1. Survey of Semantic Annotation Platforms
SAC 2005
Survey of Semantic Annotation Platforms
Lawrence Reeve Hyoil Han

2. Semantic Annotation
Creating semantic labels within documents for the Semantic Web
Used to support:
Advanced searching (e.g. concept)
Information Visualization (using ontology)
Reasoning about Web resources
Converting syntactic structures into knowledge structures (humanmachine)

3. Semantic Annotation Process

4. Semantic Annotation Concerns
Scale, Volume
Existing & new documents on the Web
Manual annotation
Expensive – economic, time
Subject to personal motivation
Schema Complexity
Storage
support for multiple ontologies
within or external to source document?
Knowledge base refinement
Access - How are annotations accessed?
API, custom UI, plug-ins

5. Semantic Annotation Platforms
Why semantic annotation platforms (‘SAPs’)?
Reduces human involvement
Consistent application of ontologies
Reduced cost – economic & time
Scalability
Multiple ontologies for single document

6. Semantic Annotation Platforms
Characteristics
Provide many services, not just annotation
Storage: ontology, KB, and annotation
Access APIs (query annotations)
Integrate information extraction methods
Support for IE (gazetteers)
Extensible

7. SAP General Architecture

8. SAP Classification

9. SAP Classification Pattern-based Pattern-discovery Rules
Iterative learning
provide initial seed set
find new entities  find new patterns
repeat
Rules
Manually define rules to find entities in text
Simple label matching

10. SAP Classification Machine-learning based Wrapper Induction
LP2
Uses structural and linguistic information
Produces tagging & correction rules as output
Statistical models
Hidden Markov Model

11. SAP Classification Multistrategy
Combine pattern and machine-learning approaches
Did not find a platform that implements this approach
Platform extensibility important for implementation

12. Semantic Annotation Platforms
Selection
Idea is to get a representative sample of platforms using various information extraction techniques
System needed to be a platform offering services, not just algorithm

13. Semantic Annotation Platforms

14. Language Toolkits GATE – language processing system
Component architecture, SDK, IDE
ANNIE (‘A Nearly-New IE system’)
tokenizer, gazetteer, POS tagger, sentence splitter, etc
JAPE – Java Annotations Pattern Engine
provides regular-expression based pattern/action rules
Amilcare
adaptive IE system designed for document annotation
based on LP2
uses ANNIE

15. KIM (2003)
ontology, kb, semantic annotation, indexing and retrieval server, front-ends (Web UI, IE plug-in)
KIMO ontology
250 classes, 100 properties
80,000 entities from general news corpus in KB
(plus >100,000 aliases) IE
Uses GATE, JAPE
Gazetteers (from KB)
Source:

16. Ont-O-Mat (2002) Uses Amilcare Extensible Wrapper induction (LP2)
Adapted in 2004 for PANKOW algorithm
Disambiguation by maximal evidence
Proper nouns + ontology  linguistic phrases
Source:
kcap2001-annotate-sub.pdf

17. MUSE (2003) Pipeline of processing resources (PRs) Makes use of JAPE
PRs called conditionally based on text attributes
Makes use of JAPE
Adaptive rules
Can link multiple resources together
Gazetteer + part-of-speech tagger
Resolve entity ambiguities
Source:

18. SemTag (2003) Large-scale annotation Uses the TAP taxonomy
Annotations separate from source
“Semantic Label Bureau”
Uses the TAP taxonomy
Approach is:
Find match to label in taxonomy
Save window before & after match
Perform disambiguation
Main contribution is using taxonomy for disambiguation
Source:
resources/semtag.pdf

19. Platform Effectiveness
*as reported by platform authors

20. Summary Several platforms developed in last several years
Large implementation effort; many services
Differentiated by
IE methods used
Services provided
Future
IE integration will likely improve annotation accuracy
Extension of existing platforms will allow for quicker research