Semantic Web and Semantic Technology Trends in 2020

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One way or another, it’s all about graphs. And machine learning. And AI. And what their connections to each other are.

Welcome to the world of semantic technology in 2020. The year ahead largely picks up the pace on what industry experts predicted would happen in 2019. Graph technologies have finally become mainstream, says Andreas Blumauer, founder and CEO, Semantic Web Company and director of PoolParty Software. “This can be seen above all in the fact that they are increasingly being embedded within larger platforms and are virtually disappearing there.”

The stars are beginning to align between the two graph worlds, too. To summarize, Resource Description Framework (RDF) graphs are based on the semantic web and characterized by several inference capabilities (a class is a subclass of another class, and so on), and property graphs provide directed, named, semantically-relevant connections between node entities.

Whatever form they take, graphs are now recognized as a cool way of managing data, says Dean Allemang, Principal Consultant at Working Ontologist. That being the case:

“The idea of sharing graph data on the web will become obvious, and the war between ‘property graphs’ and ‘RDF’ will sort out its rightful territory. You need a web-based graph language to distribute data.” 

There is a greater willingness of the RDF graph and property graph communities to seek constructive dialogue and to bring the two technologies closer together in the medium term, says Blumauer:

“The market will continue to learn and better understand that there are different graph technologies, and that semantic graphs (RDF) and property graphs are not simply interchangeable but are the better and more economically viable alternative in their respective use case,” he said.

A big development on the property graph front this September was that the international committees that develop the SQL standard voted to initiate GQL (Graph Query Language) as a new database query language, said Amit Chaudhry, Vice President, Product Marketing at Neo4j, which implements the property graph model down to the storage level:

“Now to be codified as the international standard declarative query language for property graphs, GQL represents the culmination of years of effort by the broader database community,” he said. “It’s a huge win for the market as practitioners, customers, and vendors will all benefit.”

And in the RDF graph database arena, nearly all of the RDF graph database vendors now support SHACL, a W3C standard for data validation. It also has been quickly adopted in business solutions that are using knowledge graph technologies, said Irene Polikoff, co-founder and CEO of TopQuadrant. “It provides simple-to-use access to the power of knowledge graphs for the mainstream developers.” Overall, 2020 will see the “ungeeking” of the use of semantic technology with more mainstream access and tools.

Graph, Graph Baby

The knowledge graph is seeing broad adoption across industries, said Dr. Jans Aasman, CEO of Franz Inc:

“The big-name Silicon Valley companies (LinkedIN, Airbnd, Apple, Uber) are all building knowledge graphs. But more importantly, Fortune 500 companies, especially banks, are also investing in knowledge graph solutions.”

The entrance of Google into the graph arena this year reflects the importance of graph databases for overall Data Management, both on-premise and in the cloud, Chaudhry said. Skilled graph practitioners are becoming more important, too. “The requirement for skilled graph practitioners is another signal that graph deployments are becoming commonplace,” he said. “Graph was a top skill in 2019 for developers,” who are becoming more influential and valuable to the enterprise, becoming involved in business technology choices in ways they never were before.”

What’s driving the growing adoption? As Polikoff explains it, semantic technology approaches provide a powerful basis for machine learning and artificial intelligence. “The interaction between AI/ML processes and knowledge graphs can be synergistic — a virtuous cycle. New information can be added at any time and merged into the available ‘knowledge’ because it is integrated with the meaning of the information,” she said. So, AI and ML can now be applied at a high-level — that is, to business concerns vs. mostly at the data level:

“Knowledge graphs provide a business model of the organization. The new aggregate of knowledge can, in turn, result in further rounds of productive additions from AI/ML processing.”

There’s more, according to Polikoff:

Knowledge graphs also enable inferencing and rules-based reasoning, which is very helpful in the semi-automation of some Data Governance processes and generation of new knowledge through, for instance, data relationship discovery and exploration.”

IoT gets into the picture too. Aasman points to “digital twins,” which can be thought of as specialized knowledge graphs, as an exceptionally lucrative element of the technology with an applicability easily lending itself to numerous businesses. Its real-time streaming data, simulation capabilities, and relationship awareness may well prove to be the ‘killer app’ that takes the IoT mainstream, he said. As an example, by consuming data transmitted by IoT sensors, digital twins will inform the monitoring, diagnostics, and prognostics of power grid assets to optimize asset performance and utilization in near real-time.

Expect more companies to rebrand themselves as knowledge graph companies — think metadata, database companies, or semantic web companies — and for new ones to show up, “figuring out how to make a product that is an enterprise knowledge graph,” Allemang said. Blumauer’s take is that we’ll see more mergers in the market as the combined use of NLP, machine learning and knowledge graphs — “in short, “semantic AI” — continues to gain acceptance. 

Next on the Semantic Agenda

A new development in the semantic world is “Chunks,” an amalgam of RDF and property graphs inspired by work in cognitive science, said Dave Raggett, W3C lead for Data Activity:

“It focuses on machine learning and blending symbolic and sub-symbolic approaches — that is, graphs plus statistics — and offers a new paradigm for AI that mimics the brain at a level above that of individual neurons.”

Industries are focused on traversing and manipulating graph data, not on deductive logic and formal semantics. Chunks take this further by including statistical information so that query results for cognitive databases are based upon prior knowledge and past experience:

“A statistical approach is also key to dealing with the uncertainty, incompleteness, and inconsistency of real-world data,” he said. “Right now, data scientists spend far too much of their time on preparing clean datasets.”

On another front, experts say that chatbots will continue to gain benefit from semantic technologies. “The automation of chatbots is moving from script-driven to data-driven, based on semantic analysis of the history of consumer interactions,” said Dave McComb, President of Semantic Arts.

“Chatbots in their next generation will be increasingly underpinned with semantics and will finally also become serviceable and mainstream,” agreed Blumauer. He adds that they will play an increasingly important role in more complex recommender systems that go beyond simple similarity search, too.   

Raggett plans to draw more attention over the year to what he thinks will be a very exciting development. Designing ontologies for particular domains involves the direct manual editing of knowledge, and that will become increasingly costly as the number and size of vocabularies rises. As a result:

“The future is likely to see a shift to machine learning of vocabularies and rulesets, changing the role of developers into that of teachers who instruct and assess the capabilities of systems, and monitor their performance,” he said. “The effectiveness of vocabularies and rulesets can be assessed in terms of curated sets of test cases, with the ability for developers to add new cases as needed to match evolving business needs.”

“There’s a great deal of work to be done on that front,” Raggett said. 

Image used under license from Shutterstock.com

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