Onboarding to Enterprise Knowledge Graphs

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knowledge graphsEnterprise Knowledge Graph vendors are working hard to find their place in the heart of businesses, helping them do more with and get more out of their mountains of data. Recently, for example, Stardog has adopted its leading Knowledge Graph platform to be “FIBO-aware,” mapping to the Financial Industry Business Ontology (FIBO) semantic standards out-of-the-box. GraphPath launched what it says is the first Knowledge-Graph-as-a-Service (KGaaS) platform. And Maana, with its Knowledge Graph-centered Knowledge Platform, has been talking up its partnerships with clients like Shell to drive digital transformation efforts.

As part of these efforts, work is underway to make it easier for businesses to adopt these solutions – for experts like data engineers who will manage the graphs, of course, but also for the business users who will consume data from them via different applications that developers create.

Metaphacts, the mind behind the metaphactory end-to-end platform for enterprise Knowledge Graphs, is very keen on lowering the barrier to entry, says founder Peter Haase. A Semantic Technology veteran, Haase is well aware of the role knowledge graphs can have in enabling intelligent applications – semantic search, Q&A, analytics, dashboards, knowledge sharing, and knowledge management – by sitting atop and enabling integrated access across data stores unified by semantic descriptions of entities and their relationships. But delivering to those ends demands easing all lifecycle aspects, from Semantic Graph Data Management to data-driven application development.

“Google and Facebook really helped to set Knowledge Graphs on the agenda of CIOs and Chief Architects, but there was the challenge of what tools to use to bring this concept into their companies, says Daniel Herzig-Sommer, who was CEO of SearchHaus. The company was recently acquired by metaphacts, and Herzig-Sommer is now COO there.

Realizing Enterprise Graph Value

“People see the potential but organizations struggle with implementation,” Haase notes. While certain base technologies are very well established, like RDF triple stores, there are deficiencies in other areas. End users have rich information needs that in principle Knowledge Graphs can address, he says:

“But they don’t want to author SPARQL queries. They need more of an end user interface to capture their information needs and translate them to SPARQL for them for end-user oriented search.”

With SearchHaus’s GraphScope product now integrated into metaphactory, users can find precise answers in large data graphs using keyword search as a way to help on that front. “With GraphScope we developed a search engine to query Knowledge Graphs as you would query any other data source,” says Herzig-Sommer. It handles the work of translating keyword queries into SPARQL query patterns to run against triple stores, recognizing the links between connected and interlinked objects.

Being able to ask natural language queries and get semantic results:

“Opens up the enterprise knowledge graph to the end user who would never write a SPARQL query but now can use the technology to uncover entities and relationships,” he says.

Other end-user oriented paradigms it supports for searching enterprise Knowledge Graphs include visual query constructions and faceted exploration. It even can map an Amazon Alexa Intent to a SPARQL query to the Wikidata Semantic Knowledge Graph – or any Knowledge Graph managed in metaphactory – via a voice interface. Metaphacts now is an AWS technology partner, and expects to strengthen its position in the AWS public Cloud space. The executives acknowledge, though, that there’s still a concern among enterprise customers about the intersection between putting sensitive data and queries into the public cloud space.

Visual explorations of results can take the form of graph views, maps, tree tables, and charts, among others. Queries and search results can be saved, too.

Use Cases at Hand

On the Knowledge Graph backend, metaphacts supports the data lifecycle with features including scalable data processing, built-in inferences and custom services, and standard connectors for a variety of data formats. Knowledge Graph creation benefits from capabilities such as curating, linking and annotating data from diverse sources.

The system also supports rapid development of mobile and Web end user-oriented applications to present, visualize and search data: Providing an open platform API and SDK; full HTML5 compliance; re-useable, declaratively configured Web components; and, provenance information capture.

Right now, metaphacts’ clientele is mainly in the pharma/life sciences, Industry 4.0 (digital engineering and manufacturing, where “digital twin” data across business units is relevant from engineering through to production) and cultural heritage arenas. Most of its customers already had some familiarity with the Open Semantic and Linked Data standards and technology stacks upon which metaphactory is based.

“On the expert IT user side, they had already bought into the idea of using Knowledge Graphs or semantic technologies,” says Haase. Still, metaphacts provides training on how to properly build ontologies, organize and query data and other issues, since the technology can be challenging even to those with an affinity towards leveraging it.

Current use cases include pharma company Sanofi using metaphactory to build and leverage Knowledge Graphs that connect data silos to improve drug research. In a project with The British Museum, metaphacts is building a Knowledge Graph-based collaboration platform for cultural heritage researchers.

Anyone can experiment with that project here, and Herzig-Sommer urges potential users to keep in mind that the same systems and functionalities evident there are all transferable to other domains. Says Haase, “The entire platform is horizontal, with generic components that are easily customizable across different domains.”


Photo Credit: Titima Ongkantong/

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