Home » Uncategorized

A few large enterprise software provider strategies for the knowledge graph market

  • Alan Morrison 
A few large enterprise software provider strategies for the knowledge graph market
Image by David Zydd from Pixabay

In November 2023, MarketsandMarkets announced the publication of its Knowledge Graph Market report. In its announcement, M&M estimated the 2023 global knowledge graph market at $0.9 billion, forecasting market growth to $2.4 billion by 2028, a compound annual growth rate of 21.9 percent. M&M also listed these 12 “key players” in its announcement:

  • AWS (US)
  • Franz Inc (US)
  • IBM (US)
  • Microsoft (US)
  • Neo4j (US)
  • TigerGraph (US)
  • Oracle (US)
  • Ontotext (Bulgaria)
  • OpenLink Software (US)
  • SAP (Germany)
  • Semantic Web Company (Austria)
  • Stardog (US)

I haven’t seen M&M’s report, just the press release announcing the report. But it’s interesting anyway to ponder what M&M considers the knowledge graph market to be and where they think the growth prospects are. First some comments about the players I just did a bit of desk research on, to update myself.

SAP’s business process-oriented knowledge graph strategy

All the listed players with the exception of IBM, SAP and Semantic Web Company (provider of PoolParty, a knowledge modeling platform) are prominent graph database providers. 

The true outlier on the list is SAP.  An application suite provider since its inception, SAP hasn’t offered knowledge modeling products or databases on a standalone basis. All of its efforts are focused on selling and enhancing the utility of its applications, now provided as cloud services. 

But even so, SAP does have a novel strategy for positioning itself in this emerging market. Stefanie Glenk’s April 2023 feature on the SAP News Center site “Knowledge Graphs: The Dream of a Knowledge Network” touches on these themes:

  1. Hybrid AI’s potential. SAP underscores that knowledge graphs, unlike current static machine learning techniques, can be updated with new data anytime. Also, that knowledge graphs bring with them precision, explainability and model-driven application power that statistical ML just doesn’t have. Best-in-class approaches blend statistical ML with knowledge graph sources, external as well as internal.
  2. Business scenario automation potential and the Situation Knowledge Graph. SAP has built its own process ontology, distilling 50 years of the company’s process-oriented knowledge, which it now uses to facilitate situation handling. As I understand it, a domain expert working with the process model can place a situation that requires special handling within its context and specify rules to resolve the problem associated with that exception.The intriguing thing about this approach is that the domain experts can do a bit of modeling where they need it to solve an immediate business problem. 
  3. Blending the external with the internal with the help of standards. Users of SAP S/4HANA (an in-memory ERP suite) can take advantage of public knowledge graphs such as Wikidata and DBpedia that harness the discoverability and sharing power of semantic standards for a blend of external and internal sources. SAP points to the integration scalability of graphs with this approach and also the ability to start small with subgraphs and iterate and combine to form larger knowledge graphs.

SAP’s approach for these reasons reflects a grasp of the importance of standard, shared semantics and the need to provide business users with a graph-oriented modeling capability where and how they need it. The devil’s in the details, of course. Much depends on how this set of capabilities is implemented.

Oracle’s supply chain-oriented knowledge graph strategy

Judging from what I’ve seen that’s been posted online recently, Oracle’s knowledge graph strategy focuses on desiloing supply chain data. For example, Jason Duncan-Wilson, senior director for Oracle Spatial’s energy & water data exchange, and Kestas Markauskas, senior cloud architect, presented in May 2023 to the Oracle Data & Analytics User Group on the status of the exchange. 

Much of this talk targeted an audience who weren’t familiar with knowledge graphs, and it emphasized the power and extensibility of the core industry ontologies Oracle provides that customers can embrace and extend. The messaging was quite consistent with that of the standards-based RDF triple/quad store platform providers on the M&M list.

Microsoft and IBM?

I took a look at the Microsoft and IBM sites as well to see what they offered on the knowledge graph front, because I do track the KG market and hadn’t heard much at all about them in this space, at least not commercial products. 

What I found wasn’t strictly related to knowledge graphs, or at least not standards-based knowledge graphs. Microsoft offers the multi-model CosmosDB, which third parties have occasionally used to build narrowly focused, non-standard graphs. IBM of course had the original Watson effort from the 2010s that used knowledge graphs extensively, but the more recent materials I found that mentioned the term “knowledge graph” were tagged as deprecated, no longer current. I came away wondering if I’d missed something.

Final thoughts

As someone who’s followed enterprise semantic graph evolution since 2009, I can safely say the knowledge graph market has always been slow to develop, despite its obvious potential. KGs are essentially middleware, and are hard to sell for that very reason. Which is precisely why SAP’s strategy, which sidesteps the middleware issue, intrigues me.

The Microsofts and IBMs of the world have understandably have had an on again, off again tendency when it comes to commercial KG platform offerings, and the big consultancies who are their major enterprise partners have had that same tendency. 

My main takeaways? 1) SAP with its process ontology may be homing in on how to get businesspeople in the knowledge graph loop, but they’re not quite there yet. 2) Oracle with its industry ontologies seems well positioned for supply chain integration, a critical use case. Much depends on finding customers willing to commit to managing meaning at graph scale.