Knowledge graphs represent a powerful tool for improving management, understanding and analysis of data, offering a semantic and interconnected view of information. This facilitates viewing the relationships between entites, improves the search for information, allows for the inference of new knowledge, and drives decision making and data analysis.
Our virtual approach to creating knowledge graphs through ontologies enables interoperability and maintenance, and guarantees scalability, allowing for the management of large volumes of data without losing coherence.
Knowledge Graphs
Self-Service Business Intelligence
By leveraging the semantic model of the domain provided by the ontology, end business users, data scientists, or sales development reps can access the data, through their business language and rules, without writing a single line of code, and can build their custom visualizations without the help of technical teams. Our focus is prioritizing data access by putting the information in the hands of as many people as possible. To do so, we heavily invest in no-code features and user experience.
Data Integration
Using open standards such as RDF and OWL, knowledge graphs and ontologies facilitate integration and interoperability between different systems and data sources. We strive to create a high-level, business-oriented overview of complex business domains, while at the same time resolving semantic ambiguities in the data, improving its accuracy, and enabling access to heterogeneous data.
Ontology Development
We provide the solutions and know-how to help business organizations create semantic models of their enterprise domains. Our ontology design environments provide knowledge engineers with easy-to-use fully graphical tools to identify key business concepts and relationships, and seamlessly translate them into ontologies in open standards like OWL or RDF.
Data Quality Assurance
Our goal is to help users better understand the meaning of their data and feel confident in its quality. To achieve this, we use the ontology as the benchmark for data quality assurance, and streamline data quality control procedures through it. Our ontology reasoning and inference engine can automatically extract business rules from the ontology model and transform them into data quality checks which are transmitted to the underlying data sources. This way, IT experts and data scientists can focus on analyzing the information and cleaning the data, instead of worrying about where potential inconsistencies might be.
Linked Open Data Preparation
We help organizations check all the boxes of the best guidelines and practices for producing five-star Linked Open Data. Designing strong semantic models which provide the meaning that underlies the data, using standard vocabularies and defining good URI naming strategy and implementation plans, building short, easy-to-maintain, and seamless dataset production pipelines.
Data Catalog
Large-scale organizations handle an ever-growing number of data assets. Without the right tools to help consumers understand where information is stored, how to access it, and which sources are relevant to their goals, managing this complexity can become a serious challenge.
OBDA Systems provides a platform through which business users and data managers can organize their data assets, gather insight on the provenance and lineage of their data, and increase explainability of the business queries and reports.
