Semantic Web Solutions for Publishers

Engage Your Readers with Personalized Adaptive Content

The Ontotext Dynamic Semantic Publishing Platform consists of a set of databases, machine learning algorithms, APIs and tools we use to build solutions for news, educational, scientific, academic and other publishers.

At a high level, the platform consists of:

  • Machine learning models for text analysis, disambiguation of entities, concept extraction and classification.
  • A set of databases to store semantic indexes, consolidated entity profiles, linked open data and user behavior profiles. GraphDB™ is part of this stack.
  • APIs for text analysis, model training, search, recommendations, content management and concept profiles
  • Tools used to build UIs for contextual authoring, enrichment monitoring, curation & quality assurance, template definition and other user applications.

How It Works

Semantic Web Services

Users are able to search content using key words, phrases, facets and full text search. Behind the scenes we build hybrid queries, powerful SPARQL that queries your content store and the GraphDB™ RDF triplestore simultaneously. We don’t compromise performance in production environments that have large volumes of queries, updates and inferences taking place on the database at the same time.
By using semantic knowledge, your internal users and website visitors see the most important and relevant content. This creates a very powerful, compelling user experience leading to higher levels of loyalty and dramatic improvements in authoring time.

Search and Analytics

Users are able to search content using key words, phrases, facets and full text search.

Semantic Enrichment

Our text mining pipelines analyze your text, extract concepts, identify important entity relationships, classify all of your content and disambiguate entities that are similar.

How Semantic Enrichment Works

Data Management

We are able to ingest and normalize content and data from any number of diverse sources.

Continuous Adaptation

As users work with the system, Ontotext Dynamic Semantic Publishing platform is constantly learning, remembering and refining responses.

Recommendation Engine

We have combined user profiles, search histories and semantic classifications of data to deliver hyper-contextual results.

Semantically Enriched Content, Personalized Recommendations
Your Foundation for Personalized, Efficient Semantic Search

Ontotext Dynamic Semantic Publishing platform leverages a variety of data sources to deliver optimal results. At the heart of the solution is a knowledge base used to classify all of your content and metadata. This is combined with your content store, Linked Open Data for enrichment and profiles tracking behavior over time. This blend of semantic databases is stored and accessible from our graph databases engine, GraphDB™.

NOW – Publishing Platform

This is a demo of our services. If you like it you can set up one like it for free. Just follow the instructions.  Ontotext looks forward to helping news publishing be more beneficial for readers and equally rewarding to publishers.

Ontotext Dynamic Semantic Publishing Platform Solutions

Semantic News Publishing

Engage Your Readers with Personalized Adaptive Content Ontotext Semantic News Publishing is the basis of our solutions in news & media publishers.

Scientific Publishing

Ontotext Scientific Publishing helps publishers maximize content value by leveraging existing taxonomies, thesauri and controlled vocabularies.

Personalized Learning

Ontotext Personalized Learning harnesses semantic technology to empower educators. Educators can match student needs with relevant learning resources.

With 32 teams, 8 groups and 776 individual players, managing the Web site for the 2010 FIFA World Cup was a daunting task. There were simply too many pages and too few journalists too many pages and too few journalists to create and manage the site’s content.

After growing in part through acquisition, Euromoney found itself with 84 brands and more than 100 different publications. They turned to Ontotext for a solution that would help them easily reuse and repurpose content within and between the business units.

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