Read about the unique blend of technology offered by Ontotext - coupling text mining and RDF triplestores.
Making Sense of Text and Data
Provide consistent unified access to data across different systems by using the flexible and semantically precise structure of the knowledge graph model
Interlink your organization’s data and content by using knowledge graph powered natural language processing with our Content Management solutions.
Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
Quick and easy discovery in clinical trials, medical coding of patients’ records, advanced drug safety analytics, knowledge graph powered drug discovery, regulatory intelligence and many more
Make better sense of enterprise data and assets for competitive investment market intelligence, efficient connected inventory management, enhanced regulatory compliance and more
Connect and model industry systems and processes for deeper data-driven insights in:
Improve engagement, discoverability and personalized recommendations for Financial and Business Media, Market Intelligence and Investment Information Agencies, Science, Technology and Medicine Publishers, etc.
Unlock the potential for new intelligent public services and applications for Government, Defence Intelligence, etc.
Connect and improve the insights from your customer, product, delivery, and location data. Gain a deeper understanding of the relationships between products and your consumers’ intent.
Link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs.
Organize your information and documents into enterprise knowledge graphs and make your data management and analytics work in synergy.
Integrate and evaluate any text analysis service on the market against your own ground truth data in a user friendly way.
Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph.
Informational
What do you think is the common thread between the Great Roman Empire and your Great scientific research, journalistic report or financial analysis?
In a word, it is interconnectedness.
These are the avenues that connect objects to make a rich system of intelligent pathways throughout your content management system and across the web.
The interconnectedness of Roman Roads.
Among the things that made the Roman Empire great were its roads. The communication channels linking Rome to its colonies fostered further expansion, making exchange thrive throughout the entire empire. With building military roads in mind, what Romans actually created was a vital infrastructure that facilitated the movement of goods, people and ideas.
It is that same facilitating of communication and information exchange within the infrastructure of an organization’s content assets that can make knowledge discovery flourish.
When everything is interlinked, the interconnected parts are more easily remixed, recombined, repurposed, you name it.
And semantic enrichment (also known as semantic annotation) is exactly that, enriching textual content with additional, well-defined information which can be processed by computers. Words become not only words but rather easy-to-search and easy-to-use, machine-readable pieces of data.
By telling a computer how data items are related and how these relations can be evaluated automatically, the processing of complex filter and search operations becomes possible. And this is vital for efficient content management and knowledge discovery.
Trying to make good use and sense of their content assets, today enterprises realize that they are “virtually sitting on more text data than they have ever experienced before.”
As a structural solution to the growing amount of data organizations face, the last decade has seen the emergence of “intelligent content”. The data pieces created by semantic enrichment are the building blocks of this type of content. These building blocks allow content to travel across multiple channels, platforms and systems. They help you connect the dots, get deeper insights, guide your research and last but not least aid the uncovering of hidden relationships.
Depending on what you need and expect from your organization’s content assets, semantic enrichment can solve some of the most common problems, allowing for:
Creative chaos might work for you but not for everyone else trying to make sense of your content.
So, in order to make our ambiguous, difficult-to-interpret, often messy and heterogenous content a neat set of machine-processable data pieces, semantic enrichment comes into play.
It adds machine-readable meaning to specific chunks of text, thus taking them to the next level, where what they contain can be boiled down to reusable information, opening the door for better search and presentation in an organization’s content or web-wide.
In order for a text to become a neat set of data pieces, it is put through a number of text-enrichment steps.
Text is extracted from articles, documents or any form of unstructured data.
After sentences are split, the important concepts are identified through dictionary word lists.
Machine learning algorithms classify and disambiguate the identified concepts.
Relationships between the entities are also identified.
The facts and the original reference to the articles are indexed and stored with corresponding classifications and relationships in a triplestore.
Here’s how, in five steps, information is given well-defined meaning and becomes ready to join other data pieces, enabling new combinations, insights and cross-references.
Collectively, this powerful blend of relationships, classifications, explicit & inferred facts and unstructured data allow organizations to understand and interrogate their content and data at a much finer grain of detail.
Excerpt from: The Truth about Triplestores
It is through this extraction of structured, processable data from a free-flowing textual content that we can help computers help us. And it is through semantic enrichment that we can “package” our content in a way that would further allow its easy traveling across platforms and devices.
To get back to the Roman infrastructure reference, with semantic enrichment, we have the unique opportunity to interconnect objects and thus facilitate information exchange and knowledge discovery. Just like Roman roads did.
Excited about how semantic enrichment changes the way we create and consume content?
White Paper: Text Analytics for Enterprise UseUse the power of text analytics for your enterprise |
Read about the unique blend of technology offered by Ontotext - coupling text mining and RDF triplestores.
Discover interesting news, aggregated from various sources with Ontotext's NOW and enjoy their enriched content with semantic annotation.
Learn how semantically annotated texts enhance the delivery of content online with Ontotext's News On the Web (NOW) demo.
Read about the unique blend of technology offered by Ontotext - coupling text mining and RDF triplestores.
Discover interesting news, aggregated from various sources with Ontotext's NOW and enjoy their enriched content with semantic annotation.
Learn how semantically annotated texts enhance the delivery of content online with Ontotext's News On the Web (NOW) demo.
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.
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