What is an Enterprise Knowledge Graph?
Learn what is an Enterprise Knowledge Graph and how it can benefit your organisation
What is a Knowledge Graph?
There are many possible definitions of “Knowledge Graphs”.
The one we want to focus on, to best describe what an Enterprise Knowledge Graph (EKG) is intended for, is not a precise definition but is the one that most closely describes the characteristics of a graph structure and the automatic systems and algorithms that can be associated with:
A knowledge graph can be represented as a large network of facts and concepts (including entities, concepts, and events) relevant to a specific domain, related to their properties, types and relationships, and organised in a structured way by explicitly describing how each one is connected.
The data is machine-readable and uniquely identifiable based on semantic standards (usually in “triples” form with subject, predicate, and object).
An Enterprise Knowledge Graph (EKG) models, integrates and accesses information assets within an organization using data and metadata.
Enterprise Knowledge Graph (EKG) uses a similar model to extract and store data in triples. An EKG enables organisations to connect and show meaningful relationships between data with a combination of:
- Knowledge representation structures An EKG model helps organisations understand and organise data in a structured way. It can provide you with integrated, linked and reusable data (rather than data silos) and can easily evolve to reflect your organisation’s needs.
- Information management processes Through EKG, organisations can see meaningful relationships and incorporate updated datasets easily
- Search and share using algorithms EKG enables organisations to analyze their graphs to acquire new knowledge and reuse the data for various purposes. Formal semantics is the perfect tool for dealing with large volumes of data in various forms and unravelling complex relationships.
What Enterprise Knowledge Graph can offer you?
Today, Enterprise Knowledge Graphs are evolving to meet enterprise production requirements, including stability, availability, and security.
- EKG implementation is becoming cheaper due to the growing adoption. And the cost of starting a data integration project can be greatly reduced by using a generic schema of triples.
- EKG can be added to existing architectures without disrupting the overall system, leaving the database in place. It also allows data to be decentralized across different graphs and relational databases that are linked together.
- EKG uses very intuitive ways of modelling using the triplestore format. And triple bases are much more adaptable to constant changes than traditional relational databases.
Semantic AI based on EKGs can be used for everything from searching and displaying information, as well as smart semantic AI applications (chatbots, cognitive search using Natural Language Processing —NLP, product advisers, and self-running systems…).
Enterprise Knowledge Graph gives AI applications intelligence.
Leveraging your enterprise knowledge graph can offer you more than just extracting and linking data, providing recommendations and insights:
- Facilitate the access, integration, interpretation, and recognition of entities in context, by connecting them to content and data sources.
- Provide a robust framework for content management, personalization, and interoperability of semantic data.
- Enrich semantic search with connections and relationships between data points for better insights.
- Enable users to find better solutions to address growing volumes of structured and semi-structured data.
- Enable users to make better decisions by showing them non-obvious information.
- Help identify trends, anomalies, gaps, etc. in various areas through deductive reasoning.
- Enables the design of data-centric user interfaces.
- Enables the creation of specific tools for the use cases best served by graph technology, such as business intelligence, expert systems, 360-degree views of customers, fraud detection, and investigative intelligence.
RDF Enterprise Knowledge Graph
Organisations can foster interoperability and a common language across platforms by moving away from conventional relational databases to enterprise knowledge graphs.
It is challenging to manage organisational knowledge when it is scattered across a wide range of disparate, siloed systems. By using RDF (Resource Description Framework), it is possible to build highly interconnected, interoperable, and flexible information structures.
The image that comes to mind when you hear the word ‘graph’ is probably like this: entities – the nodes – are often represented by circles – and relationships – usually by lines connecting the nodes.
This could be illustrated in a multidimensional network model, which reveals not only facts A and B, but also their relationships.
And this is how the RDF data model works.
Organisations that want to effectively manage data in order to reduce development and maintenance costs for their systems can no longer rely on traditional approaches if they want to manage data intelligently.
With semantic data models, you can combine your business data with analytics and algorithms through the use of a model that is separate from analytics and algorithms. Semantic data models provide the best framework for integrating, unifying, linking, and reusing your business data.
It offers a multitude of ways to enrich the EKG, with limited administrative burden and proven scalability:
- Enable communication between humans and computers by allowing schemas, ontologies, and data to be interpreted unambiguously (It makes data understandable in business terms rather than cryptic codes understood by only a few experts).
- Facilitate the modelling of sources and other structured metadata (data schema, taxonomies, vocabularies, all sorts of metadata,…).
- Support the management and storage of complex and large datasets containing hundreds of billions of facts.
- Provide open standards (RDF is a W3C standard), implementing unique identifiers to facilitate data integration and publishing.
- Allow information to be easily identified, disambiguated, and interconnected by AI systems.
- Offer more flexibility to data changes than other data models (schema is not redefined every time new data is added).
- Widely implemented, it gives access to large datasets from various free sources (DBpedia, GeoNames, Wikidata, etc.) whose volume is increasing every day.
- Reduce costs by operating more effectively and enabling the automation of some management processes
How can Cognizone help you with your Enterprise Knowledge Graph?
Transform your data into knowledge
In today’s market, it is imperative to push the boundaries of knowledge creation, sharing and storage.
Companies have been forced to rely heavily on decentralisation. First with BYOD (bring your own device) and then COVID, and now through knowledge graphs, decentralisation has been pushed forward.
We have seen that Enterprise Knowledge Graphs have evolved to meet the production demands of companies, including stability, availability, and security. And that Enterprise Knowledge Graphs is designed to tackle the challenge of decentralised data management.
Does this mean that you will be able to combine your data securely with knowledge from around the world to answer questions beyond your organisation’s internal knowledge? It is nearly impossible to predict when EKG will become the core of most organisations’ data management strategies. But, you don’t have to think so big to start making use of Enterprise Knowledge Graphs and Linked Open Data: EKGs can remain centralised, not part of a wider semantic web, and yet be the backbone of knowledge and content management initiatives within your organisation.
Hybrid deployment to meet your business needs
If your organisation, like many others, faces challenges in integrating and managing data from various sources, exploring and understanding data, and creating actions that have a real impact. And if you are looking for ways to get started, you can contact us.
These are the areas where we operate best at Cognizone, offering you a hybrid deployment:
- Consultancy: Defining the benefits, needs, and requirements for implementing EKG in your organisation.
- Data modelling: Provide you with support for accurately modelling your data and migrating it within the knowledge graph.
- Integrator of Knowledge graph solutions: Help you implement a standardised solution that everyone (machine and human) can understand unambiguously.
- Integrator of Knowledge graph solutions including our product Hanami: Setting up systems for data quality validation, and lineage traceability.
- Development of custom Knowledge Graph applications: Design and implementation of the most suitable solution for your business.
- Solutions for Linked and Open Data Publishing: Support you to openly publish your data to allow it to be linked and queried in a standardized way, enabling more powerful and flexible data integration and analysis from third-parties.
We believe that each customer faces unique challenges and our services are designed to best address them.
The Enterprise Knowledge Graph implementation does not follow an identical methodology from one enterprise to another. Many parameters must be taken into account for the success of such a project.
Inspired by our experience in a wide range of contexts, both in terms of the business sector, size and corporate culture, we aim to develop systems that are customised to your business cases and individual needs.
We are known for our flexibility and tailoring and are committed to delivering dedicated experiences that enhance the work of all data stakeholders.
When all parameters are considered, you can build a more manageable and efficient network, with datasets that are easier to integrate, discover, access, link, and integrate.
Takeaways
Knowledge management and data integration as Enterprise Knowledge Graphs are evolving to meet enterprise production requirements, including stability, availability and security.
An Enterprise Knowledge Graph represented by RDF and defined by OWL models is highly flexible. It provides the most appropriate framework for data integration, unification, linking and reuse, whilst maintaining the lineage of data sources. When a huge amount of data from disparate sources is processed, it is essential to know where the data comes from to ensure quality and compliance.
Enterprise knowledge graphs are a prerequisite for the creation of specific applications powered by intelligent and semantic AI such as business intelligence, expert systems, 360-degree views of customers, fraud detection and investigative intelligence.
Enterprise knowledge graphs enable communication between humans and computers by allowing schemas, ontologies and data to be interpreted unambiguously. It makes data understandable in business terms rather than cryptic codes understood by only a few experts.
Knowledge graphs offer you the most robust framework to discover new facts about your data and gain new insights into your organisational knowledge that otherwise would remain unnoticed.
We are a specialist consultancy with long experience in designing and implementing Enterprise Knowledge Graphs in government and other data-intensive sectors.
Through our combination of technical skills, industry practitioners, and expertise in open and linked data, we can help you see data challenges from a new perspective.
Some Knowledge graphs in use
The Panama Papers are an unprecedented leak of 11.5m files from the database of the world’s fourth biggest offshore law firm, Mossack Fonseca.
The LOD cloud diagram is a visualization of 1579 datasets with over 16,200 links. Each vertex is a separate dataset structured as a knowledge graph.
Airbnb knowledge graph is containing a very large database of place, events, experiences, people, travel destination.
