This document discusses using semantic technologies to address the variety challenge of big data. It provides examples of applying semantic annotation to social data and metadata. Specifically, it describes how semantic annotation can extract meaningful metadata from social media posts, including information about users, content, relationships between users, and activity networks. The document outlines different types of metadata that can be derived from social media content, users, and networks.

1. BigData
Semantic Approach to
Big Data and Event Processing
Examples of Applied Seman1c Technologies
to Solve Variety Challenge of Big Data:
Social Data Annota1on
Prof. Amit Sheth
Ohio Center of Excellence in Knowledge-enabled Compu1ng (Kno.e.sis)
Wright State University, USA
Tutorial @ Kno.e.sis Centre: Seman1cs Approach to Big Data and Event Processing, Oct 7-9, 2015

2. BigData
• Metadata: an organized way to study
– Types
– Creation/extraction and storage
– Use
2Image: http://www.biowisdom.com/tag/metadata/
Metadata/Annotations

3. BigData3
Image: http://rww.to/9zyoQa
Metadata Infrastructure: Example for Tweet Annotation (mapped out tweet)

4. BigData4hSp://www.readwriteweb.com/archives what_twiSer_annota1ons_mean.php

5. BigData5

6. BigData
`
• Explicit informa1on from user profiles
– User Names, Pictures, Videos, Links, Demographic Informa1on, Group
memberships...
• Implicit informa1on from user a+en-on
metadata
– Page views, Facebook 'Likes', Comments; TwiSer 'Follows', Retweets,
Replies..
6
People Metadata: Variety of Self-expression Modes on Multiple Social Media
Platforms

7. BigData
Identification
Network
Activity
Interests
7
People Metadata: Various Types

8. BigData
User Iden-fica-on Metadata
• User-id
• Screen/Display-name of user
• Real name of user
• Loca1on
• Profile Crea1on Date
• User descrip1on
- Biodata of the user
- Link to webpage of the user
Interest Metadata
• Author type
- Trustee/donor, journalist, blogger,
scien1st etc.
• Favorite tweets
• Types of lists subscribed
• Style of Wri1ng (personality
indicator)
• No. of Followees
• Majority of author type of
Followees
8
People Metadata: Continued

9. BigDataWeb Presence:
- User affilia1ons
- KLOUT Score – influence measure (www.klout.com)
Ac-vity Metadata
• Age of the profile
• Frequency of posts
• Timestamp of last status
• No. of Posts
• No. of Lists/groups created
• No. of Lists/groups subscribed
Influence Metadata
(Inferring People Metadata from Network level Informa-on)
• No. of Followers – normal, influen1al
• No. of Men1ons
• No. of Retweets/Forwards
• No. of Replies
• No. of Lists/groups following
• No. of people following back
• Authority & Hub Scores
9
People Metadata: Continued

10. BigData
1. Content Independent metadata
• date, loca1on, author etc.
10
2. Content Dependent metadata
a. Direct content-based metadata
i. Explicit/Mentioned Content metadata
• named entities in content
ii. Implicit/Inferred Content Metadata
• related named entities from knowledge sources
b. Indirect content-based metadata (External metadata)
• context inferred from URLs in content (images, links to articles,
FourSquare checkins etc.)
V. Kashyap and A. Sheth, 'Semantic Heterogeneity in Global Information Systems: The Role of Metadata,
Context and Ontologies,’ in Cooperative Information Systems: Current Trends and Directions, M. Papazoglou and G. Schlageter (Eds.), Academic
Press, 1998, pp. 139-178.
Content Metadata

11. BigData
• For Tweets
1. Published date and 1me
2. Loca1on (where tweet was generated from)
3. Tweet pos1ng method (smart-phone, twiSer.com,
clients for twiSer)
4. Author informa1on
11
• For SMS
1. Publish date and time
2. Location (where SMS is generated)
3. Receiver (NGO, Government organization)
4. carrier information (available on request)
Content Metadata: Content Independent

12. BigData12
Direct Content-based Metadata
Indirect content-based metadata (External metadata)
Content Metadata: Content Dependent (Tweet)

13. BigData
Direct Content-based Metadata
13
Content Metadata: Content Dependent (SMS)

14. BigData
Connections/Relationships matter! (foundation for the network)
14
Structure Metadata
• Community Size
• Community growth rate
• Largest Strongly Connected
Component size
• Weakly Connected Components &
Max(WCC) size
• Average Degree of Separa1on
• Clustering Coefficient
Rela-onship Metadata
• Type of Rela1onship
• Rela1onship strength
• User Homophily (based on certain
characteris1c such as loca1on,
interest etc.)
• Reciprocity: mutual rela1onship
• Ac1ve Community/ Ties
Network Metadata

15. BigData
Semantic Approach to
Big Data and Event Processing
Thank you!
Any Ques1on?
Name
Schole
links