Webinar: Tagging with Rich Knowledge Graphs Presenter: Borislav Popov, Director Business Development, UK Semantic tagging is the driving force behind text analytics used by news and media organizations increasingly often. The service can help publishers target content with pinpoint accuracy. The webinar revolves around Ontotext's semantic publishing stack and lessons learned from production deployments of GraphDB, Concept Extraction Service and the Dynamic Semantic Publishing Platform, but it does not require prior knowledge of the Ontotext product set. As a result, we hope to give you a wider understanding of the interplay between text analytics and linked data, the importance of machine learning and continuous adaptation based on user feedback. Besides some of the reasons and benefits for using semantic tagging, its application both through an UI and as SaaS is also discussed. Presented are the data sets compiled in order to support the text analytics and what are the machine learning driven training processes in order to resolve their inherent ambiguity. Other points of discussion include methodology for building new tagging pipelines and how to address different domains, languages and types of content. To find out more, visit http://ontotext.com/

1. Tagging with Rich Knowledge Graphs

2. Semantic
Annotation

3. Semantic Annotation in Life Sciences
pmid:17714090
umls:C0035204
COPD
Bronchial Diseases
Respiration Disorders
umls:C0006261
Chronic Obstructive
Airway Diseases
Asthma umls:C000496
Ian A Yang
Clinical and experimental pharmacology …

5. Ontotext and Euromoney
Ontotext, GraphDB, HA Cluster
Profile
• Euromoney Institutional Investor PLC,
the international online information
and events group
Goals
• Create a horizontal platform to serve
100 different publications
• create a new publishing and
information platform which would
include the latest authoring, storing,
and display technologies including,
semantic annotation, search and a
triple store repository
Challenges
• Different domains covered
• Sophisticated content analytics incl.
Relation, template and scenario
extraction
• Analytics of reports and news of various
domains
• Extraction of sophisticated macro
economic views on markets and market
conditions; trades, condition and trade
horizons, assets, asset allocations, etc.
• Multi-faceted search
• Completely new content and data
infrastructure
#5December 2015

6. Ontotext and Financial Times
Ontotext, GraphDB, HA Cluster
Profile
• Top 3 business media
• Focused both on B2C publishing and
B2B services
Goals
• Create a horizontal platform for both
data and content based on semantics
and serve all functionality through it
Challenges
• Critical part of the entire workflow
• Multiple development projects in
parallel with up to 2 months time
between inception and go live
• GraphDB used not only for data, but
for content storage as well
• Horizontal platform with focus on
organizations, people, GPEs and relations
between them
• Automatic extraction of all these concepts
and relationships
• Separate stream of work for a user behavior
based recommendation of relevant content
and data across the entire media
#6December 2015

7. Business/Functional
requirements
gold standards
a knowledge base

10. Domain
driven
ontology
design

11. Conceptual Schema

12. Domain Model: Macro Economics

13. Knowledge Base

14. Initial Pipeline

15. Machine
Learning

16. Example Curation Tool:
PressAssociation

17. Continuous
Adaptation

19. Semantic
Indexing

20. Tagging with Rich Knowledge Graphs