This poster paper provides an overview of euBusinessGraph{ a business knowledge graph for basic company data, together with related artefacts (datasets, ontology, and infrastructure), and its use for creating a prototype for a data marketplace for basic company data. euBusinessGraph was developed by aggregating, linking, and provisioning data from several distributed data sources.
Many data value chains, in various sectors such as procurement and marketing and sales, depend upon company data. Integrating company data from various authoritative and non-authoritative sources is challenging due to heterogeneity and complexity of the data and the lack of generally agreed upon semantic descriptions. Several initiatives (e.g., the Global Legal Entity Identi cation System (GLEIS)1) have been established to harmonise and increase the interoperability of corporate data across national borders. Such initiatives are mostly fragmented across borders, limited in scope and size, and siloed within speci c business communities. As a step in addressing this challenge, governments are increasingly publishing open data about rmographics. Unfortunately, these datasets are not yet fully harmonised and interoperable because data di ers widely in semantics and data formats are often poorly accessible and documented. In other words, the harmonisation of company data is far from a solved problem.
To address the aforementioned issues, we follow the established ontology-based approach for harmonising and integrating data. We built a business knowledge graph (KG)|euBusinessGraph|by aggregating, linking, and provisioning basic company data from several distributed data sources. In this poster paper, we provide an overview of the underlying datasets, the ontology developed to help with data integration, the data ingestion process, data provisioning infrastructure, and the use of KG for creating a prototype data marketplace for sharing company data. 2
The KG was built by aggregating data from four data providers, mapping and translating them into RDF with respect to an ontology and storing and publishing data in a triple store following Linked Data and Semantic Web principles. The data providers for the KG were OpenCorporates2, SpazioDati3, Br nn ysund Register Centre4, and Ontotext5. The data made available by the data providers originally came from both o cial sources, such as national business registers, and uno cial sources, such as the corporate web.
We developed an ontology|the euBusinessGraph ontology [
DataGraft data management platform
Company data from data providers
CSV or JSON 1
Data cleaning and transformation (Grafterizer framework) 2 euBusinessGraph
ontology
RDF mapping (Grafterizer framework) Semantic graph database
GraphDB 3
Business knowledge graph representations for the same entity and in some cases close but not entirely similar semantics. The resulting euBusinessGraph ontology is composed of 20 classes, 33 object properties, and 56 data properties. Fig. 1 provides an overview of the ontology, depicting the main classes and their relationships (i.e., object properties). Registered organisations are the main entities for which information is captured in the ontology. The main classes include RegisteredOrganisation, Identifier, IdentifierSystem, Person, and Dataset. Three types of classi cations are de ned in the ontology for representing the company type, company status, and company activity. These are modelled as SKOS concept schemes. External vocabularies and ontologies were used as needed, for example, the W3C Organisation ontology, W3C Registered Organisation Vocabulary (RegOrg), SKOS, Schema.org, and Asset Description Metadata Schema (ADMS) were reused. The ontology, datasets, and examples are released as open source and are available on GitHub6.
We devised a data mapping approach to convert company data from CSV
and JSON sources into RDF conforming to the euBusinessGraph ontology using
the DataGraft platform [
We developed a data marketplace prototype on top of the resulting KG. The 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 that create a market barrier for smaller company data providers. The intention of the marketplace is to enable such smaller players to join a common ecosystem to promote their data o erings, and for data consumers to have a central point where they could easily compare company data o erings. A public prototype of the data marketplace application is available online7 and a screenshot if its homepage is shown in Fig. 3.
The available data in the marketplace application includes the most central attributes that re ect how the ontology can be used to describe the semantic relations of company data. Each data provider URI in GraphDB is related to a dataset description that describes the data being o ered in the marketplace by inserting void:inDataset for each rov:RegisteredOrganization in the graph database. The marketplace includes functionality for full-text advanced search and detailed faceted search for exploration of the company knowledge graph. Furthermore, the marketplace o ers analytics services such as data aggregation and visualisation (e.g., company activities per city), search for company news articles, and search for company events. The ontology was used in the marketplace to realise scenarios such as: { Company search: Find a speci c company by displaying a page that describes available attributes of the company. The ontology enables search for detailed company information from di erent providers, and facilitates data
provenance, as the speci c company data from data provider can be traced back to its sources. { Advanced company search: Find how many companies are in a certain jurisdiction, active or inactive, registered in a certain year, with a certain type, in a certain location or are operating within a certain economic activity. This scenario is covered by allowing search for companies by certain criteria or facets and dynamic ltering of results. The search functionality of the marketplace demonstrates how the semantic model enables a uniform way of harmonising and representing hierarchical facets for geographical location and economic classi cation. { Analytics related to company data: Find out how many companies are registered per year in a speci c country and city and are operating in a speci c location. The marketplace application provides the ability to get basic statistics about the company data in the knowledge graph. A bar chart visualization lters information by country, city and activity and gives the user a visual representation of the data. 4
In this poster paper we argued for the importance of harmonised basic company data as a key enabler for di erent value chains in various sectors that depend on company information and described the euBusinessGraph approach for harmonising basic company data. Our approach is based on a lightweight ontology for aggregating, linking, provisioning, and analysing basic company data. The ontology was developed following best practices for ontology development and published following Linked Data and Semantic Web principles.
Acknowledgements The work reported in this paper is partly funded by the EC under the H2020 euBusinessGraph project (Grant nr. 732003).