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Motivated by our marketing team’s aim to simplify content discovery on our website, we initiated the OntotextKnowledgeGraph (OTKG) project. We envisioned harnessing the power of our products to elevate our entire content publishing process, thereby facilitating in-depth knowledge exploration. What is OTKG?
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? Everything gets fed in and out of the semantic knowledgegraph. What is the relationship between AI and knowledgegraphs?
Ontotext Platform 3.0 features significant technology improvements to enable simpler and faster graph navigation, including GraphQL interfaces to make it easier for application developers to access knowledgegraphs without tedious development of back-end APIs or complex SPARQL.
At Graphwise, we aim to make knowledgegraph construction faster and more cost-effective. Therefore we explored how GenAI could automate several stages of the graph-building pipeline. Named Entity Recognition (NER) is a foundational step in knowledge extraction and a critical task for knowledgegraph construction.
Consequently, many data leaders today are striving to overcome these barriers by streamlining their enterprise knowledge management processes and practices. The knowledgegraph model is one way of doing it and, not surprisingly, it has been in increasing demand in the last decade. Why Enterprise KnowledgeGraphs?
This is where PubMiner AI comes to help such interdisciplinary teams of biomedical researchers and data scientists in their journey to knowledge extraction. Through AI-powered insights, graphical representations, and customizable queries, they can leverage this approach to transform vast amounts of medical data into actionable knowledge.
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.
In the last couple of years, we’ve had so many Ontotext webinars on interesting topics, attended by an increasing number of people, asking more and more questions that we’ve decided to start a new series of blog posts dedicated to them. We can address many of their challenges with our products – GraphDB and Ontotext Platform.
This post continues the series of posts we started with At Center Stage: 2 Ontotext Webinars About Reasoning with Big KnowledgeGraphs and Power of Graph Analytics. We want to give you the bigger picture of what we do and where Ontotext webinars fit into it – just a couple of webinars at a time. Why is that?
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?”
Ontotext has offered semantic technology training for nine years. A great new course that follows this model is our Graph RAG Training , where you can learn how knowledgegraphs can enhance the use of large language models for more accurate, contextual question answering.
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. What can it do and how are enterprise knowledgegraphs related to it? Source: tag.ontotext.com. What is it?
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. They enable the interlinking of various data sources and provide deeper insights, considering multiple points of interest.
One of the limitations of ChatGPT is its lack of understanding of the context and background knowledge of the text it generates. One way to overcome these limitations is by training the ChatGPT language model with data from KnowledgeGraphs. However, like any machine learning model , it has its limitations.
KnowledgeGraphs give AI context to solve problems and make decisions. Within Ontotext’s GraphDB there are knowledgegraphs to represent data but also its meaning. This is how data and information are transformed into the ‘knowledge’ in knowledgegraphs.
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 the previous post, we talked about data virtualization and how Ontotext’s RDF database for knowledgegraphs GraphDB provides the tools for the full journey from graphs to relational tables and back. This post continues our series in which we want to provide an overview of what we do and how our webinars fit into it.
So, we started this series by introducing knowledgegraphs & their application in data management and how to reason with big knowledgegraphs & use graph analytics. Ontotext Answer: Indeed, we are. The Ontotext reconciliation service is in development and would be based on Elasticsearch indexing.
This data comes from both private and public sources and in structured and unstructured formats, making it difficult to create a unified, queryable view of existing knowledge. A knowledgegraph-based approach solves this problem by unifying the information within an intuitively linked structure. See figure 1.).
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.
Seen through the three days of Ontotext’sKnowledgeGraph 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.
ONTOTEXT ANSWER: As with most serious matters, the question of security is not simple to answer. As GraphDB handles a knowledgegraph, many users would like to have security access over graphs or even particular triples. So, when you are uploading data, can you use data in other graphs for inference?
For these reasons, we have applied semantic data integration and produced a coherent knowledgegraph covering all Bulgarian elections from 2013 to the present day. In the back-end the data is hosted in Ontotext’s GraphDB engine. The post 5-Star Linked Open Elections Data appeared first on Ontotext. The road ahead.
Ontotext Platform synergizes knowledgegraphs and text analysis as follows: Knowledgegraphs can improve text analysis performance. Big knowledgegraphs provide rich semantic profiles of all the popular concepts in a given domain and allow those to be more accurately recognized in text.
In the past year, knowledgegraphs topped the curve of the Gartner Hype Cycle for Artificial Intelligence, and graph database vendors raised more than half a billion dollars in venture capital funding. It’s safe to say knowledgegraphs have entered the spotlight. Drawing More Value Out of Existing Data.
Knowledgegraphs have been proven to be a powerful, scalable and intelligent technology for solving today’s complex business needs. Gartner’s Hype Cycle of Artificial Intelligence 2020 , includes Ontotext as one of the exemplar vendors list for knowledgegraphs. The aspiration behind Ontotext Platform 3.3
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?
ONTOTEXT ANSWER: You probably saw our performance comparison on the topic and are wondering if more performant is always better. As an abstract knowledge representation model, it does not differentiate between data and metadata. In a property graph, the connection is a concept in itself. Named Graphs.
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. You need to be able to answer these questions before you can start a knowledgegraph project.
As knowledgegraphs have made their way into more and more areas of our lives through smarter search and immersive information navigation, they are also gradually turning into the default way we expect to interact with knowledge. You can read in detail how the EOKG was built in: European Olfactory KnowledgeGraph, version 1.)
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. Read our post: Okay, You Got a KnowledgeGraph Built with Semantic Technology… And Now What?
KnowledgeGraphs are the Warp and Weft of a Data Fabric. Connecting the data in a graph allows concepts and entities to complement each other’s description. Given a critical mass of domain knowledge and good level of connectivity, KG can serve as context that helps computers comprehend and manipulate data.
Through this series of blog posts, we’ll discuss how to best scale and branch out an analytics solution using a knowledgegraph technology stack. Our main weapons when beating that beast will be GraphDB , Ontotext Platform , Kafka , Elasticsearch , Kibana and Jupyter. Inferring new knowledge. Ontotext’s GraphDB.
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. Data is interconnected, like some sort of worldwide web of knowledge. Ontotext’s GraphDB.
Are LLMs Knowledgeable? And Other Crazy Ideas) The official conference started with a bang: Xina Lunda Dong’s presentation: Generations of KnowledgeGraphs : The Crazy Ideas and the Business Impact [ PPT ]. You can also play with ESG + KnowledgeGraph or read about PoolPary meeting ChatGPT ).
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?
During the battle, however, it is discovered that Ontotext has secret plans to allow both GraphQL and SPARQL to co-exist, and flourish together, building the ultimate weapon, the ONTOTEXT PLATFORM ! An armored KnowledgeGraph platform with enough power to enrich an entire planet. Star Wars KnowledgeGraphs.
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. Knowledgegraphs can help do both.
There’s been a lot of criticism that knowledgegraphs are too complex. So, why do we recommend knowledgegraphs, which are perceived to be complex, to our customers? Next, I will explain how knowledgegraphs help them to get a unified view to data derived from multiple sources and get richer insights in less time.
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.
Knowledgegraphs represent a collection of interlinked descriptions of concepts and entities. A knowledgegraph can be used as a database because it structures data that can be queried such as through a query language like SPARQL. It can be treated as a graph, a set of vertices and edges.
A knowledgegraph that integrates the several internal and some large external data sources would be able to automatically find many of these connections and free up these domain experts to explore and analyze the most interesting instances. The Benefits KnowledgeGraphs Bring to the Table.
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.
Metadata, our CEO Atanas Kiryakov told me, in a brief conversation about Ontotext’sknowledge management solutions , is for data as packaging is for goods. In an enterprise knowledge management context, metadata, and especially semantic metadata , also: facilitates data use. The one from packaging. supports data reuse.
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