates to the Recommender and Ranking Systems, the authors took the liberty to consider these systems as information systems which allowed to map this skill to KDSENG10 – Information Systems, collaborative systems by defining two DS Project Attribute Types: (i) (Information)Recomender_System_Used and (ii) (Information)Ranking_System_Used
The table containing a list of all identified unique keywords, resources, and action words demonstrating the relationships between the EDSF CF-DS Skills, Knowledge, and the defined data science project attributes is available in a GitHub repository [16]. It should be noted that, in some cases, the data science project attribute types have missing action verbs and/or resources due to limited text in either the knowledge unit/topic (e.g., KDSDA13 – Optimisation) or the skills (e.g., KDSDA14 – Optimisation). In some cases, the authors applied placeholders Y and Z, which were used to maximize the number of aspects of the data science domain represented by the data science project attributes: 16 (20%) of the DS project attributes are missing a Domain Specific Resource, and 3 of them are also missing a Domain Specific Action Verb. These missing values simply reduce the specificity of the defined DS Project Attribute Types while still providing a (limited) representation of this aspect of the data science project.
When comparing the newly defined data science project attribute types with the
previously defined data analytics project attribute types [
Given the original intention of the ontology of being used as a schema for a knowledge graph, the UML schema (Fig. 6) was defined for a knowledge graph using OWL classes and subclasses as well as object properties.
The UML class diagram shows the classes and subclasses defined for this ontology, here the class(rdf: ID=”DSProjectAttributeType”) was defined for the DS (Data Science) Project representing the resources available in the data science domain. This will organize the knowledge gained within a previously completed data science project and will relate to instances of the class(rdf: ID=”DSProjectAttribute”) through an ‘isAnAttributeOfType’ relationship, for instance: - ‘DSProject ABC’(instance of <owl:Class rdf:ID="DSProject">)
hasDSProjectAttrbiuteValue(property of <owl:Class rdf:ID=" DSProject ">) ‘Decision Trees’ (instance of <owl:Class rdf:ID="DSProjectAttributeValue">) - ‘Decision Trees’ (instance of <owl:Class rdf:ID="DSProjectAttribute">) isAnAttributeOfType(property of <owl:Class rdf:ID="DSProjectAttribute">) ‘(Supervised)MachineLearning_Technology/Algorithm/Tool_Used’(instance of <owl:Class rdf:ID="DSProjectAttribute">) - ‘DSProject ABC’(instance of <owl:Class rdf:ID="DSProject">)
hasDSProjectAttributeOfType(property of <owl:Class rdf:ID=" DSProject">) ‘(Supervised)MachineLearning_Technology/Algorithm/Tool_Used’(instance of <owl:Class rdf:ID="DSProjectAttributeType">)
The three triples mentioned above represent the relationship between the data science project, data science project attribute type, and data science attribute in the form of DSProject ABC, (Supervised) MachineLearning_Technology/Algorithm/Tool_Used’, Decision Tree. Similarly, other data science project attributes can represent project features such as Natural Language Processing_Method_Used, Data Preparation/Data Preprocessing_Method_Used, Performance/Accuracy_Metric_Used, etc. The additional RDF triples mentioned below relate the data science project to a data science application domain.
- ‘DSProject ABC’(instance of <owl:Class rdf:ID="DSProject">)
relatesToDSProjectDomain(property of <owl:Class rdf:ID=" DSProject">) ‘Health Care’ (instance of <owl:Class rdf:ID="DSProjectDomain">)
This new RDF triple, combined with the reasoning capabilities of knowledge graphs realized through rule-based reasoning, allows for inferring what resources can be used within a specific data science application domain. The rule-based inference is realized in this ontology through the use of a SWRL [13] rule:
relatesToDSProjectDomain(?project, ?domain) -> canBeUsedInDSApplicationDomain(?tool, ?domain)
To demonstrate the use of this ontology from a practical standpoint, we implemented a knowledge graph that will store, and present knowledge acquired from a single project within the data science domain [14] that introduces SatelliteBench, a framework for satellite image extraction and vector embeddings generation and it’s utility in creating predictive models for poverty, education, and dengue prediction. The information provided in this project is presented using Protégé [15] (Fig. 6).
The following Data Science Attribute instances were defined with the corresponding Data Science Project Attribute Types(with one standard attribute type, with the others being custom): 1. Dengue Prediction Model – PredictiveAnalytics_Method_Used(Standard Type) 2. Vector Embeddings Development – DataFusion_Technique_Used 3. Access to Education Model – EducationAccessibility_PredictiveModel_Used 4. Multi model Fusion Pipeline – MultiModel_Fusionpipeline_Used 5. Meta data Extraction – DataCollection_Technique_Used 6. Poverty Assessment Model – PovertyIndex_Assesment_Used 7. Satellite Image Extraction – ImageProccesing_Technique_Used 8. Image Extraction Technique – ImageProccesing_Technique_Used
The fact that all these resources can be utilized in the domain of Public Health can also be inferred using the SWRL rule that was defined relating the DSProjectDomain and the DSProjectAttribute instances. Most new instances required the definition of Custom data science project attribute types.
It should be mentioned that these instances were defined using ChatGPT 4o (accessed 15th of August, 2024) with a query that outlined the structure of the ontology (including definitions of object properties as given in Section 2.3), provided instances of Standard Project Attributes(mentioned in Section 2.4) , and an attachment of the pdf version of the research article[14] combined with a request to present the result as instances in RDF/XML format.
This demonstrates the possibility of utilizing this ontology with LLMs or other advanced text parsing technologies to automate the accumulation and presentation of knowledge gained from completed or ongoing data science projects. The reliability of LLMs for the production of the instances for the defined data science project ontology requires further research, but this work demonstrates how domain-specific attribute types defined in a format that mimics natural language allow formulated queries to be used by LLMs easily.
The flexibility of the ontology enabled through the class representing data science project attribute types allows the user to define data resources they are interested in and to disregard the instances they are not interested in (demonstrated by the fact that of the 77 standard attribute types defined, only one ‘PredictiveAnalytics_Method_Used’ was needed to represent the project, whereas 7 additional custom attribute types needed to be defined). This flexibility also allows automation of the knowledge accumulation process based on the information that is available with the project reports (in this case being a research article published as a result of this data science project), and also to represent resources that are not widely used (represented by the custom data science project attributes such as DataFusion_Method_Used, PovertyIndex_Assessment_Used, etc. (Fig. 5)) or have recently been introduced by a research project.
This work outlines the definition of an ontology that can be used to facilitate the reuse of
knowledge acquired through the completion of data science projects. This ontology is based
on data science project attributes (a concept of project attribute was introduced for data
analytics in an author’s previous work [
The utility of the data science project ontology was demonstrated by representing the knowledge acquired from a single data science project [14], presenting knowledge such as the Dengue Prediction Model as a Predictive Method Used and the Multimodal Fusion Pipeline as a Data Fusion Technique Used. Through the use of a SWRL rule that relates the defined data science project attributes to a data science application domain, it is possible to infer the application domain of these data science resources which in this case was public health. Also demonstrated is how using this ontology (with instances of the types of project attributes within the domain of data science) in tandem with advanced text parsing technologies such as LLMs, it is possible to automate the knowledge accumulation process through the use of project reports (which in the case of the discussed project [14] was a single research article).
This paper demonstrates how shifting the goal of the ontology for domain representation to the representation of projects within a domain can simplify the ontology itself as well as reduce the complexity related to the maintenance of the ontology by making it more instance-centric than class-centric.
Future works can demonstrate the further utilization of the ontology-defined data science project attributes for knowledge representation and automation of the knowledge accumulation process. [9] G., Henriques, D., Stacey, "An Ontology-Based Framework for Analysis Recommendation," 2014 IEEE International Conference on Bioinformatics and Bioengineering, Boca Raton, FL, USA, 2014, pp. 277-282, doi: 10.1109/BIBE.2014.70. [10] F., Provost, T., Fawcett, 2013. Data science and its relationship to big data and datadriven decision making. Big data, 1(1), pp.51-59. [11] N., Ye, 2013. Data mining: theories, algorithms, and examples. CRC press. [12] Stanley, Jason. “Know how.” OUP Oxford, 2011. [13] I., Horrocks, P.F., Patel-Schneider, H., Boley, S., Tabet, B., Grosof, M., Dean, 2004. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission, 21(79), pp.1-31. [14] D., Moukheiber, D., Restrepo, S.A., Cajas, et al., A multimodal framework for extraction and fusion of satellite images and public health data. Sci Data 11, 634 (2024). doi: 10.1038/s41597-024-03366-1. [15] M.A., Musen, The Protégé project: A look back and a look forward. AI Matters.
Association of Computing Machinery Specific Interest Group in Artificial Intelligence, 1(4), June 2015. doi: 10.1145/2557001.25757003. [16] A., Kumarasinghe, Ontology for Data Science Research Results Reuse (Version 1) [Computer software]., (2024). https://github.com/ArithaRTU/Ontology-for-DataScience-Research-Results-Reuse.git