For the past decade or so, Knowledge Graphs have been sneaking into our daily lives, be it through voice assistants (such as Alexa, Siri or Google Assistant), intuitive search results or even personalized shopping experiences through online store recommenders. We are constantly interacting with Knowledge Graphs on a daily basis. However, Knowledge Graphs and underlying graph databases are still a mystery to most and because of its seamless entrance into our lives, most of us aren’t even aware of how dependent we are on the technology – or worse, how we have come to expect a certain quality and standard that we are now accustomed to.
Many organizations are already using Knowledge Graph technology to help themselves stay ahead of the game. And Knowledge Graphs and graph databases have been in use for all types of industries, ranging from banking, the auto industry, oil and gas to pharmaceutical and health, retail, publishing, the media and more. Although these companies use Knowledge Graphs for different use cases, the end game is the same: taking large amounts of data from various data silos and adding value to it so that it can be used (and ultimately re-used) in a meaningful and more intelligent way.
The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface
Gartner (2018): “Hype Cycle for Artificial Intelligence”
What is a Knowledge Graph?
A Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. It provides a structure and common interface for all of your data and enables the creation of smart multilateral relations throughout your databases. Structured as an additional virtual data layer, the Knowledge Graph lies on top of your existing databases or data sets to link all your data together at scale – be it structured or unstructured.
What is the difference between a Knowledge Graph and a Graph Database?
Knowledge graphs are data. They have to be stored, managed, extended, quality-assured and can be queried. This requires databases and components on top, which are usually implemented in the Semantic Middleware Layer. This ‘sits’ on the database and at the same time offers service endpoints for integration with third-party systems.
Thus graph databases form the foundation of every knowledge graph. Typically, these are technologies based either on the Resource Description Framework (RDF), a W3C standard, or on Labeled Property Graphs (LPG).
In order to roll out knowledge graphs in companies, however, more than a database is required: Only with the help of components such as taxonomy and ontology editors, entity extractors, graph mappers, validation, visualization and search tools, etc. can it be ensured that a knowledge graph can be sustainably developed and managed. While graph databases are typically maintained by highly qualified data engineers or Semantic Web experts, the interfaces of the Semantic Middleware also allow people to interact with the knowledge graph who can contribute less technical knowledge instead of business and expert knowledge to the graphs.
How is an Enterprise Knowledge Graph different than Google’s Knowledge Graph?
Knowledge Graphs are all around: Facebook, Microsoft, Google, all of them operate their own Knowledge Graphs as part of their infrastructure. Google introduced in May 2012 its own version and interpretation of a Knowledge Graph. Since then the notion of a ‘Knowledge Graph’ got more and more popular. On the surface, the information from the Google Knowledge Graph is used to augment search results.
On top of that, the Google Knowledge Graph also enhances its Artificial Intelligence (AI) when answering direct spoken questions in Google Assistant and Google Home voice queries. Behind the scenes and in return, Google uses its Knowledge Graph to improve its machine learning algorithms.
But Google’s Knowledge Graph is quite limited in how users and software agents can interact with it. It only covers a few areas of industry-specific knowledge, and it doesn’t cover the internal knowledge of companies. Its API returns only individual matching entities, rather than graphs of interconnected objects.
This is where Enterprise Knowledge Graphs come into place. Enterprise Knowledge Graphs help companies create their specific web of knowledge representing their very own domain. As a result, they can seamlessly break down data silos to use information assets in an agile way. Furthermore, it is a cost-efficient solution that does not replace but boosts existing IT systems. Enterprise Knowledge Graphs fulfill today’s requirements to process real-time sources of information and retrieve knowledge from data stored in disparate systems.
Four reasons an Enterprise Knowledge Graph can Help You
1. Combine Disparate Data Silos
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2. Bring Together Structured and Unstructured Data
Accumulating data doesn’t mean just assembling documents and excel sheets. Knowledge Graph technology means being able to connect different types of data in meaningful ways and supporting richer data services than most knowledge management systems. Organizations will then use this technology to extract and discover deeper and more subtle patterns with the help of AI and Machine Learning technology.
3. Make Better Decisions by Finding Things Faster
Even prior to computers, looking for information meant digging through piles of documents to find a particular sentence, or number, etc. that is valuable to your train of thought. Using Knowledge Graph technology mitigates this by giving you more enriched and in-depth search results, helping to provide relevant facts and contextualized answers to your specific questions, rather than a broad search result with many (
4. Future Proof your Database with Standards
Most enterprises working towards their AI strategy assume that external vendors are able to create a black box that funnels their data into an intelligent Knowledge Graph. This also means that these enterprises are highly dependent on external services and are unaware of how their machines are making decisions. Without quality data, it is impossible to get quality knowledge. With an Enterprise Knowledge Graph in place, organizations will benefit from higher reusability of their data, when managing data models because their Knowledge Graphs are compliant with W3C standards. This triggers, not only internal network effects, but it also allows for the re-use of publicly available industry graphs and ontologies (e.g., FIBO, CHEBI, ESCO, etc.), as well as the ISO standard for multilingual thesauri. This also ensures that you are fully in control of your Knowledge Graph since everything is maintained in-house.
Four Examples of Knowledge Graph Implementations
One of the top 20 companies in the pharmaceutical industry uses the extensive capabilities of Enterprise Knowledge Graphs to provide a unified view of all their research activities.
IT & IT Services
A large IT services enterprise uses Enterprise Knowledge Graphs to help them link all unstructured (legal) documents to their structured data; helping the enterprise to intelligently evaluate risks that are often hidden in common legal documents in an automated manner.
A global telecom company benefits from the power of Enterprise Knowledge Graphs, helping to generate chatbots based on semi-structured documents.
A large governmental organization provides trusted health information for their citizens by using several standard industry Knowledge Graphs (such as MeSH and DBPedia etc.). The governmental health platform links more than 200 trusted medical information sources that help to enrich search results and provide accurate answers
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