Knowledge Graph Applications
Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Knowledge graphs link diverse data and content to provide access to knowledge locked in siloed systems or static documents. This is particularly important when it comes to connecting enterprise information through a data fabric as well as analysis of data from data lakes.
We’ve have successes implementing this vision with in-use applications across several verticals:
How You’ll Benefit
Connect and publish enterprise knowledge, integrating data from different sources.
Utilize your enterprise data better to empower deeper analytics for stronger insights.
Get value from information previously locked in disconnected systems or in static content.
Develop new revenue streams from existing assets and data products.
Enrich your proprietary information with external domain knowledge to put it in context.
How it works
Next-generation data management and content management technologies rely heavily on diverse, complex and dynamic metadata. Active metadata is at the heart of the data fabric concept for automation of data analytics. Knowledge graphs bring together data and metadata across different IT systems to deliver disruptive efficiency gains across numerous applications as shown on the map below.
Main Applications
Content Management
If you are a content-centric enterprise, knowledge graphs can help you better utilize and repackage your content. By enriching your content with additional metadata, you can make it easier to discover and explore, generate better feeds, improve user engagement on your website, suggest better recommendations and more. Content enrichment can be automated by the use of text analysis.
See also: What is text analysis?, Text Analysis for Content Management, Media & Publishing
Knowledge Management
Having the right knowledge about your products, devices, technical documentation, skills and competencies, research teams performance, etc. is crucial for many processes within an organization. By developing domain-specific taxonomies and vocabularies to populate the semantic model of a knowledge graph, you can better classify your documents and thus improve your knowledge discovery and analytics.
See also: Building Semantic Knowledge Organization Systems with Graphite and GraphDB, Turning a Taxonomy into a Recommendation Engine
Data Management
If you are a data-centric organization, knowledge graphs can enable you to have unified access to all your diverse data. Using large volumes of domain knowledge about companies, electrical networks, pharmacology, scientific publications, etc. can help you get deeper analytics, enhance decision making, improve search and recommendations, strengthen scientific collaboration and more.
See also: Healthcare and Life Sciences, Financial Services, Manufacturing, AECO & Infrastructure, Energy, Public Sector & Defense
Business Process Management (BPM) & Automation
Knowledge graphs can help your enterprise get a global view across an extensive set of products or components working together in a complex system such as Building Automation Systems, automotive manufacturing, aerospace, etc. They can significantly facilitate infrastructure monitoring, predictive maintenance, research and repurposing of existing resources (connected inventory), supply chain management and more.
See also: Connected Inventory, Building Automation Systems, Manufacturing, AECO & Infrastructure, Energy
CRM, PR, Compliance
Knowledge graphs in combination with text analysis can enable your organization to do better media monitoring for PR and reputation management, market research, brand or product perception, competitor analysis, as part of know-your-customer and anti-money laundering policies, etc. They can also help you with regulatory reporting, digital twins, chatbots and more.
See also: Better Trade Surveillance by Using Ontotext GraphDB to Tackle Market Manipulation, Advanced Drug Safety
