Read about how knowledge management can be made smarter using a knowledge graph built with semantic technology.
Today’s complex enterprise environments need sophisticated solutions and these solutions are not only about technology. They are about strategic partnerships and exploring synergies. There’s a much-needed spirit of collaboration that cannot be bypassed when it comes to creating, maintaining and consuming knowledge graphs.Complex architectures like knowledge graphs cannot be built as a one-size-fits-all solution. The very nature of the connected business environment calls for modular approaches rather than heavy monolithic hierarchical structures. Click To Tweet
In the two decades of building knowledge graphs with the W3C consortium standards (which are essentially a collaborative effort, fitting the need for interoperable and future-proof data management), we have discovered the power of synergies. Through this power, various players can contribute their essential piece of the puzzle and by connecting these pieces, we can jointly deliver complete, end-to-end, reusable solutions.
When it comes to solving a business problem with a knowledge graph in any domain, no vendor can have it all. No single vendor can offer the richness of applications that can satisfy the requirements of every specific use case. What can, though, is a network of partners that provides a dynamic combination of technology stack and professional services and that can supply the best technology blend to meet each specific business need.
With more and more organizations turning to knowledge graphs for better enterprise knowledge management, we’ve been privileged to work with some of the most knowledge-intensive enterprises in Financial Services, Life Sciences and Healthcare, Industry, Media & Publishing and Public Sector & Defense.
Our takeaway from all this is that the best way to build knowledge graphs is the semantic data modeling way, which involves a tailored approach to each specific business case. And we’ve seen again and again that such knowledge graph driven solutions not only address business needs more efficiently. More often than not they redefine the problem, opening up new opportunities as enterprises change their approach to knowledge and data management.
In a world that needs connectivity at every level across the enterprise structure, unified data management is central to keeping businesses in business. To that end, knowledge graphs provide a single point of access to all enterprise knowledge, which enhances decision-making and fosters innovation.
There are different end-to-end knowledge graph solutions, which address different business needs and each knowledge graph application can be achieved through various means.
As you can see from the map above, knowledge graphs can be applied in several main areas and in each of these areas there are a number of applications. There is a wide (and wild!) variety of technology capabilities required and things that need to be done and for none of them (not even for any of the applications) is there a single best way of doing it.
So, instead of claiming to be able to do everything that is required, we have chosen to serve our customers through providing a rich ecosystem of partners. Over the years, we have developed an ecosystem of about 70 companies in the areas of knowledge and content management, data cataloging, data visualization, semantic search, BPM & automation and more. Together, we can cover almost all knowledge graph applications and, for many of them, our clients can even choose what will work best for their needs.
Let’s go through several success stories that our partners have shared with us. They illustrate how strong and powerful the synergistic approach towards building an enterprise knowledge graph is.
While the rest of the post is organized by partner, here we provide references to the presented stories grouped by application areas.
Content and Knowledge Management:
Business Process Management and Automation:
CRM, Public Relationships, Compliance:
Now let us continue with the actual presentation of the stories provided by our partners.
One of EK’s clients, a healthcare workforce solutions provider, was looking to increase engagement and improve learning outcomes across their learning platform by developing personalized content offerings. To that end, EK built a cloud-hosted semantic course recommendation microservice on top of GraphDB. The microservice was integrated with the client’s learning platform and successfully suggested courses and learning paths relevant to each user’s exam performance. EK’s solution not only increased engagement but also provided more sophisticated methods for governance and scale by ensuring the courses covered available topics.
Another client was a federally funded engineering research center who wanted to better organize their extensive “project library” of technical documents, certifications and reports related to various engineering projects. Many documents were in the format of scanned handwritten notes with little metadata and it was difficult to find all relevant projects or experts. To help connect the dots between people, projects, engineering components and engineering topics, EK developed a PoC enterprise knowledge graph and incorporated it into a semantic search platform. This allowed users to browse documents by person, project and topic and keep up to date with project staff changes and evolving requirements.
The first one is about JobTeaser – a company providing a career portal that helps applicants make the best profiles and find jobs that fit their criteria. In order to match users to the most accurate profiles, the JobTeaser platform assesses many aspects ranging from psychology, salary expectations, skill profiles, etc. With such a diverse baseline, JobTeaser required a tailored semantic approach that could integrate multiple facets of data such as the ESCO Skills Taxonomy to create a controlled vocabulary of skills and careers.
In the second one, Insider Intelligence – a research company providing access to information, data and trends about digital business – wanted to improve their user experience. The company struggled to manually update the research taxonomy with new topics and related items and their website offered limited search experience. By employing GraphDB and PoolParty’s easy-to-use taxonomy management tool, the company could seamlessly update the taxonomy. As a result, their website provided fast and powerful semantic search and suggested related results based on the terms in the taxonomy.
We joined forces to support a Swiss multinational healthcare company in building a knowledge graph based solution that provided highly interlinked information across various data sources and offered a modular approach to R&D data discovery and knowledge consumption. Combining GraphDB, a large inventory of ready-to-use biomedical datasets, and metaphactory’s low-code approach to knowledge graph application building made it easy to create a big customized knowledge graph and configure intuitive search and exploration interfaces on top of it. As a result, data scientists, immunologists and systems biologists can explore data and gain meaningful and actionable insights for their daily tasks.
In another success story, a global manufacturer of sensors and sensor solutions for industrial applications wanted an efficient collaboration platform for driving new projects. The solution employed metaphactory on top of GraphDB to power the new knowledge graph driven platform. The intuitive model-driven authoring, search and visualization interfaces allowed employees to track new ideas and project proposals, and to contribute to existing projects. Thanks to metaphactory and GraphDB, management teams at the customer site can quickly report on proposed projects that can be used to define company-wide goals and drive business decisions.
Through the partnership, Synaptica’s clients are able to build their ontologies and taxonomies in an RDF graph database, which provides the foundation layer for enterprise knowledge graphs. For several decades the main uses for taxonomy have been content classification, web navigation and faceted search but, more and more, Synaptica’s clients are starting to use Graphite on top of GraphDB to solve other business challenges such as:
While traditional applications for taxonomy helped people find content, new and emerging applications offer a very different set of benefits. They support machine-based decision criteria, trigger actions and help enterprises automate business processes. They also require tools like Graphite to be integrated with more diverse IT systems, not just content management systems.
One of Perfect Memory’s clients – a major European football franchise – needed a solution that would help them better market and monetize their content assets. To meet these business goals, Perfect Memory set up a platform powered by GraphDB. This platform was able to reconcile data and content coming from various sources, in different formats and used by different user profiles.
In order to make all of this content, including videos and images, searchable and monetizable, Perfect Memory did intelligent content extraction to auto-generate metadata from it (such as sponsors, teams, locations, etc.). The solution significantly improved user experience by providing a unique access point to any fragments and facets of the content. It also allowed users to get relevant results by asking complex questions.
Case in point, GraphDB has powered many of their smart processes automation solutions such as:
Using GraphDB as the underlying graph technology, the Conversational AI platform makes it easy for Onlim’s customers to ingest large sources of structured and unstructured data in a knowledge graph and build relationships between this data. The factual information is then used to run automated conversations in chatbots or voice assistants.
Onlim’s Conversational AI platform allows users to run dialogues, query the knowledge graph for factual information and build natural language answers upon these facts. Some of the use cases include fast and cost effective access to product information in manufacturing, access to tourist information and services, answering any customer service questions in the agricultural sector, etc.
In a world that is growing more and more complex and interconnected, the challenges today’s enterprises face are becoming more and more data and knowledge intensive, software vendors, consultants and integrators innovate constantly to be able to keep up and offer efficient solutions.
Knowledge graphs and their applications are the next generation tool for helping enterprises make critical decisions, based on harmonized knowledge models and data derived from siloed source systems. Their building is deeply rooted in the creation of synergies. It is synergies that make for innovative solutions, which are faster to build and more effectively meet business challenges.