• Blog
  • Informational

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

How the Semantic Web vision reincarnated in thousands of Linked Open Data datasets and millions of Schema.org tagged webpages. And how it enables knowledge graphs to smarten up enterprises data.

July 29, 2021 12 mins. read Teodora PetkovaAtanas KiryakovAtanas Kiryakov

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. But what exactly are we talking about when we talk about the Semantic Web? And what are the commercial implications of semantic technologies for enterprise data?

The Semantic Web started in the late 90’s as a fascinating vision for a web of data, which is easy to interpret by both humans and machines. One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. While Semantic Web is often condemned for being too academic, two of its incarnations already enjoy massive adoption.

The first one is Schema.org: millions of web pages are tagged with semantic annotations to enable a much better web search experience. The second one is the Linked Open Data (LOD): a cloud of interlinked structured datasets published without centralized control across thousands of servers. Knowledge graphs (KG) came later, but quickly became a powerful driver for the adoption of Semantic Web standards and all species of semantic technology implementing them. KGs bring the Semantic Web paradigm to the enterprises, by introducing semantic metadata to drive data management and content management to new levels of efficiency and breaking silos to let them synergize with various forms of knowledge management. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.

In this post you will discover the aspects of the Semantic Web that are key to enterprise data, knowledge and content management. We will walk you through the Semantic Web roots, the debate about it and further take you beyond its academic and visionary aspect into the world of efficient enterprise data management with semantic technologies and knowledge graphs. Connecting the dots for you between concepts like RDF, semantic annotation, Linked Open Data, we will help you understand why the Semantic Web will always work and how knowledge-intensive domains and applications can benefit from its affordances.

Source: tag.ontotext.com

In 1994 Tim Berners Lee described the Web as “a flat, boring world devoid of meaning” (Plenary Talk Geneva) for computers. To set the stage for a Web more meaningful to our machines, in 1998, a Semantic Web Road Map came into being. After the design of this “architectural plan untested by anything except thought experiments”, in 2001 the Semantic Web seized the public’s imagination with a seminal article featured in Scientific American called: The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities.

Today, 20 years after Tim Berners-Lee, James Hendler and Ora Lassila outlined a Semantic Web driven world where intelligent software agents automatically book flights and hotels and give us personalized answers, we can see Google transitioning from a search engine into a question-answering system and Alexa becoming a device that can book a flight for you. There are more than 80 million pages with semantic, machine interpretable metadata, according to the Schema.org standard. Take this restaurant, for example. Under the hood of its web content lie formalizations describing its address, opening hours, name and other details.

In parallel, we have the Linked Open Data (LOD) cloud, which currently contains 1,301 datasets with 16,283 links – datasets with mappings across them (https://lod-cloud.net/ as of May 2021).

Schema.org and Linked Open Data are just two incarnations of the Semantic Web vision. Those are two massive information domains using W3C’s stack of the Semantic Web standards (RDF, SPARQL, OWL, etc.).

And still there is confusion around the Semantic Web.

What is it? How exactly is the Web semantic? What can it do and how are enterprise knowledge graphs related to it?

Let’s start with the first question.

Which Semantic Web?

Paradoxically, the Semantic Web, which aims to unambiguously describe things, people and concepts, is itself an ambiguous term.

For some, the Semantic Web is about intelligent agents browsing the Web and executing sophisticated tasks. For others, the concept boils down to smart and efficient data management  (Pascal Hiltzer traced the triumphs and challenges of two decades of Semantic Web research and applications in a recent Review of the Semantic Web Field).  For its critics, it is a pipe-dream, too academic to be realized, a never to come true vision (see the perspectives regarding the degree to which the original Semantic Web vision has been realised and the impact it can potentially have on the Web gathered by Aidan Hogan.) Authors Marshal and Shipman, for example, explored the many rhetorical, theoretical and pragmatic perspectives on the Semantic Web, discerning three major threads in them. The Semantic Web, their research showed, is seen as:

 (1) a universal library, to be readily accessed and used by humans in a variety of information use contexts; (2) the backdrop for the work of computational agents completing sophisticated activities on behalf of their human counterparts; and (3) a method for federating particular knowledge bases and databases to perform anticipated tasks for humans and their agents.

17 years later, in the third edition of The Semantic Web for the Working Ontologist, authors Dean Allemang, Hedler and Gandon covered these same perspectives with one distilled explanation:

The Semantic Web faces the problem of distributed data head-on.

In more detail, they explained that just as the hypertext Web changed how we think about the availability of documents, the Semantic Web is a radical way of thinking about data. Its main idea is to support a distributed Web at the level of the data where organizations or individuals don’t just publish a human-readable presentation of information but a distributable, machine-readable description of the data.

We can see all of the above theoretical investigations in practice when it comes to what Tim Berners Lee said once when asked about the Semantic Web: a way to pull data and then pull other data and connect them to see how things fit in.

What Does The Semantic Web Already Do for Us?

If you’ve used Google, you’ve used the cornucopia of Linked data across the Web, through Google’s Knowledge Graph (Google’s Knowledge Graph is reportedly supported by Freebase – the knowledge acquired by Google in 2010.) If you’ve enjoyed the efficiency of rich snippets, you’ve enjoyed the riches schema.org (based on RDF) brings to the world of search since 2011. If you’ve used Wikidata – the structured encyclopedia – you’ve been using a giant RDF knowledge graph, describing about 100 million topics with over 10 billion properties and relationships. That is also one of the sources from which Google’s Knowledge Graph is updated.

Taking a closer look at these applications, we see two main perspectives from which the Web is becoming increasingly semantic.

Weaving the Semantic Web with Semantic Annotations and Linked Open Data

Ontotext was founded in 2000 with the Semantic Web in its genes and we had the chance to be part of the community of its pioneers. We can’t imagine looking at the Semantic Web as an artifact. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. Below, we outline the two directions in which we at Ontotext see and build the Semantic Web.

The two distinct threads interlacing in the current Semantic Web fabrics are the semantically annotated web pages with schema.org (structured data on top of the existing Web) and the Web of Data existing as Linked Open Data.

It is these two important types of data, which, taken together, implement the Semantic Web vision bringing forward innovative ways of tackling data management and data integration challenges. (Read about schema.org and LOD in our Knowledge Hub). In them, we can see context being the enabler of value creation – context built by bringing data pieces together, at web-scale. And that connectivity at data level is what makes the Semantic Web and the technologies related to it such a good solution to the challenges of knowledge management and data integration. Ultimately, for the latest reincarnation of the field: the knowledge graph.

Enterprise Knowledge Graphs and the Semantic Web

Facing the need to manage and analyze information at a previously unforeseen level, organizations began searching for infrastructures that could handle the massivity of available data and provide the means to make sense of this data. In this “data + knowledge” era in the history of creating intelligent systems to integrate knowledge and data at large scale (as Juan Sequeda calls the period from 2000s till now in his “A Brief History of Knowledge Graphs”) knowledge graphs began to emerge as such infrastructures.

Еnabling semantic search, easier and deeper navigation across diverse data, knowledge graphs have become a business-critical element for many enterprises today. Among the largest knowledge graphs are those of Google, IBM, Amazon, Samsung, Ebay, Bloomberg, NY Times. Most of those first big knowledge graphs are used in web-to-consumer applications where a single graph serves a wide variety of clients based on non-proprietary information.

Enterprise knowledge graphs came as a second wave to serve a different purpose – they use ontologies to make explicit various conceptual models (schemas, taxonomies, vocabularies, etc.) used across different systems in the enterprise. Using the enterprise data management slang, knowledge graphs represent a premium sort of semantic reference data: a collection of interlinked descriptions of entities – objects, events or concepts (see our definition of knowledge graphs).

Providing a formal unified conceptual model, ontologies enable unified access to and correct interpretation of diverse information and greatly facilitate analytics, decision making and knowledge re-use. The most advanced enterprise knowledge graphs smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment. Such knowledge graphs deliver not only “operational optimizations”, but help organizations combine their proprietary wisdom and information with rich domain knowledge and get a competitive advantage in dynamic environments. And while not all knowledge graphs (see Adoption of Knowledge Graphs, late 2019) are built the semantic modelling way, they all have benefited from the Semantic Web. This is either because they use RDF or because, to a different extent, they’ve used Linked Data to broaden the scope of what the graph “knows”.

The “know more thread” is central to understanding the rapid adoption of semantic technologies for knowledge graphs. This is because for a system to “know” more it is essential to have broader knowledge. Such broader knowledge is unattainable by any single organization. No one company on the planet can build the ultimate knowledge graph. As Amit Sheth explained, today’s knowledge graphs needed for Google Semantic Search or Amazon Alexa seem to be built, as a rule of thumb, with at least 10,000 pairs of hands.

It is exactly that ability to derive knowledge by interconnecting data that turns the building and maintaining of an enterprise knowledge graph into an activity of building a competitive advantage. The more connected the data (Linked Data), the more knowledge the enterprise knowledge graph is infused with. And it is that knowledge (enabled by the technologies of the Semantic Web, specifically by RDF) that gives a competitive advantage to the company building the graph.

The major added value of knowledge graphs is the paradigm for using ontologies, explicit formal conceptual models, to put together data scattered across different systems. The key characteristic is that ontologies can capture, integrate and operationalize knowledge across several disciplines and type of systems:

  • Database schemas;
  • Master and reference data (critical in enterprise data management);
  • Taxonomies and controlled vocabularies (critical in content management and knowledge management);
  • Scientific data;
  • Product catalogues.

Epilogue: Why the Semantic Web Will Always Work

In any of its aspects, for us and our clients, the Semantic Web will always work, by incessantly providing the necessary technologies for granular, detailed and well-described semantic metadata. The richness of RDF is expressive enough to be able to put them together and work together. That’s the genius of RDF – as a way of presenting data and metadata, it’s a good fit for all these things at once. The RDF data model and the other standards in W3C’s Semantic Web stack (e.g., OWL and SPARQL) enable the use of these knowledge models as hubs for:

  • integration of data across different systems, without collisions;
  • virtualized or federated access to data residing in different systems;
  • data unification and fusion;
  • providing alternative views on top of a single dataset;
  • management of diverse structured metadata.

In a nutshell, summarizing the enabling features of these standards, it is global identifiers that facilitate  interoperability, formal semantics that brings explicit common meaning and validation (SHACL/RDF Forms) that leads to high data quality.

Despite the widespread belief that knowledge graphs help enterprises find content easier and deal with heterogeneous data more efficiently (which they do), the biggest driver for the enterprises to build KG is to get  better insights and competitive advantage, smartening up their proprietary information by using global knowledge as context for interpretation and source for enrichment.

In knowledge-intensive domains and applications, which require highly interconnected reference data and building complex relationships between them, knowledge graphs help enterprises get profound insights via linking, analysis and exploration of diverse databases, content, proprietary and global data.

And all that mesh of data, or data fabrics as you might see it referred to, wouldn’t have been possible if it weren’t for the affordances of the Semantic Web to connect data in knowledge graphs in order to derive value from it.

Are you ready to learn more about the reincarnation of the Semantic Web – the knowledge graph?

Listen to our webinar: Knowledge Graph Maps: 20+ Application and 30+ Capabilities, which focuses on enterprise knowledge graphs as hubs for data, metadata and content offering unified views to diverse information.

New call-to-action

Article's content

Marketing Expert at Ontotext

Teodora is a philologist fascinated by the metamorphoses of text on the Web. Curious about our networked lives, she explores how the Semantic Web vision unfolds, transforming the possibilities of the written word. From 2022 on, Teodora helps with the creation and curation of the Ontotext knowledge graph to foster information ecology out of marketing content that will enable relevant user experiences across Ontotext's universe.

Atanas Kiryakov

Atanas Kiryakov

CEO at Ontotext

Atanas is a leading expert in semantic databases, author of multiple signature industry publications, including chapters from the widely acclaimed Handbook of Semantic Web Technologies.

Do Large Language Models Dream of Knowledge Graphs – Impressions from Day 2 At SEMANTiCS 2023

Read our report from Day 2 of SEMANTiCS 2023 to find out if ChatGPT is the killer app for the Semantic Web, how do we tame the genie of LLMs for Healthcare and more

Can LLMs Become Knowledgeable – Impressions from Day 1 At SEMANTiCS 2023

Read about the interplay between LLMs & KGs and how business and academia tackle them in our report from Day 1 at SEMANTiCS 2023

It’s Time We Give Each Other More Data Spaces: Impressions from the Pre-conference Day at SEMANTiCS 2023

Read about SEMANTiCS pre-conference day, which covered the topics of interoperability, ESG data, knowledge engineering, scholarly communication, and academia & industry collaboration.

GraphDB in Action: Navigating Knowledge About Living Spaces, Cyber-physical Environments and Skies 

Read about three inspiring GraphDB-powered use cases of connecting data in a meaningful way to enable smart buildings, interoperable design engineering and ontology-based air-traffic control

Your Knowledge Graph Journey In Three Simple Steps

A bird’s eye view on where to start in building a knowledge graph solution to help your business excel in a data-driven market

GraphDB in Action: Putting the Most Reliable RDF Database to Work for Better Human-machine Interaction

Read about the world of academia research projects that use GraphDB to meet the challenges of heterogeneous data across various domains

Knowledge Graphs for Retail – Connecting People, Products and Platforms

Read about how knowledge graphs can serve the retail industry’s growing need to connect, manage and utilize data efficiently, aligning it in a collaborative data ecosystem

Data Wants To Be Truly Sovereign: Designing Data Spaces with Linked Data Principles In Mind

Read about what data spaces are and how semantic technologies and Linked Data can make them a stronger and safer mechanism for commercial data exchange

GraphDB in Action: Powering State-of-the-Art Research

Read about how academia research projects use GraphDB to power innovative solutions to challenges in the fields of Accounting, Healthcare and Cultural Heritage

KGF22: Knowledge Graphs and The Not So Quiet Cognitive Revolution

Read about Ontotext’s KGF22 days dedicated to stories about knowledge graphs in the domains of Industry, Healthcare & Life Sciences and Financial Services

KGF22: Wittgenstein, Developers Empathy and Other Semantic Data Considerations

Read about our event report from Ontotext’s Knowledge Graph Forum 2022, highlighting expert insight on building knowledge graphs and designing enterprise-grade solutions with semantic technologies.

A Little SEMANTiCS Goes A Long Way

Take a sneak peek at some of the keynote speeches and tutorials throughout SEMANTiCS 2022

It Takes A Village To Raise An Enterprise Knowledge Graph

Read about the design processes behind crafting knowledge-graph enabled solutions and explore some of the stories of our partners.

Smart Buildings Are Built of Smart Data: Knowledge Graphs for Building Automation Systems

Read about how knowledge graphs offer a sustainable solution for harnessing and making sense of heterogeneous data in the building automation industry.

Metadata Moves: Knowledge Graph Technology for Logistics

Read about how the world of metadata humming behind the logistics and other supply chain processes can benefit from using knowledge graph technology.

Electrical Standards, Smart Grids and Your Air Conditioner

Read about how applying Linked Data principles and semantic technology to electricity data can make for a more efficient, reliable and sustainable electricity market.

The Semantic Web: 20 Years And a Handful of Enterprise Knowledge Graphs Later

Read about how the Semantic Web vision reincarnated in thousands of Linked Open Data datasets and millions of Schema.org tagged webpages. And how it enables knowledge graphs to smarten up enterprises data.

Metadata is Like Packaging: Seeing Beyond the Library Card Metaphor

Read about what metadata is, why it is important and how it can enhance the ways information flows across the enterprise.

From Fragmented Data to a Comprehensive Knowledge Graph: The Case for Building an R&D Repository

Read about how enterprise knowledge graphs can unlock meaning and thus create a smart future-proof living repository of scientific data and its relationships.

Texts Without Pages: Advancing Text Analytics with Content Enrichment

Read about how text analytics can be brought forward with content enrichment processes for better text authoring, delivery and navigation.

A Shield Built of Connected Data: Knowledge Graphs Meet Cybersecurity

Read about how a knowledge graph can help organizations stay vigilant of the increasing number of cyber threats, keeping malicious attacks at bay with the help of semantics.

Digital Twins: If It Sounds Like Cyberpunk, It’s Because It Is

Read about what digital twins are, what makes them attractive to companies and how digital twins relate to semantic technology and enable organizations to design, simulate and validate various scenarios virtually.

Eating the Knowledge Soup, Literally

Read about the fluid essence of knowledge and the capability of knowledge graphs to power an information-rich platform of diverse facts about anything, a broccoli soup included.

If Curiosity Cabinets Were Knowledge Graphs

Read about why and how knowledge graph technology can help build networks of interwoven digital objects in the world of cultural heritage.

On the Hunt for Patterns: from Hippocrates to Supercomputers

Read about the ExaMode project that will help medical professional use the power of supercomputers and knowledge graphs for more efficient patient care through data-driven diagnoses.

Crafting a Knowledge Graph: The Semantic Data Modeling Way

Read about how to build a knowledge graph the semantic data modeling way in 10 steps, provided by our knowledge graph technology experts.

A Graphful of Investment Opportunities

Read about the story of an algorithm that mines data to narrow down opportunities for investing.

Okay, You Got a Knowledge Graph Built with Semantic Technology… And Now What?

Read about how knowledge management can be made smarter using a knowledge graph built with semantic technology.

If Johnny Mnemonic Smuggled Linked Data

Read about how semantic technology and Linked Data can help enterprises benefit from smart data management and retrieval practices.

Data, Databases and Deeds: A SPARQL Query to the Rescue

Read about why and how SPARQL queries make for a better search in diverse datasets across an organization in an integrated way.

Semantic Technology and the Way We See the World

Read about how semantic technology can help you set the wheels for many processes related to еfficient data management and governance.

Telling Stories with an RDF Database

Read about the opportunities for authoring and publishing workflows opened by an RDF triplestore.

The Power of URI or Why Odysseus Called Himself Nobody

Read about URI and its power to enable the sharing and reuse of machine-readable data with minimum integration costs.

From Cultivating Nature to Cultivating Data: Semantic Technology and Viticulture

Learn how the potential that Big Data streams bring to grape and wine production can be harnessed with the right kind of technology.

The Knowledge Graph and the Enterprise

Read about the knowledge graph and about how many enterprises are already embracing the idea of benefiting from it.

It Don’t Mean a Thing If It Ain’t Got Semantics

Learn how you can turn data pieces into actionable knowledge and data-driven decisions with an RDF database.

The Bounties of Semantic Data Integration for the Enterprise

Learn about the potential semantic data integration carries for piecing massive amounts of data together.

Here’s a Graph, Go Figure! Coupling Text Analytics with a Knowledge Graph

Learn why and how a Knowledge Graph boosts significantly Text Analytics processes and practices and makes text work for us in a more meaningful way.

Cognitive Computing: Let’s Play an Awareness Game

Read about the new breed of computing is emerging before our eyes – cognitive computing and join us in our Awareness Game.

Machine Learning and Our (Insatiable) Penchant for Making Things Smarter

Read about how machines can be of great help with many tasks where fast and error-free computation over big amounts of data are required.

Staying In the Vanguard of Digital Transformation with Open Data

Learn about Open Data and its potential to be part of smart solutions to data problems and innovative products and services.

Whose Meaning? Which Ontology?

Read about how ontologies open up opportunities for a new class of tools to power information consumption and knowledge management.

Shiny Happy Data: A Praise for RDF

Learn how to choose the right solution for working with your data the conceptual framework of “happy connected people”.

Enterprise Metadata Matters: From Having Data to Acting Upon Them

Learn more about the importance of being metadata-driven today in our latest SlideShare presentation.

Data Daiquiri: The Power of Mixing Data

Learn how companies can tap into the power of the data coming their way by integrating the huge data flows with their proprietary data.

Migrating to GraphDB: Your Why and How in 20 slides

Learn about the steps you need to migrate your data to GraphDB to use it as a smart brain on top of your legacy systems.

Got meaning? Or Why an RDF Graph Database Is Good for Making Sense of Your Data

Read about how you can create systems capable of discovering relationships and detecting patterns within all kinds of data.

Brains Armored with Smart Data

Read our thoughts rising from questions such as “Will Giant Brains Rule the World?” and “Can a mechanical brain replace you?”

One Step Closer to Intertwingularity: Semantic Metadata

Learn about how semantic metadata allows us to add granularity to an object, interlink it to other objects and make it easy to search.

Exceptional User Experiences with Meaningful Content NOW

Content enrichment and semantic web technologies are key to efficient content management. Learn why and see these technologies in action.

Semantic Information Extraction: From Data Bits to Knowledge Bytes

Learn about semantic information extraction and how it pulls out meaningful data from textual sources, ready to be leveraged for insights, decisions and actions.

Weaving Data Into Texts: The Value of Semantic Annotation

Read about how semantic annotation links certain words to context and references that can be processed by an algorithm.

Exploring Linked Open Data with FactForge

Learn about FactForge and how you can turn the opportunities that data flows on the web can pour into our business into a real experience.

What is GraphDB and how can it help you run a smart data-driven business?

Learn about GraphDB in a simple and easy to understand way and see what Ontotext’s semantic graph database has to do with pasta making.

Linked Data for Libraries: Our New Librarians

Learn how semantic technologies can bring audiences back to libraries and make library archives and collections visible and accessible.

Working with Data Just Got Easier: Converting Tabular Data into RDF Within GraphDB

Read about OntoRefine – a new tool that allows you to do many ETL (extract, transform and load) tasks over tabular data.

GraphDB: Answers for Kids of All Ages

Read about how GraphDB can help you clean up messes of data (just like your room) – whether you are a kid or not.

The Knowledge Discovery Quest

Learn how by joining the dots, semantic search enhances the way we look for clues and compare correlations on our knowledge discovery quest.

Connectivity, Open Data and A Bag of Chips

Learn how LOD’s connectivity allows data to be shared seamlessly, used and reused freely. As simple as a bag of chips.

Data Integration: Joining the Data Pieces of Your Business Puzzle

Learn how to use information interconnectedness to integrate, interpret and ultimately make sense of data.

Cooking Up the Semantic Web

Read about the Semantic Web and what it takes to reach its potential and evolve from a Web of Documents to a Web of Data.

Semantic Search: The Paradigm Shift from Results to Relationships

Read about semantic search and how it takes information retrieval to the next level and puts information at our fingertips.

A Web of People and Machines: W3C Semantic Web Standards

Learn how and why Semantic Web Standards are to serve the Web of Data for better collaboration between people through computers.

Thinking Outside the Table

Learn how to manage highly connected data, working with complex queries and having readily available relationships, without the need to express them explicitly.

Our Networked Lives, Publishing and Semantic Technologies

Read about how semantic technology helps publishing handle data in an interconnected way, attaching machine-processable and readable meaning to them.

Why Graph Databases Make a Better Home for Interconnected Data Than the Relational Databases?

Read about how you can turn data into a resource, easily accessed and effectively used across the organization with a graph database.

Text, Data and the Roman Roads: Semantic Enrichment

Read about semantic enrichment and the unique opportunity it offers for interconnecting objects to facilitate knowledge discovery.

4 Things NOW Lets You Do With Content

Go beyond conventional publishing with Ontotext’s News On the Web and get the feel of how you can discover and consume content with semantic technology.