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Those are bold plans, because in 2022 we received recognition for what we’ve achieved through investment to help us expand, accelerate growth and engage the market with the technology we’ve been developing for 20 years. Over the next 3 years Ontotext transformed itself into a product focused company with over 30% revenue growth.
At the end of an unconventional year, we at Ontotext still want to honor our tradition and provide our readers with a round-up of the most popular posts on our blog. In 2020, we continued to develop our leading database engine for management of knowledge graphs, GraphDB , and expanded it with a lot of new functionalities.
Only by making connections between all of these sources and their own proprietary data can enterprises identify potential new treatments. A friend of mine went whale watching recently. He spent a small fortune on tickets for his family and then proceeded to sit on a boat for four hours scouring the ocean for any sign of life.
Assessing the abilities of Artificial Intelligence (AI) systems is a critical aspect of development that has become increasingly important as AI has expanded into various areas of application. An area of AI that Ontotext has been working on for over 20 years is text analytics. You can read more about it in this blog post.
The reason is that the inherent complexity of big enterprises is such that this is the simplest model that enables them to “connect the dots” across the different operational IT systems and turn the diversity of their business into a competitive advantage. Yet, you don’t expect to be able to get to the moon on a bike, do you?
Had Johny known and used semantic technology, and more specifically Linked Data, to “transport” tons of bits of information, would he have been better off as a data carrier? Which are not so different from the concerns of any other enterprise having to deal with data management. Today, I want to invite you to a thought experiment.
Many enterprise data and knowledge management tasks require strict agreement, with a firm deterministic contract, about the meaning of the data. They can also help with “oversight”: validation, explanation and filtering of the answers of an LLM. Let me share a few examples of how LLMs help knowledge graphs.
Those are bold plans, because in 2022 we received recognition for what we’ve achieved through investment to help us expand, accelerate growth and engage the market with the technology we’ve been developing for 20 years. Over the next 3 years Ontotext transformed itself into a product focused company with over 30% revenue growth.
Had Johny known and used semantic technology, and more specifically Linked Data, to “transport” tons of bits of information, would he have been better off as a data carrier? Which are not so different from the concerns of any other enterprise having to deal with data management. If Johnny Mnemonic Smuggled Linked Data.
However, in the race to become data-driven, most efforts have resulted in a tangled web of data integrations and reconciliations across a sea of data silos that add up to between 40% – 60% of an enterprise’s annual technology spend. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”.
This structured representation of knowledge not only allows for more efficient sharing and reuse of information but also facilitates the discovery of new knowledge within the domain. In this post, we present several of the key benefits they offer and support them with case studies of Ontotext clients and other examples.
In our previous post, we covered the basics of how the Ontotext and metaphacts joint solution based on GraphDB and metaphactory helps customers accelerate their knowledge graph journey and generate value from it in a matter of days. You can also listen to our on-demand webinar on the same topic or check out our use case brief.
Chunks of information can be reordered, sentences expanded, and words given definitions on the spot […]. What’s more, the interactions and connections between them can be, if not computed in their entirety, at least mapped and interlinked for smarter use. The text in these documents is also changing.
In this moment of technological change, I think it’s helpful to reflect on the challenges our professional community has already faced and solved together. A concrete example helps to explain the breakthrough. It’s always a pleasure to attend KMWorld because of the opportunity to connect in person, and this year was no exception.
The age of Big Data inevitably brought computationally intensive problems to the enterprise. Get these wrong and chances are your enterprise processes and systems will suffer. While Big Data was all the rage, now “small and wide” data is the focus, giving more specificity to the information being developed.
In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. Changing the game with knowledge graphs So how does knowledge graph technology helpenterprises in the Financial Services Industry become data-driven?
The one helping you see the consequences of your behavior (behavioral data to be exact) is built by Accenture, expanding on the concept of captology. It is hard to think and talk about digital twins without feeling like living a sci-fi novel. If both these sound like cyberpunk, it is because they are. But they are real.
The first one is Enterprise 360 where organizations are saying, “The information about my most important assets is spread across many different systems. The first one is Enterprise 360 where organizations are saying, “The information about my most important assets is spread across many different systems.
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