Creating Core Ontology for Social Sciences Empirical Data

Integration

Dmitry Kudryavtsev

, Tatiana Gavrilova

and Alena Begler

Graduate School of Management St. Petersburg University, Saint-Petersburg, Russia

Keywords: Ontology Reuse, Empirical Data Integration, Empirical Research Datasets, Knowledge Engineering.

Abstract: There exist several dozens of metadata standards for empirical research data, making it difficult for users to

choose and apply such standards. Consequently, the integration of datasets from similar empirical studies for

further knowledge acquisition is highly constrained. To resolve this problem, an ontology for social science

research data integration (Empirion-core) has been developed. The ontology reuses existing data integration

schemas: DDI-RDF Discovery Vocabulary, Generic Statistical Information Model, Core Ontology for

Scientific Research Activities, Data Catalog Vocabulary, and DCMI Metadata Terms. It consists of five

subontologies that provide concepts for empirical datasets description: Information resource ontology,

Research activity ontology, Research coverage ontology, Measurement ontology, and Sampling ontology.

1 INTRODUCTION

The volume and number of datasets generated by

empirical research are increasingly becoming a

challenge for professionals, who use them to extract

knowledge and carry out comparative studies. More

specifically, the amount of such data is increasing,

while their accessibility and reuse potential are

declining (Vines et al., 2014). Over two thousand

repositories for open research data exist (according to

the re3data.org web portal). Each of these repositories

can store up to several hundred thousands open

datasets. From this perspective, therefore, research

data represents one of major open data categories

alongside the government data (Vetrò et al., 2016;

Mouromtsev, Jens et al., 2015).

Unfortunately, majority of these datasets cannot

be reused due to lack of data integration

opportunities. One of the issues is lump of metadata

schemas that vary among repositories and research

groups. The inconsistency in data description leads to

data fragmentation and inhibits knowledge

acquisition, as it implies working with separate

datasets, whereas certain tasks can be handled more

efficiently using multiple integrated datasets.

Increasing amount and heterogeneity of data results

https://orcid.org/0000-0002-1798-5809

https://orcid.org/0000-0003-1466-8100

https://orcid.org/0000-0003-4375-1106

in the growing need for new data integration

solutions, as well as general principles of

organization, storing and distribution (Atkinson,

Gesing, Montagnat, & Taylor, 2017; Wilkinson,

2016). This problem is particularly important for

social science data as it is relatively cheap and

therefore less hardly managed.

The advancement of semantic technologies,

including the ones designed for data integration, can

be regarded as a response to this challenge

(Kudryavtsev & Gavrilova, 2020; Lenzerini, 2011; Li

et al., 2013). Ontologies have already been used for

research data description. Existing application can be

divided into three types:

 metadata schemas associated with a specific type

of data or repository – for instance, DCAT, a

recommendation of the World Wide Web

Consortium for data catalogue integration

(Archer, 2014), or DataCite Metadata Schema

(Starr & Gastl, 2011) built for citing data through

the DataCite web portal;

 domain-specific ontologies;

 ontologies of research activity such as ontologies

of scientific experiments EXPO (Soldatova &

King, 2006), ontology of research activity

(Zagorulko & Zagorulko, 2015), The SWRC

Kudryavtsev, D., Gavrilova, T. and Begler, A.

Creating Core Ontology for Social Sciences Empirical Data Integration.

DOI: 10.5220/0010146502670274

In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD, pages 267-274

ISBN: 978-989-758-474-9

267

(Semantic Web Research Community) Ontology

(Sure, Bloehdorn, Haase, Hartmann, & Oberle,

2005), (KA)2 ontology (Benjamins & Fensel,

1998), a schema of the Integrated Scientific

Information Space of the Russian Academy of

Sciences (Bezdushny et al., 2000).

Such ontologies (especially metadata schemas) do

create an infrastructure for datasets storing and

description, however, they are not fully support data

reuse as they are lacking variable-level refinement.

The approach under discussion is aimed to

overcome this limitation with a creation of an

ontology for empirical datasets description

(Empirion-core). The ontology is defined as “formal,

explicit specification of a shared conceptualization”

(Gruber, 1993; Studer, Benjamins, & Fensel, 1998)

that can serve as a knowledge base schema (Villazon-

Terrazas et al., 2017). The proposed ontology is a

core ontology in a sense that it includes a set of

concepts that are both necessary for social science

empirical data description and are not domain-

specific (Ruy et al., 2017). The development of

ontology is based on the scenario of knowledge reuse

(Suarez-Figueroa, Gómez-Pérez, & Fernández-

López, 2012) that includes the following stages: 1)

specification of ontology requirements; 2) analysis of

ontology reuse resources; 3) conceptualization; 4)

formalization; 5) software implementation. This

paper presents the first three stages (one by section).

2 REQUIREMENTS TO

ONTOLOGY FOR EMPIRICAL

RESEARCH DATA

INTEGRATION

While existing schemas describe dataset as a whole,

some dataset-related tasks require understanding of

the variables inside the dataset. For example,

researcher or analyst might be interested in datasets,

where (examples for a managerial research provided

in brackets):

 concept or phenomenon or construct X is

examined (e.g. a company’s human capital);

 the relationship between concept X and concept Y

is examined (e.g. the relationship between a

company’s human capital and its performance);

 variable A is used to evaluate concept X (e.g.

variable ‘the proportion of staff members with

higher education’ used to evaluate the concept

‘company’s human capital’);

 a specific data collection method was used (e.g. a

survey);

 data was collected in a specific region or a region

with specific characteristics (e.g. in a developing

country / emerging market);

 samples included members of the population with

specific characteristics (e.g. with disabilities);

 variable Z was considered as an influencing factor

(e.g. company size);

 the study was aimed at resolving a problem X (e.g.

investment decision-making);

 data was collected in the last 5 years;

 specific equipment or technologies were used

during data collection (e.g. eye movement

tracking).

To answer such questions ontology should contain

terms that allow to address exact variables in a

dataset. This is important because different datasets

might contain data for similar variables that can be

integrated. For example, different managerial

research of the concept “customer loyalty” may use

different variables (or metrics) such as Net Promoter

Score (NPS) or Repurchase Ratio. The principle of

variable-based integration is shown at Fig. 1.

Figure 1: A principle of datasets integration.

The backbone of the ontology development was

three main assumptions. The first, the ontology

should be, above all, suitable for the description of

research data in social sciences (see subject areas in

All Science Journal Classification Codes, ASJC),

including cognitive research, management, and

economics. The second, it should be extensible as any

research group can create a conceptual model for

specific variables. The third, it should itself extend

existing research metadata infrastructure as the field

of research publishing (including data publishing) is

covered by the variety of schemes and vocabularies.

KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development

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3 ANALYSIS OF RESOURCES

FOR REUSE

To answer discussed questions, an ontology should

include the terms:

 dataset (data array);

 concept (phenomenon, construct);

 variable;

 data collection method;

 sample;

 member of the population;

 place of the study / data collection;

 time of the study / data collection;

 data collection equipment / technologies.

Thus, the ontology should incorporate a data

description format for empirical research data,

empirical research ontology, ontology of research

activity, etc. Aside from this, it should also be able to

integrate domain-specific extensions.

The given terms are partially covered by existing

ontology resources, suitable for reuse. To construct

the requisite ontology, the following ontologies were

used as a basis:

 DDI-RDF Discovery Vocabulary (disco) (Bosch,

Gregory, Cyganiak, & Wackerow, 2013);

 Generic Statistical Information Model (GSIM)

(UNECE, 2013);

 Core Ontology for Scientific Research Activities

(COSRA) (Campos, Reginato, & Almeida, 2019);

 Data Catalog Vocabulary (DCAT) (Archer,

2014);

 DCMI Metadata Terms (“DCMI Metadata

Terms,” n.d.).

DDI-RDF Discovery Vocabulary (disco) (Bosch,

Gregory, Cyganiak, & Wackerow, 2013), for

describing statistics data and metadata, focused

primarily on questionnaires. Data Documentation

Initiative (DDI) were developed for the social,

behavioral, and economic sciences data management

(Bosch, Gregory, Cyganiak, & Wackerow, 2013).

This standard deals with social science data, data

covering human activity, and other data based on

observational methods measuring real-life

phenomena. DDI formally describes the main

concepts and common practices in this domain and

puts stress on both microdata and aggregated data. It

concentrates on microdata – data about the attributes

and properties of population units. DDI offers the

reuse of metadata of existing studies (e.g. questions,

variables) for designing other studies, an important

ability for repeated surveys and for comparison

purposes (Vardigan et al, 2008). The DDI-RDF

Discovery Vocabulary represents a research dataset

organization as a set of variables. It also provides

connection of the dataset with related research entities

such as instruments (for example, questionnaire) and

concepts under investigation.

Generic Statistical Information Model (GSIM)

(UNECE, 2013), is dedicated to statistics data

description. It proposes a general framework of

internationally agreed definitions, attributes and

relationships that illustrate the fragments information

that are used in official statistics or other open

information objects. GSIM may be supposed as a

common language to describe information as a

background of the common statistical production

procedures from the identification of user

requirements to the dissemination of the statistical

results and outcomes (GSIM Brochure; UNECE,

2013). We reused some of the elements from Concept

Group, which defines the meaning of information to

provide understanding of what the data are measuring

(Clickable GSIM v1.2) and from Structure Group,

which describes the information structure within the

statistical process (Clickable GSIM v1.2).

The main value of the GSIM for the research

datasets representation is a three-level approach to the

variable understanding: (1) as a something that can be

measured; (2) as its representation as a measurement

of a particular kind; and (3) as a concrete measure.

The third can be understood as data values in a

dataset, the second as variables with the information

needed to interpret them, and the first as all the

possible variable that may occur in datasets.

Core Ontology for Scientific Research

Activities (COSRA) (Campos, Reginato, &

Almeida, 2019), a domain-independent ontology for

describing research processes related to data

collection. The ontology provides classes for the

detailed description of the activities necessary to

interpret a dataset. Its scope is similar to the proposed

ontology with two important differences. At first, the

proposed ontology focuses on social science datasets

and consider corresponding standards (DDI, GSIM),

while COSRA is more generic and is deductively

created from upper ontology (Unified Foundational

Ontology, UFO). A strong dependence on UFO is not

great for Social Science, where a majority of domain

ontologies are Basic Formal Ontology (BFO)

compliant. At second, the proposed ontology is

dedicated to description of variables in the empirical

research datasets and goes in more detail at a dataset

level while in less detail at a context level. However,

the context related COSRA classes are relevant to the

social science datasets description. Additionally, the

proposed ontology reuses some elements of

COSRA’s modular structure.

Creating Core Ontology for Social Sciences Empirical Data Integration

269

Data Catalog Vocabulary (DCAT) (Archer,

2014) – a recommendation of the World Wide Web

Consortium aimed at increasing the dataset

interoperability. The majority of research related

metadata vocabularies (including the mentioned

DDI-RDF Discovery Vocabulary) is aligned with

DCAT. It describes the dataset as a part of a catalog

and provides description of dataset’s physical

properties as well as time and space coverage.

DCMI Metadata Terms (“DCMI Metadata

Terms,” n.d.) – Dublin Core, a metadata collection

that provides a minimal necessary information for

digital asset description and widely used by academic

literature publishers and research data repositories. It

contains such concepts as author and creation date

and thus necessary for reuse in the proposed model.

Despite all the mentioned models describe

datasets in detail they provide only a dataset-level

description. Namely, majority of them allow to say

something about the dataset, but not about variables

it contains. The proposed ontology aimed to extend

the mentioned vocabularies to allow variable-level

integration. Thus, it extends the mentioned

vocabularies with the additional level of details.

4 CONCEPTUAL ONTOLOGY

SCHEMA FOR EMPIRICAL

RESEARCH DATA

(EMPIRION-CORE)

The architecture of the Empirion-core ontology

resembles the Core Ontology for Scientific Research

Activities (COSRA), which includes Research

activity ontology, Sampling ontology, Preparation

ontology and Measurement ontology. But since the

Empirion-core ontology is targeted at Social Science

data we mostly reused concepts from DDI-RDF

Discovery Vocabulary (DISCO) and Generic

Statistical Information Model (GSIM). The

Empirion-core ontology consists of 5 subontologies:

1. Information resource ontology that considers and

describes empirical research dataset as a kind of

information resource. This ontology references

disco:Dataset and dcat:Resource classes and

provides their connections with different types of

Metadata using corresponding classes. This is

important as research dataset is often

accompanied be the metadata in the separate files.

2. Research activity ontology with a focus on types

of research activities, agents (or actors) of

research activity and methods of data collection.

This ontology references classes

cosra:ResearchActivity and disco:Instrument to

reflect how dataset was collected. These classes

relate to the Research coverage ontology classes.

3. Research coverage ontology provides a context of

the research activities and thus locates dataset in

space (Period of Time class) and time (Location

class) and connects it with the information about

Object and Subject of research.

4. Measurement ontology is especially important in

the context of variable-based data integration. The

central concept of this ontology is gsim:Variable,

it also includes the associated concepts such as

measurement unit, value domain etc. The goal of

this ontology is to provide meaningful variables

description. For example, the same measurement

may be presented using different measurement

units. To allow the data integration for such cases

the ontology references classes

gsim:MeasurementUnit that connects variable

with its unit of measure and gsim:ValueDomain

that allows to describe range of values.

5. Sampling ontology describes units of research

such as sample and target population. This

ontology extends Research coverage ontology

with the Target population and Sample classes.

The latter characterizes the former and relates to

Measurement ontology.

The more detailed reuse and mapping to existing

ontologies is represented in Table 1. All the

subontologies are connected through relations

between their classes (see Figure 3). The key class is

Data Set that has representation in particular files and

is described by metadata. This physical

representation of dataset (described by Information

resource ontology) relates to Research coverage

ontology concepts as dataset

has geographical and

temporal coverage as well as subject and object

coverage. The research object should include target

population that is characterized by the research

sample (the concepts of Sampling ontology). The

dataset is a result of a research activity that is

described by the corresponding ontology. The

research activity is performed by an agent and uses

some methods of data collection. Finally, to interpret

the information in the dataset it should be connected

with Measurement ontology: the dataset contains

variables that measures concepts.

5 CONCLUSIONS

The paper proposes an ontology for integration of the

empirical datasets obtained in various research

KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development

270

studies. Similar problems are encountered in a

number of areas of the social sciences, and the

approach discussed in the paper will support the

integration and merging of such datasets in order to

extract new knowledge from existing data. The

proposed approach is based on the ontology

engineering paradigm and principles.

The proposed Empirion-core ontology allows to

describe datasets obtained from empirical research.

Empirion-core combines and merges existing

ontological and non-ontological resources, and

supplements them with the new necessary concepts

(terms) allowing to display metadata schemes used in

existing data sets of empirical research.

Figure 2: Composition of an ontology system for empirical research data (Empirion-core), upper level.

Figure 3: Relationships in Empirion-core ontology.

Reused concepts specified in the Table 1 for the sake of readability.

Creating Core Ontology for Social Sciences Empirical Data Integration

271

Table 1: Reuse and mapping to existing ontologies and non-ontological resources.

Empirion-core elements DDI-RDF Discovery

Vocabulary

(DISCO) (Bosch,

Gregory, Cyganiak,

& Wackerow, 2013)

Generic Statistical

Information Model

(GSIM) (UNECE,

2013)

Core Ontology for

Scientific Research

Activities

(COSRA)

(Campos, Reginato,

& Almeida, 2019)

Other ontologies

and non-ontological

resources

Information resource

ontolo

Data Set Data Set

Logical Data Set Logical Data Set

Data file Data file

Metadata

Embedded metadata (Duval, Hodgins,

Sutton, & Weibel,

2002)

Associated metadata

Thir

-Party metadata

Research activity

ontolo

Research activit

Research Activit

Research design

Data collection Study Measurement

Data analysis

Method of Data collection Instrument

ent foaf: A

ent

Research coverage

ontolo

has subject coverage dc: Subject

dct: has subject

covera

has ob

ect of research Researchable Entit

Concept Universe Concept skos:Concept

has geographical

coverage

Geographic point dc: Spatial Coverage

dcat:

Spatial/geographical

covera

has temporal coverage dc: has temporal

coverage

dcat: temporal

covera

Measurement ontolo

Variable Variable Variable Measure

Scale

Represented variable Represented

variable

Value domain Value domain Scale Value

Measurement unit Measurement unit Measure Unit

Instance variable Instance variable Measured Value

Sam

lin

ontolo

Target population Universe Universe Sampled Entit

Sample Population Sample

ACKNOWLEDGEMENTS

The reported study was funded by RFBR, project

number 20-07-00854.

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