Text Analysis for Content Management

Interlink your organization’s data and content by using knowledge graph powered natural language processing.

Our Content Management solutions enable you to improve the discovery, organization and consumption of your content and unfold the value locked away in unstructured text. This empowers you to turn static documents into actionable data.

Verticals we benefit:

Text Analysis for Content Management

How you’ll benefit

value from information Icon

Get value from information previously locked away in disconnected systems or in static content.

text mining functionalities Icon

Take advantage of text mining functionalities that happen on top of your data and content.

Utilize your enterprise data Icon

Utilize your enterprise data better to empower deeper insights.

Make use of formalized knowledge Icon

Make use of formalized knowledge about the world, provided by a custom knowledge graph.

News On the Web Icon

News On the Web

Tag by Ontotext Icon

Tag by Ontotext

OTKG Icon

Ontotext Knowledge Graph

About Text Analysis for Content Management

Ontotext’s Text Analysis for Content Management is based on off-the-shelf components, which we can also customize and extend to address the challenges specific to your business.
Whatever your use case or domain, we can help you apply your subject matter expertise to the data that is important to your organization.

Ivaylo Kabakov, Head of Semantic Analytics Solutions and Borislav Ankov, Project manager at Ontotext, talk about Text Analysis with Ontotext Platform.

How it works

When building Content Management solutions, we need to go through the following 5 stages:

Features

Semantic Tagging

Semantic Tagging

Discover mentions of known and novel concepts and link them to the relevant parts of your content.

Content Classification

Make use of context-sensitive analysis and classification for categorizing and organizing your unstructured content.

Content Classification
Content Recommendation

Content Recommendation

Suggest relevant related content based on the semantic fingerprint of your documents and the actions of your readers and their profiles.

White Paper:
Text Analysis for Content Management at Ontotext

5 Steps To Make Your Content Serve Your Business Better

Get started with your Text Analysis for Content Management today!

White Paper: 
Text Analysis for Content Management at Ontotext

"A big and key piece of the value of Target Discovery was the ranking by targets - that caught our attention. And the willingness of Ontotext to work with us to understand our needs was a good part of the choice. They showed how we could use our data, and customized the tool and process to fit our needs."

Senior Researcher

Senior Researcher, Leading US Cancer Research Institute

"Ontotext’s solution does what they need it to do. The willingness of the Ontotext team to adjust the tool based on needs was a critical point. We formed a relationship with them that made a difference - and our ability to handle large data sets, to find and rank choices is now so much better and faster!"

Anonymous

Researcher, Leading US biomedical and genomic research center

Our experience with Ontotext Target Discovery platform has been phenomenal. We initially thought we’d just plug in a set of genes, then get some results, and go and validate them. But it’s reached far beyond that. The platform has all these little tools that we can use to better prioritize candidates or build a stronger rationale

Montdher Hussain

Mulligan Lab, Queen’s University

"When I think of Target Discovery, I believe it would be an integral part of every workflow. Now I always keep an open tab while I’m doing my work and it’s a super useful tool. It’s something I’d always refer to, whether that’s a literature search, looking for patterns I can use, or building a rationale for something. I can get the information I need and, within minutes, I’m able to build enough rationale to guide my research"

Montdher Hussain

Mulligan Lab, Queen’s University

"It’s been helpful to get an idea of how to rank and stratify genes, so we would definitely recommend your platform to our colleagues and consider it for use in other projects."

Dr. Emma Mead

Chief Scientific Officer, Oxford Drug Discovery Institute

"It’s quite hard to go to all EMBL (European Molecular Biology Laboratory) databases and pick out the relevant experimental data. The Target Discovery platform is quite helpful for getting meaningful insights from any information we are interested in."

Ayesha Khan

Research informatician, Oxford Drug Discovery Institute

“Ontotext is the right place for people like me who are ready to dive into complex challenges and those who also value a flexible working environment.”

Kristina Gocheva

Software Engineer, Solutions team

“My journey at Ontotext has been nothing short of adventurous and exciting. Here you can combine theoretical knowledge and practical solutions to many challenging problems. From graph traversal algorithms to highly available distributed systems, you get the opportunity to learn about new technologies, frameworks and algorithms and apply this knowledge to compelling use cases.”

Tomas Kovachev

Team Lead, Product Development team

“I’m quite a curious person and Ontotext is great for me because I work with data coming from new fields all the time. This is exciting because through the data they collect, I get to discover the reality of the field through one of the most objective lenses possible.”

Nikola Tulechki

Data Scientist, Research team

"GraphDB: Relatively robust. Ease of use for developing queries. Offer free features that could help the widespread use."
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Assistant Professor, Education

"GraphDB: The class hierarchy, class relationships, visual graph these options are great to know about ontology of data and relationship of data."
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Software Developer, Software

"GraphDB: I'm impressed with its performance and ease to use. I also appreciate the scalability as it has easily accommodated my growing datasets without sacrificing speed or reliability."
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Student - Data Analyst, Education

"GraphDB: Visualization of the graph SPARQL query interface. Easy importing and exporting. Multiple output formats available."
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Researcher, Education

"GraphDB: Graphical relations are organized and presented in multiple meaningful ways. I am yet to encounter any performance issues, but I have only worked with the free edition."
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Ingénieur en Recherche et Développement, Telecommunication

"GraphDB: Visualization, creating and executing SPARQL queries, inference, supporting all RDF languages. There is a free edition available."
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Engineer, Miscellaneous

"GraphDB: It's efficient and very much as per our requirement. Totally love the product and planning to use it with more solutions."
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Data Engineer, Construction

"GraphDB is known for high performance. It can handle large amount of data and can process queries very quickly, which makes it very suitable for use in large-scale applications."
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Software Developer, IT Services

Do you want to explore how our Text Analysis for Content Management can be customized for your organization?

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