The euBusinessGraph ontology: A lightweight ontology for harmonizing basic company information

2.1.Organizational structure

The W3C Organization Ontology (ORG) [29] is a W3C recommendation since 2014. It aims to capture information about organizations (companies and institutions), including governmental organizations. It primarily captures organizational structure (e.g., sub-organizations and classification), reporting structure (e.g., roles and posts), location information (e.g., sites and addresses), and organizational history (e.g., merger and renaming). ORG is highly generic and designed as a core ontology, capturing general concepts and encouraging extensions for specific domains. It has been reused by other ontologies such as PPROC [25] in the procurement domain.

The e-Government Core Vocabularies [32] were developed in order to provide a minimum level of semantic interoperability for e-Government systems as part of the SEMIC (Joinup) community1414 and the ISA program1515 of the European Commission. They include basic concepts about legal entities, locations, persons, public services, public organizations, and public services. The Core Public Organization Vocabulary (CPOV) and the Core Business Vocabulary (CBV) are the most relevant vocabularies in our context. After initial development by EC SEMIC and ISA2, they were transferred to W3C for standardization. The CBV was revised and formally adopted by W3C in 2013 as the Registered Organization Vocabulary (RegOrg).1616 RegOrg is an extension of ORG for describing organizations that have gained legal entity status through a formal registration process, typically in a national or regional register.

The Popolo Project defines data interchange formats and data models in the context of the Open Government initiative.1717 A set of concepts and relations are provided for capturing persons and organizations and the relationships between them (e.g., membership properties). A vocabulary for describing organizations is also provided. This vocabulary reuses terms from the ORG ontology and adds some new ones (e.g., other name, area, and contact detail).

The Application Profile of the Organization Ontology (ORG-AP-OP) was developed by the Publications Office of the European Union and supports its Whoiswho service.1818 It provides actual contact information for staff working at the European Institutions. It is concerned with people and the roles they play in the actual institutions. Similarly, in 2015, the ISA Programme of the EC initiated the development the Core Public Service Vocabulary and its Application Profile (CPSV-AP) [42]. However, it defines a number of terms closely related to CPOV, such as the administrative level, the type of organization, and its home page.

The Schema.org initiative [17] is spearheaded by the big four search engines, Google, Yahoo, Bing and Yandex, and is a collaborative effort to create, maintain, and promote schemas for structured data on the Internet. It is highly reusable since it makes few ontological commitments in order to cater to a truly global audience of millions of Web sites. Schema.org considers schemas as a set of types arranged in a hierarchy and associated with a set of properties. The core vocabulary is currently composed of 614 types and 902 properties. The “Organization” concept is among one of the commonly used types (among with, e.g., person, product, event) and models businesses (e.g., type, contact, etc.) and marketing aspects (e.g., logo, social profile, etc.).

2.2.Financial and economic

The Financial Industry Business Ontology (FIBO) [7] is a joint effort of the Enterprise Data Management Council (EDMC) and the Object Management Group (OMG), aiming to go beyond a mere dictionary and capture the semantics of the business domain from a financial perspective. FIBO formalizes entities such as companies, directors, ownership and control relations, business registers, monetary amounts, debts, obligations, contracts, and financial instruments. It is composed of a large number of smaller ontologies, with a modular perspective, each of which models a specific financial area [24]. The result is a large and very complex set of ontologies for the financial industry consisting of 11 core domains and 49 modules made available in more than 400 ontology files.

There are a number of classification vocabularies to specify the kind of economic activity such as International Standard Industrial Classification of All Economic Activities (ISIC) [11], which is a United Nations industry classification system, and European Commission’s NACE [14], which is preferred in the context of European interoperability.

The Entity Legal Forms Code List1919 (ISO 20275) by GLEIF provides a world-wide list for types of business entities including a translation to English. Wikipedia’s Legal Entity Types2020 also provides approximate equivalents in the company law of English-speaking countries.

2.3.Company identification and location

The Global Legal Entity Identifier Foundation (GLEI) established a registration structure to issue Legal Entity Identifiers (LEI) to legal entities participating in financial transactions. The LEI structure is standardized as ISO 17442 [19]. LEI includes two code lists that are relevant in the context of basic company information, that is registration authorities list including 651 national official registers with their descriptions such as authority code, jurisdiction, and website; and, entity legal form code resolving variant names for each valid legal form within a jurisdiction to a single code per legal form.

The Business Registers Interconnection System (BRIS) interconnects business registers across Europe and provides a single (though limited) company search form.2121 The list of legal forms, list of national registers, and the pan-European company identifier (which is formed by register and company identifiers) are relevant for capturing basic company information.

With respect to capturing various forms of locations for companies, several initiatives are relevant. Eurostat has established a unified hierarchy of regions across the EU, EFTA and Candidate Countries. It consists of a nomenclature of Territorial Units for Statistics (NUTS) [15] and Local Administrative Units (LAU).2222 NUTS and LAU are important geographic resources since a significant amount of open data is available that can support address data mapping (e.g., from postal code to NUTS) and use cases (e.g., hierarchical facets, distance calculations, spatial inclusion); and, NUTS and LAU provide a uniform hierarchy, whereas the administrative hierarchy varies greatly in different countries.

The EU ISA2 Location Core Vocabulary [13] aims at describing any place in terms of its name, address or geometry through a minimum set of classes and properties. It integrates with the Business (i.e., RegOrg) and Person Core Vocabularies of ISA2.

GeoVocab.org2323 provides vocabularies for geospatial modelling. This includes vocabularies NeoGeo Geometry Ontology for describing geographical regions and NeoGeo Spatial Ontology for describing topological relations between features.

Finally, GeoNames2424 provides a free geographical database covering all countries and containing over eleven million place names. It includes data elements such as administrative regions and settlements, and physical places.

2.4.Other relevant initiatives

In addition to well known initiatives such as FOAF,2525 Dublin Core2626 and DBPedia,2727 there are other ontologies, vocabularies and initiatives that are relevant in the context of modelling basic company information, including:

3.1.Scope and requirements

After an analysis of the data provided by the different providers and the information available therein, we identified the major concerns that the ontology should address. Figure 1 provides an overview of the different types of information found during the data analysis, organized according to the type of entity being described (Registered Organization and Officer). In addition, the ontology needed to cover the description of dataset offerings by individual data providers (Dataset) and the description of identifier systems used to uniquely identify companies (Identifier System).

Fig. 1.

Overview of the scope of the euBusinessGraph ontology.

We identified target domains for our ontology, which primarily map to the business information sector, the marketing and sales sector, and the business publishing industry interested in new innovative data-driven products and services. Users working with data in these domains will benefit from a common representation that covers the types of information contributed by the different data providers. This common representation will also ease the task of data providers and aggregators who need to validate, transform and clean the data by providing a single ontology to target. The fact that there is a single ontology that provides a common representation will also benefit service developers who need to reference company information to implement their services. To this end, the ontology has to capture the properties of the different identifiers that can be used to link the different entities being represented, providing machine readable descriptions for the identifier systems in use, including support for describing rules for validation and normalization of company and company-related identifiers.

Taking into account the needs of the intended users of the ontology and after the analysis of the data provided, we elicited the ontology requirements following the Neon methodology [39] for requirements specification, and considering two kinds of requirements.

Firstly, we consider functional requirements dealing with the scope of data and groups of competency questions that the data should be able to answer. The functional requirements include:

Fig. 2.

Phases of the euBusinessGraph ontology development process.

Examples of groups of competency questions that the data should be able to answer include:

Secondly, we consider non-functional requirements dealing with the general requirements or aspects that the ontology should fulfill. Foremost amongst these is reuse:3636

3.2.Ontology development

The ontology development process was guided by the need to harmonize and integrate datasets with different sets of attributes, different representations for the same entity and in some cases close but not entirely similar semantics. Figure 2 depicts the four phases of the ontology development process in which we (a) gathered data from all company data providers that include natural language descriptions and example instances of each data attribute they provided, (b) analyzed attribute descriptions, refining them with additional notes describing their scope and using this information to group similar attributes, (c) analyzed identifiers and their identifier systems to produce machine readable descriptions of their properties, and (d) carried out manual reconciliation with the aim to reuse existing vocabularies.

There are differences in the types of information available from source to source (e.g., one dataset contains only official information from the national registers, while another integrates contact information parsed from company websites), differences in the way the same bit of information is represented by each provider (e.g., addresses as strings or as complex objects with separate attributes for street number, name and municipality) and differences in semantics for closely related concepts that may appear to be the same (e.g., information about officerships and their durations that contain references to possibly ambiguous officer names versus log entries that link person identification numbers to roles in different companies through time).

In the first phase of the ontology development process, as shown in Fig. 2(a), each data provider provided a description of the dataset they shared. This data analysis focused on identifying the different attributes present and the way in which they were represented. Each attribute was described, adding notes and example uses that clarified the semantics as deemed appropriate. In this phase we already identified similar or even same-as candidates (e.g., company_number, base.ukCompanyNumber, organisasjonsNummer in Fig. 2(a)). Moreover, each provider specified to which extent a particular attribute was shared, in one of three modalities: (i) fully available, (ii) fully available to perform entity matching, but not available in any other case, and (iii) fully available for matching but available in reduced form for other purposes (e.g., address information without street numbers). Analyzing the descriptions provided in the previous phase, we identified a common subset shared by all contributed datasets. This common subset contained attributes that represented the same or very similar concepts in all datasets, which allowed us to group attributes from different providers accordingly (see similar attributes grouped under the legalName label across different providers in Fig. 2(b)).

In the next phase, exemplified in Fig. 2(c), we performed a different analysis to assess the suitability of each attribute to work as an identifier of the instance it described. The analysis contained a heterogeneous group of attributes with identifying characteristics: identifiers for geographical entities, legal entities, company headquarters and secondary sites, company websites, among others. Within the provided data, we found several ways to identify an instance in a group of similar instances (e.g., registration numbers and legal names are two different and useful ways to identify a company). Some identifiers are ambiguous in nature, such as company names, while others can be used to uniquely refer to a company, as is often the case with company registration numbers. The expectation is that the former will often be found in unstructured texts while the latter will be useful to annotate those unstructured texts to link to the corresponding instance being referred to. Some identifiers belong to official registers while others are self-issued and not centralized (e.g., websites). Some identifiers are subject to particular geographic jurisdictions (e.g., company registrations in local trade registers), or belong to special registers that attest that companies belong to a certain class (e.g., register of startup companies). In other cases, identifiers simply indicate the database in which the company information can be found (e.g., identification codes issued by data providers such as OpenCorporates, codes issued by other companies that aggregate company data such as Dun & Bradstreet), the website of a company or the various associated social network identifiers (e.g., a company’s Facebook page or Twitter handle).

In light of the varied nature of the identifiers available, it was determined that the semantic model should also represent key aspects of the different identifier systems in use. These key aspects should encode expectations of the identifiers issued under each system and provide readily available rules to aid in validation and transformation of these identifiers. The expectations should help to determine the suitability of a particular indicator for common use cases that included publishing, reconciliation and matching within unstructured text. Additionally, the semantic model should provide links to information about issuing authorities and maintainers, revisions, databases and other resources.

In the last phase of the development process, as exemplified in Fig. 2, we searched within existing vocabularies for all the concepts identified in the common subset aiming to reuse whenever possible. Examples of reuse from appropriate ontologies include W3C Org, RegOrg, Location, Person (not W3C), Schema.org and ADMS datasets and identifiers.

Differences in the ways each provider decided to share the various attributes present in their datasets made it necessary to understand the scope of the ontology as early in the process as possible. In this way, it was possible to determine what to cover while having a clear path for extensibility.

3.3.Reuse approach

One reuse approach is to create own terms (classes and properties), and tie them to existing ontologies using semantic mapping properties (e.g., equivalentClass, subClassOf, equivalentProperty, subPropertyOf). This has the benefit of a single name space, which may make it easier to produce data conforming to the ontology. Such approach is often used in very large ontologies, e.g., Wikidata or Schema.org. Using such semantic mappings has its cost, since it complicates inferencing and querying requirements. For lighter-weight ontologies such as euBusinessGraph, we prefer to reuse terms from existing ontologies directly. We also prefer to define our own terms only when we cannot find appropriate terms in existing ontologies. For example, we added the following classes (see Sections 4.2.1 and 4.2.3):

Furthermore, we want our own terms to be easily reusable. This is facilitated when terms do not carry a lot of “ontological baggage”, such as deep class hierarchies or strong bindings of properties to classes. RDFS defines the properties rdfs:domain and rdfs:range, which are strict and “prescriptive”: according to RDFS semantics, every time the described property is used, its subject (respectively object) gets the domain (respectively range) as type. This also makes these RDFS properties “monomorphic”: they should take single values, otherwise the subject/object will get multiple types, which are usually not intended. E.g., if “name” is applicable to Person and Organization with the axiom :name rdfs:domain :Person, :Organization, entities with “name” will become both Person and Organization, which is an often made mistake. Ontology engineers overcome this problem by introducing abstract super-classes (e.g., Actor or even Nameable), using owl:union to make a disjunction of several classes, or using owl:Restriction to bind the property to the class locally. But all of these approaches complicate the ontology, increase “ontological commitment”, and ultimately make ontology reuse harder.

Schema.org describes many real-world entities, is applicable in a wide variety of domains, and integrates data from a huge number of providers and domains. The Web Data Commons crawl from 2019-123737 found 44B triples about 14B entities at 934M pages from 12M web domains, and the majority of that data is in Schema.org. To cope with this web-scale integration of data, Schema uses properties schema:domainIncludes and schema:rangeIncludes, which are advisory and “descriptive” (describe properties applicable to a class, without being exclusive), therefore polymorphic: an axiom like :name schema:domainIncludes :Person, :Organization doesn’t cause any unintended types to be inferred. In addition to Schema.org, the same approach is used in the Web of Things initiative.3838

This approach has much lower “ontological commitment” and we find that it enables more flexible reuse and combination of different ontologies, so it is appropriate in the company data domain, where data comes from a large variety of providers. Rather than using complicated OWL mechanisms, we prefer to use RDF Shapes to validate incoming data from data providers.

4.1.Registered organization

Registered organizations are the main entities for which information is captured in the euBusinessGraph ontology. The ontology is not concerned with unregistered informal groups. Registered organizations gain legal entity status by the act of registration and are distinct from the broader concept of organizations, groups or, in some jurisdictions, sole traders. Figure 4 shows the classes and properties for representing core data about a registered organization. The class RegisteredOrganization contains names and other basic information about an organization such as legalName{rov} and jurisdiction{dbo} (see Section 4.1.1), supports different types of classifications such as orgActivity{skos}, orgType{skos} and orgStatus{skos}) (see Section 4.1.2). An organization can have several online resources associated such as email{schema} (see Section 4.1.3). A registered organization has a public site/address where legal papers can be served, and possible other sites/addresses. The sites/addresses are represented using the classes Site and Address (see Section 4.1.4). The object property registration{rov} denotes the identifier of a company. The identifier system is described in further details in Section 4.2.

4.1.4.Sites and addresses

Physical presence of companies is defined via addresses. We model address in a structured way using a set of attributes such as country, macroregion, province, etc. Addresses may have geographic locations specified with a different resolution level. Least precise geographic location are resolved at the level of a country, while most precise are geographical points that specify location up to a street and house number. We also enable data providers to provide full addresses in the form of a free text, which is essentially a string that combines all attributes together into a human-readable format. To provide RDF binding for the attributes, we considered two ontologies. From the ISA Programme Location Core Vocabulary we reused structured attributes such as fullAddress{locn} that specifies the full address in a free-text form. To represent geographic coordinates, Schema.org was used as it provides a simpler way to model geographic coordinates via two properties (latitude{schema} and longitude{schema}).

We distinguish between registered, and other kinds of addresses. Many jurisdictions have the concept of registered address, i.e., the legal address where summons, subpoenas and other legal documents can be sent. An address is modelled using the Site and Address classes. A Site of a company is connected using the object property hasSite{org}. A registered site is additionally connected using the object property hasRegisteredSite{org}. A Site connects to an Address through the object property siteAddress{org}.

Fig. 5.

Example of company representation for SpazioDati.

The class Address represents a mailing or physical address of the company and has the following properties:

• fullAddress{locn}: Full address, free text.

• adminUnitL1{locn}: Country of the address.

• adminUnitL2{locn}: NUTS1 region of the address.

• adminUnitL3{ebg}: NUTS2 region of the address.

• adminUnitL4{ebg}: NUTS3 region of the address.

• adminUnitL5{ebg}: LAU1 region of the address. Some countries (e.g., Bulgaria) use both LAU1 and LAU2 levels. Others (e.g., Italy) use only LAU2.

• adminUnitL6{ebg}: LAU2 region of the address.

• postName{locn}: Locality/city/settlement of the address, free text.

• addressArea{locn}: Part of a city, village or neighbourhood.

• thoroughfare{locn}: Street name (and optionally number).

• locatorDesignator{locn}: Street number and/or building name.

• postcode{locn}: Postal code of the address.

• poBox{locn}: Some addresses are associated with a PO box instead of a street address.

NUTS values are assigned using the EU NUTS classification as Linked Data (NUTS-RDF) datasets.5353 The NUTS-RDF datasets cover 34 European countries and use the NUTSRegion class to represent the NUTS regions. In order to represent the lower-level LAU regions we introduced the LAURegion class and created our own set of LAU-RDF datasets5454 covering 32 jurisdictions (including all of the EU and EEA), 26 languages, and both LAU territorial levels (lau4, lau5). LAU-RDF datasets were created from the official Eurostat Excel spreadsheet for 20165555 for EU, and our own research on some other jurisdictions.

4.2.Identifier system

Mechanisms to identify companies in various data sources are essential in integration of data about companies across data sources. A proper understanding of what kind of systems of identifiers can be used for companies is thus necessary in this context. We analyzed various types of identifiers commonly used for companies and collected various properties of the systems they are part of. We modelled identifiers and identifier systems explicitly in the ontology as shown in Fig. 6.

Fig. 6.

Classes, object properties and data properties for representing identifier systems and identifiers.

A RegisteredOrganization can have several Identifiers issued by different issuers for different purposes. This is modelled by having each company identifier belong to an IdentifierSystem (see Section 4.2.1). In this way, we can differentiate between an “official registration” in official business registers and “alternative registrations” in other kinds of registers. While they have the same nature, only the former can be used to uniquely identify a company in each jurisdiction, and to confirm existence of the company as a legal entity in this jurisdiction. Other registrations may not be unique or persistent. The ontology models the different cases through properties that describe the lifecycle of each identifier issued and by encoding a series of characteristics of the identifier system to which the identifier belongs (see Section 4.2.2). Additionally, we model Web resources (see Section 4.2.3) that are frequently found for identifier systems such as search endpoints, templates for building identifier URLs (through which company information can be reached) and other resources that describe the system’s rules. Finally, the model captures the representation of different agents (see Section 4.2.4) that are in charge of setting and maintaining rules, issuing identifiers and publishing identifier databases.

4.2.5.Example

An example of an identifier system is shown in Fig. 7, illustrating the OpenCorporates identifier system for which OpenCorporates is the publisher and the official UK identifier system for which Companies House is the publisher.

Fig. 7.

Example of representing the OpenCorporates identifier system and the Companies House official UK identifier system.

4.3.Officer

An officer is a natural person (as opposed to a legal person) that has a high-level management role in a company, e.g., executives and directors, and other important roles (e.g., secretary, legal council and treasurer). Officers have the authority to act on behalf of the corporation, including contract authority. Officers can also be shareholders. However, since few jurisdictions have rules for beneficial ownership reporting, and even those who do still have very little data about it,5656 shareholders were considered out of scope for the euBusinessGraph ontology.

We use the membership model5757 of the W3C Organization Ontology in a straightforward way to represent officer data as shown in Fig. 8. An officer is represented using a Person class for which the properties identifier{schema} and birthName{person} are mandatory. The identifier may come from official registries or be derived from these. Additionally, other properties may be present such as gender{schema}, birthDate{schema} and nationality{schema}.

Fig. 8.

Classes, object properties and data properties for representing officers.

Fig. 9.

Example of officer representation for the company OpenCorporates.

A Membership describes the relation between an officer and the company in which they occupy a position. The Role defines the position the officer fulfills according to the membership. Ideally, the roles should be defined according to a SKOS concept scheme. We have not defined a global set of officer roles as this may vary per jurisdiction and/or provider. Thus, we also introduced the data property rolePositionText{ebg} in the Membership class in order to capture the role as free text.

The membership interval is defined by the memberDuring{org} object property that points to an Interval. The interval has a beginning and an end date. For open intervals only the beginning is mandatory. These dates are defined by the class Instant which has the data property inXSDDate{time}.

4.4.Dataset

Data consumers need to know how many companies are included in a data provider dataset, from which jurisdictions, and what depth of data is included (e.g., which properties, addresses with what geo resolution, etc.). We thus need to express both metadata about the dataset itself, and fine-grained statistics about the content of a dataset, e.g.,:

After an analysis of various dataset description ontologies, we decided on using VOID with some extensions (see Fig. 10). VOID describes RDF datasets in terms of entities, classes, properties, triples, partitions (e.g., triples having a particular class), etc. A Dataset has a subset{void} relation that is used to describe a dataset polyhierarchy. For each data provider we can capture their full dataset and the respective subsets. For each Dataset the publisher{dct}, type{dct} and license{dct} have to be captured.

Fig. 10.

Classes, object properties and data properties for representing datasets.

4.4.1.Example

Figure 11 shows an example of the datasets provided by SpazioDati. The dataset <dataset/SDATI> consists of two subsets, namely <dataset/SDATI/IT> and <dataset/SDATI/GB>. For each subset we specify the number of entities and the properties that are available.

Fig. 11.

Example of datasets provided by SpazioDati.

4.5.Validation rules

In order to ensure that data can be correctly published according to the ontology, we devised a set of data validation rules that are associated with the ontology. The types of validations rules considered are as follows:

Fig. 12.

Example of SHACL shape used to validate EBG RDF company data.

There are several possible ways to describe data validation rules, ranging from an algorithmic style such as: legalName EXISTS AND len(trim (legalName)) <> 0 to a semantic based definition by using the SHACL [22] (Shapes Constraint Language) notation. SHACL is a language for validating RDF data graphs against a set of conditions that are provided as shapes and other constructs expressed in the form of an RDF graph (i.e., a shapes graph). ShEx [28] (Shape Expression) is a similar high-level language that can be used to validate RDF graph data. Both SHACL and ShEx use RDF syntax, and share the mechanisms of shape constraints, node constraints, property constraints, cardinalities, and logical operators. Examples of SHACL and ShEx shapes for the euBusinessGraph ontology are available in the Github repository.5959 Figure 12 shows an example of how SHACL validation shapes can be defined for a company URI node and two corresponding attributes (i.e., legalName and orgActivity). The legalName pattern requires the legal name to be canonicalized, i.e., not have leading, trailing or consecutive spaces (denoted as underscores below).

The ontology itself is not limited to European companies, but to ensure maximum compatibility across European data, our data provider rules and RDF Shapes choose specific EU-relevant thesauri. We use NACE for industrial classification (rov:orgActivity), NUTS+LAU for geographic regions (properties of locn:Address), specific SKOS schemes for Legal Type and Status. These can be easily adapted to other nomenclatures or knowledge bases: there is a multitude of industrial classifications (NAICS and the older SIC for North America; TRBC and GICS for stock indexes, etc.), and GeoNames is more appropriate for geographic regions in a global setting. For example, the ONTO CG ontology that builds upon euBusinessGraph uses more general nomenclatures.

5.1.Overview of data mapping approach

In order to develop the euBusinessGraph knowledge graph harmonizing data from various data providers, we devised a data mapping approach that was used to convert company data from CSV and JSON sources into RDF conforming to the ontology. In the following, we describe the mapping notation and provide specific examples showing how the mapping rules were used. Actual mappings for data are publicly available via the DataGraft platform6060 [30 ,31].

The first step of the mapping process is to select attributes (e.g., base.legalName) from the original data source (e.g., JSON file from data provider), and construct parameter names (e.g., legalName) so that we can reference the attribute values in the definition of the mapping functions, as exemplified in Table 2. When defining the mappings, we assume that the input data is a set of attribute-value pairs. Mapping parameters in Table 2 that are specified as lower-case italic letters refer to a string or number value (e.g., legalName refers to “SpazioDati S.R.L” in the data provider’s raw data source files), while parameters denoted in upper-case letters refer to SKOS concept schemes that were defined as part of the RDF generation process. As an example of the use of concept schemes, the mapping parameter ORGACTIVITY will refer to a URI that uses a classification vocabulary to represent the data attribute (e.g., the URI <nace:62.01> uses a controlled vocabulary6161 to describe NACE economic activities for a company).

Next, Table 3 defines a set of helper functions for a subset of base URIs that will be used to map JSON data to RDF. The helper functions improve readability of mapping rules by reducing the text needed to refer to a specific URI. As an example, the helper function curi refers to the actual URI http://data.businessgraph.io/company/IT/361163703. To produce this URI, mapping parameters listed in italic (e.g., jurisdiction and id) will be replaced by the actual values (e.g., “IT” and “361163703”) from the source JSON data. Furthermore, the mapping definitions may contain input parameters denoted in bold that refer to another function that was defined as part of the mapping process (e.g., ebg-comp points to the URI http://data.businessgraph.io/company). After the set of helper functions were defined, mapping rules were constructed for each of the data provider JSON attributes listed in Table 2. The resulting mapping rules are described in Table 4.

5.2.Infrastructure for the knowledge graph generation

A data provisioning infrastructure was developed to onboard data from various data providers. Using this approach, data source files from data providers were processed and mapped to the euBusinessGraph ontology using the mapping process discussed in the previous section. After transforming each dataset from a tabular format (i.e., CSV or JSON) to RDF, the resulting data was published to one named graph for each data provider jurisdiction in an enterprise semantic graph database, GraphDB,6262 hosted by Ontotext.

GraphDB is a service component on the Ontotext Platform.6363 The platform also includes important features such as data mutations, user management (Fusion Auth), access control, deployment and monitoring. In addition to GraphDB, the data provisioning infrastructure includes a set of data ingestion services and data preparation tools that can be used to simplify data cleaning and transformation from the various sources. The services include data interlinking tools for data transformation, enrichment, interlinking, and metadata generation processes in order to publish the business graph data as Linked Data.

The core process of knowledge graph creation is executed by using the cloud-based data management platform DataGraft. Grafterizer6464 [40] is a framework (part of DataGraft) for interactive data cleaning and transformation, and RDF knowledge graph generation that is used together with the tabular annotation tool ASIA6565 [9] and ABSTAT6666 [27] to map company data to the euBusinessGraph ontology. Finally, the RDF triples are published as a knowledge graph in GraphDB. Grafterizer, ASIA and ABSTAT were used to clean, transform, enrich and convert tabular data to RDF as part of the business knowledge graph construction.

The next section describes how the published knowledge graph was used to populate a marketplace for company data.

5.3.The euBusinessGraph marketplace

A main motivation behind the development of a data marketplace for basic company data is the democratisation of the company information market, currently dominated by a few large international players (e.g., Bisnode6767) that create a market barrier for smaller company data providers like OpenCorporates and SpazioDati. The intention of the marketplace is to enable such smaller players to join a common ecosystem to promote their data offerings, and for data consumers to have a central point where they could easily compare company data offerings. A public prototype of the data marketplace application6868 developed to showcase the use of the euBusinessGraph ontology is available online.6969

The ontology was used in the marketplace to realize functionality for a) full-text advanced search and detailed faceted search for exploration of the company knowledge graph, b) analytics services such as data aggregation and visualization (e.g., company activities per city), and c) search for company news articles, and search for company events.

5.4.Use of the euBusinessGraph ontology in the public procurement domain

Public procurement accounts for a substantial part of the public investment and global economy and therefore there is a need for better insight into, and management of government spending. In this respect, national, regional, local, and EU-wide public procurement portals were established to publish procurement notices regarding the purchase of work, goods or services from companies by public authorities in order to increase transparency, economic activity, and competitiveness [33]. However, the technical landscape is quite scattered and there are no common data formats and models used for exposing such data uniformly allowing advanced analytics and analysis, such as for fraud and trend detection. To this end, the euBusinessGraph ontology was used in the procurement domain, in the context of the project TheyBuyForYou (TBFY),7070 for integrating public procurement and company data into the TBFY knowledge graph [34,36]. The resulting knowledge graph allows browsing, visualising, and analysing public EU-wide procurement data and enables a variety of business cases built on top of it by various stakeholders, such as buyers, suppliers, and policy makers.

The data integrated includes procurement data provided by OpenOpps,7171 and company data provided by OpenCorporates. OpenOpps has gathered over 2M tender documents from more than 300 publishers through Web scraping and by using open APIs and provides the resulting data in Open Contracting Data Standard (OCDS),7272 while OpenCorporates uses its own ad-hoc schema. These two datasets are integrated through an ontology network. An ontology for procurement data was developed based on the OCDS standard [37] and the euBusinessGraph ontology was used for representing the company data. The two datasets are integrated through a reconciliation process [38]. Suppliers appearing in tender data are matched against company data provided by OpenCorporates. The matched company data is extracted and ingested to the TBFY knowledge graph [35]. The current release of the TBFY knowledge graph includes 129M triples as of October 2020 and more data will be ingested.

5.5.Use of the euBusinessGraph ontology for financial transactions

Company-related economic information is crucial to many business operations. It empowers customer relationship management, acquisition of new clients, marketing campaigns, supply chain management, market analysis, competitive intelligence, mergers and acquisitions, etc. In this respect, the euBusinessGraph ontology was used for matching and linking company-related economic information within the context of Ontotext’s Intelligent Matching and Linking of Company Data (CIMA) project.7373 CIMA aims to use AI/ML technologies for linking and harmonizing company-related business data from various sources. The project applies machine learning, semantic modeling and integration, entity matching, automatic classification, logical inference to make data richer, better harmonized, integrated, interlinked and easier to use. As part of the project, Ontotext is creating a Company Knowledge Graph (ONTO-CG) for demo purposes by integrating data from open and a few proprietary datasets. The emphasis of the project is on financial data, industrial classification, company size and important observations such as annual sales and number of employees.