This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Have you ever been in a conversation where someone mentioned a “knowledgegraph,” only to realize that their description was completely different from what you had in mind? What is a knowledgegraph? Just a few years ago, a harmless mix-up like this one would hardly catch anyone’s attention.
If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. Before we start, I have a few questions for you. Can you find them all?
The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledgegraph 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?
was very unlikely to bring anything meaningful, notes Phil Lewis in Smarter enterprise search: why knowledgegraphs and NLP can provide all the right answers. It is tempting to imagine a future where a knowledgegraph powered semantic search will become so sophisticated that we could even ask: “Is soup one of life’s great mysteries?”
Graph Databases vs Relational Databases. With graph databases the representation of relationships as data make it possible to better represent data in real time, addressing newly discovered types of data and relationships. Not Every Graph is a KnowledgeGraph: Schemas and Semantic Metadata Matter.
The role of knowledgegraphs in AECO transformation At present, knowledgegraphs are the best-known technology capable of offering decentralized ways of going beyond existing data silos. Introduction Unlike our not-so-distant hunter-gatherer ancestors, today most of us live in a built environment.
Stop wasting time building data access code manually, let the Ontotext Platform auto-generate a fast, flexible, and scalable GraphQL APIs over your RDF knowledgegraph. Are you having difficulty joining your knowledgegraph APIs with other data sources? If so, STOP and give Ontotext platform a try.
Data fabric enthusiasts assert that the design pattern is much more than that and reference one or more emerging data analytics tools: AI augmentation, automation, orchestration, semanticknowledgegraphs, self-service, streaming data, composable data analytics, dynamic discovery, observability, persistence layer, caching and more.
Public trust in this process and its fairness is among the most important ingredients of the social capital that makes the democracy function effectively. A consistent electoral process is at the foundation of every modern democracy. The data shared by the CEC represents the election process at the most granular level.
The term “knowledgegraph” (KG) has been gaining popularity for quite a while now. Today, as the number of decision-makers recognizing the importance of more dynamic, contextually aware and intelligent information architectures is growing, so is the number of companies with solutions based on knowledgegraphs.
In this blog post, we will highlight how ZS Associates used multiple AWS services to build a highly scalable, highly performant, clinical document search platform. ZS is a management consulting and technology firm focused on transforming global healthcare.
Firstly, we aren’t agreed on what intelligence actually is, natural or artificial. The most famous test for artificial intelligence, the Turing Test, is little more than a process where, after asking a series of questions, a person decides if the thing they are interacting with seems intelligent or not. A ‘computer’ used to be a job title.
In this article, we argue that a knowledgegraph built with semantic technology (the type of Ontotext’s GraphDB) improves the way enterprises operate in an interconnected world. Okay, You Got a KnowledgeGraph Built with Semantic Technology… And Now What? Why a KnowledgeGraph?
In sixteenth and seventeenth century Europe, humans’ never-ending need for knowledge and insatiable curiosity manifested in what was first labeled as Wunderkammers (cabinets of curiosities). But is such digital representation of objects and artifacts enough to help us satiate our need for knowledge?
Data architecture is an important discipline for understanding data and includes data, technology and infrastructure design. These include conceptual, logical, physical, hierarchical, knowledgegraphs, ontologies, taxonomies, semantic models and many more. The Value of Data Architecture. Relational data modeling, 1970s.
Metadata, our CEO Atanas Kiryakov told me, in a brief conversation about Ontotext’s knowledge management solutions , is for data as packaging is for goods. Metadata, our CEO Atanas Kiryakov told me, in a brief conversation about Ontotext’s knowledge management solutions , is for data as packaging is for goods. The one from packaging.
Seen through the three days of Ontotext’s KnowledgeGraph Forum (KGF) this year, complexity was not only empowering but key to the growth of knowledge and innovation. Content and data management solutions based on knowledgegraphs are becoming increasingly important across enterprises.
I learned that fact from a comment in the audience on the second day of SEMANTICS 2023 – the European conference series focused on semantic technologies ever since 2005. What If ChatGPT Is the Killer App for the Semantic Web? The last day of SEMANTiCS started as excitingly as all the rest.
In the same way, it’s good to know what you need to have in place to feel all the benefits knowledgegraphs can bring. Why You need to make sure the people in your organization know the why of the knowledgegraph project. But while all those are important, they are not the be-all and end-all.
So, we started this series by introducing knowledgegraphs & their application in data management and how to reason with big knowledgegraphs & use graph analytics. This post continues our series aiming to provide the bigger picture of what we do and how our webinars fit into it. Mapping UI.
Next month marks the twelfth edition of our live online training Designing a Semantic Technology Proof-of-Concept. But it has enriched us in terms of identifying key needs for those looking to build a simple prototype in order to demonstrate the power of semantic technology, linked data and knowledgegraphs.
Where once people would confide in divine oracles, golems, or fairies, today we trust our search platforms to digest encyclopaedic knowledge and make it easily available. It is a great human-machine interface, and an awe-inspiring creative co-pilot, less so a reliable storage of knowledge.
Similarly, while creating this blog post, I’m given suggestions that aim to help complete my sentences via a “Smart Compose” function. Sisense News is your home for corporate announcements, new Sisense features, product innovation, and everything we roll out to empower our users to get the most out of their data. AI throughout is in our DNA.
Well, it’s all thanks to knowledgegraphs. Knowledgegraphs are changing the game A knowledgegraph is a data model that uses semantics to represent real-world entities and the relationships between them. For many of us that is already happening the moment we enter our cars.
One of our clients is a major children’s hospital in the Midwestern United States. Apart from being a large medical service provider, the institution is also a major research center with several hundred faculty members engaged in a variety of scientific activities beyond their medical duties. The Stumbling Blocks of Tracking Research Activities.
Knowledgegraphs have been proven to be a powerful, scalable and intelligent technology for solving today’s complex business needs. The ability to define the concepts and their relationships that are important to an organization in a way that is understandable to a computer has immense benefits.
Through this series of blog posts, we’ll discuss how to best scale and branch out an analytics solution using a knowledgegraph technology stack. Through this series of blog posts, we’ll discuss how to best scale and branch out an analytics solution using a knowledgegraph technology stack. But with robots.
Organizations that invest time and resources to improve the knowledge and capabilities of their employees perform better. Staff turnover is the most obvious reason, but it might also be because management has new priorities resulting in skills and knowledge developed previously degrading. The Romans perfected the recipe around 150 BCE.
Guillaume : At the heart of Ontotext solutions lies what we call a knowledgegraph. Why do you think knowledgegraphs are the best way to access knowledge? What makes them so efficient? Guillaume : Perfect. Now, let’s go a little deeper into this topic. Doug : Right.
Fine Tuning Studio Fine tuning has become an important methodology for creating specialized large language models (LLM). RAG with KnowledgeGraph Retrieval Augmented Generation (RAG) has become one of the default methodologies for adding additional context to responses from a LLM. To view a demo, watch this vi deo.
Data Meaning is Critical It is important to note that LLMs are just ‘text prediction agents.’ That means they are highly dependent on the quality of the knowledge base (data) being used as input. The meaning of the data is the most important component – as the data models are on their way to becoming a commodity.
To stay up to date with all the novelties in their fields and to gain knowledge and insights from the huge and disparate data sources, Pharma companies are one of the first ones to turn to intelligent data management solutions. Modern medicine and the Pharmaceutical industry have made tremendous breakthroughs over the past few centuries.
A graph is like a map that represents real-life objects and the relationships between them. While many of us use Google, Twitter, Alexa and Siri, likely most don’t know (or think about) that they are powered by knowledgegraph technology. The descriptions of these entities have a specific structure and meaning (semantics).
This year’s pre-conference day of SEMANTiCS – the annual European conference on semantic technologies , organized by our partners at the Semantic Web Company (SWC), was dedicated to DBpedia. Curry listed knowledgegraphs, search, and language models as the main pillars of the evolution of data and data spaces.
Not surprisingly, the last decade has witnessed a paradigm shift in enterprise data management, leading to a rise in leveraging knowledgegraphs. Not surprisingly, the last decade has witnessed a paradigm shift in enterprise data management, leading to a rise in leveraging knowledgegraphs.
An embedding model, for instance, could encode the semantics of a corpus. By searching for the vectors nearest to an encoded document — k-nearest neighbor (k-NN) search — you can find the most semantically similar documents. Let’s say you want to buy a couch in order to spend cozy evenings with your family around the fire.
It is important to remember that in an age where new technologies can go from cult usage to widespread adoption with astonishing rapidity that a Data Fabric aims to orchestrate existing and future data services rather than replace existing infrastructure. KnowledgeGraphs are the Warp and Weft of a Data Fabric.
To get the most out of Ontotext Platform and its use of GraphQL, your organization should expose a single knowledgegraph. And when Ontotext Platform’s Semantic Objects are combined with yours, we shall have an army greater than any in the galaxy. KnowledgeGraph Training. The Jedi will be overwhelmed.
The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context.
These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. The entire history and practice of modern medicine, argues Fry, is built on finding patterns in data. What are supercomputers and why do we need them?
In our previous blog posts of the series, we talked about how to ingest data from different sources into GraphDB , validate it and infer new knowledge from the extant facts as well as how to adapt and scale our basic solution. And the LAZY system from our previous blog posts is at the threshold of that important step.
What is the future of knowledgegraphs in the era of ChatGPT and Large Language Models? Atanas Kiryakov: Knowledgegraphs will prosper in the ChatGPT era. LLM will not replace knowledgegraphs either. It’s important to realize that knowledgegraphs can be used to fine tune and customize LLMs.
Knowledgegraphs represent a collection of interlinked descriptions of concepts and entities. Humans are stuck on this planet until they devised a way to travel, 11kms per second, the escape velocity from Earth. It’s only been very recently that humans figured out how to go that fast. It’s hard and it’s expensive.
Graph technologies are essential for managing and enriching data and content in modern enterprises. But to develop a robust data and content infrastructure, it’s important to partner with the right vendors. We offer a seamless integration of the PoolParty Semantic Suite and GraphDB , called the PowerPack bundles.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content