Automated Content Enrichment
Use the transformational power of semantic tagging to enrich your content automatically. Our offerings enable you to evaluate different text mining offerings, make a build-or-buy decision, aggregate different services and cherry-pick their best sides to serve your use case in the most optimal way.
How You’ll Benefit
Facilitate advanced search capabilities, content organization and non-linear user exploration by improving metadata coverage and consistency in your content;
Optimize your operational costs and editorial efforts by automating the metadata generation.
Develop new data products from your unstructured assets, enriched through information extraction, enabling your transformation from a content vendor to a data vendor;
Some of the challenges lurking in any natural language content repositories:
- Ambiguity – the language is inherently ambiguous. The same word has different meanings in different contexts. For example, Paris, France is a location; Paris Hilton is a person; Paris is a Greek myth hero; Paris, Texas is a movie (and location), etc.
- Variability – often there are slight variations in naming conventions – i.e., William, Bill, Billy, etc.
- Inconsistency – the same term is frequently used for describing (slightly) different phenomena. This is the case even in a highly formal context (i.e., legal, medical) where there are strict and more popular terms, describing a certain concept.
- Volume – the typical task is to process large bodies of text created by different authors, organizations, cultures and targeted towards different audiences and objectives.
How it Works
- Create a knowledge organization system based on a taxonomy or an ontology describing your domain, business or use case.
- Apply automatic tagging to generate rich metadata driven by your model.
- Multiply that by leveraging Natural Language Processing integrated with big knowledge graphs to extract business concepts like people, organizations and locations and the relationships between them.
- Aggregate multiple text mining services and pick the best output from each one of them to compose content enrichment that serves your use case best.
- Use inference to enrich the resulting metadata and provide search results that are not explicitly stated in your content.
Case Studies
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Increasing User Engagement in Publishing Platforms by Interlinking Ad Serving with Semantic Technology
Ontotext’s solution for smarter recommendations and ad serving enables a leading media publisher to know more about their content as well as their readers, improve its recommendation system, boost user engagement and adopt a sophisticated ad serving system that targets a more segmented audience.
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Euromoney: Improving Content Production Through a Unified Semantic Publishing Platform
Euromoney’s BCA Research chose Ontotext technology in its quest to create a new publishing and information platform, which would include the latest authoring, storing, and display technologies including semantic search and an RDF triplestore.
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BBC Uses Semantic Publishing to Power the FIFA World Cup Website
In 2010, the BBC used Ontotext technology to bring a new approach to publishing and managing their content. In 2013, with Ontotext technology at its heart, the BBC went even further and developed its Linked Data Platform.
Showcase:
News on the Web (NOW)
A free public service, showcasing the opportunities open up before media and publishing companies. Get a real feel of the world where semantic technologies are already shaping the way we search, discover and consume content.
Products, Solutions and Services Involved
Ontotext Metadata Studio
GraphDB
Knowledge Graph Enrichment
Text Mining and Analytics
Knowledge Graph Enrichment
Do you want to explore how this solution could be customized for your organization?
