The document discusses semantic web technologies for business and industry. It covers topics like knowledge graphs, linked data, semantic text mining, semantic data integration, and the semantic web architecture. The overall goal is to explain how semantic technologies can help businesses and industries address challenges in areas such as e-commerce, media publishing, and knowledge discovery by better managing digital assets and connecting data silos.

1. Semantic Web Technologies
for Business and Industry
Challenges
Dr.U.Kanimozhi

2. Agenda
• Semantic Technologies
• Knowledge Graphs
• Linked Data based on Semantic Web
Standards
• Linguistic Concepts for Text Mining
operations
• Semantic Text Mining
• Semantic Data Integration
• Query language of the Semantic Web
• Semantic Web Architecture
• Useful Resources
• Conclusion
Semantic AI

3. • Semantic Technologies (3 mins)
• Knowledge Graphs (2 mins)
• Linked Data based on Semantic Web Standards (5 mins)
• Linguistic Concepts for Text Mining operations (3 mins)
• Semantic Text Mining (3 mins)
• Semantic Data Integration & Query language of the Semantic Web (3
mins)
• Semantic Web Architecture (

4. Major Focus
• Overview of semantic Technologies
• What differentiates Semantic Technologies
• Organizational considerations

5. Data Economy
Data needs to be linked!

6. Challenges in Business and Technology
E-Commerce Media and Publishing Knowledge Discovery &
Data Analytics

7. Challenges in Business and Technology
E-Commerce Media and Publishing Knowledge Discovery &
Data Analytics
How do we manage our digital asset?

8. Challenges in Business and Technology
E-Commerce Media and Publishing Knowledge Discovery &
Data Analytics
It comes all down to Linked Data
How do we manage our digital asset?

9. Managing contents by entities
Context
Entities
Relations

10. Different shades of Metadata

11. Connecting Data Silos
Metadata Management Standards based
technologies
Graph based
knowledge modelling
Knowledge
modelling
Text Analytics

12. Business area and solution for varying Industries

13. Semantic Technologies
● 80% of all information is in text.
● Unstructured data
○ Natural language and the
nuances
○ Meanings associated with
specific terms
○ Phrases, grammar, language
use, and context.
● NLP is an integral part of semantic
technologies

14. Uses of Knowledge Bases
Concept Tagging Entity Tagging Mapping
Data Integration Semantic Search Machine Learning

15. Knowledge Engineer; Data Architect; Data Engineer

16. “Interoperability” - Bringing various types of aspects together
Data Centric Knowledge Centric
Service-oriented industries Asset-oriented industries
Databases & Excel Content & documents
Structured data Unstructured data
Linked Data Knowledge Graph
Algorithms Collaborative knowledge management
Predictability and automation Product innovation and risk mitigation
Ontologies and Machine Learning Taxonomies and Collaboration
“Actionable Data!” “Better Decisions!”
Understand the customer and market place better Stay or become the expert in the field

17. Knowledge Engineer’s perspective

19. Source: https://semantic-web.com/

20. Deep Text Analytics
Annotation, Extraction, Classification, Rules
▸ Corpus statistics / Word embeddings
→ Keyphrase extraction
▸ Graph-based annotation
→ Entity/Concept linking
▸ Corpus Statistics embedded in graphs
→ Shadow Concepts
▸ Machine-learning-based annotation
→ Named entity recognition (NER)
▸ Machine-learning based classification
→ Document Classification
▸ Annotation based on rules
→ Regular expressions

21. What is a
KNOWLEDGE GRAPH

22. “The Knowledge Graph is a knowledge base used by
Google and its services to enhance its search engine's
results with information gathered from a variety of
sources.” (Wikipedia)
"For knowledge graphs in information science, see
https://en.wikipedia.org/wiki/Ontology_(information_science)"

23. “A knowledge graph
1.mainly describes realworld entitiesand theirinterrelations, organized in a graph.
2.definespossible classesand relations ofentities in a schema.
3.allows for potentially interrelatingarbitraryentities with each other.
4.covers various topical domain
The first two criteria clearly define the focus of a knowledge graph to be the actual instances
(A-box in description logic terminology), with the schema (T- box) playing only a minor
role.” (Paulheim)

24. Building Blocks of a Knowledge Graph
“A Knowledge Graph is a model of a knowledge domain”
Model means: "A representation, formal naming and definition
of the categories, properties and relations between the concepts,
data and entities"
Building Block One: Conceptual/Domain model (Ontology)

25. Building Blocks of a Knowledge Graph
“A Knowledge Graph must eliminate ambiguity”
Eliminate ambiguity introduced by language. This also can cover
multilinguality.
Building Block Two: Controlled Metadata
(Vocabulary/Taxonomy)

26. Building Blocks of a Knowledge Graph
“A Knowledge Graph is unified information across
an organization”
Unified information across an organization, enriched with contextual and semantic
relevance across the silos.
Building Block Three: (Virtual) Data Layer