Mining the Semantic Web: Requirements for Machine Learning

1
Mining the Semantic WebRequirements for Machine
Learning

• Fabio Ciravegna, Sam Chapman
• Presented by
• Steve Hookway
• 10/20/05

2
What is the Semantic Web

• A way to automate reasoning with web data
• RDF
• A uniform way to describe resources
• (subject,predicate,object)
• Ontology
• Hierarchical structure of data
• Property restrictions
• Implicit typing

3
Adding Meta-Data

• A prerequisite for Semantic Web (SW) is
  structured knowledge
• Manual Approach
• Too Much data
• Trust Issues
• Noise
• This process needs to be automated

4
Armadillo

• Automatically annotate web pages
• Validity based on a number of weak techniques
• Redundant Information
• Rating of Sources
• Context around a capture
• (LP)² - Extraction of knowledge
• Makes use of Natural Language Processing (NLP)

5
(LP)²

• Induce tagging rules
• Generalize NLP and keep best rules lttaggt
• Remove covered instances from pool
• High Precision, Low Recall
• Contextual Tagging
• Recovers rules and constrains their application
  lt/taggt
• Correction and Validation
• Shifts tags to correct position (within d spaces)
• Validation

6
Heterogeneity

• Armadillo
• Uses weak NLP
• Uses intra-document relation recognition
• Requirements
• Must adapt to different document types
• Relation Extraction

7
Bootstrapping Learning

• Armadillo
• Unsupervised approach user only validates
• User cannot drive system towards interesting
  documents and facts
• Requirements
• Identify triples
• Goal Bootstrap learning on a large scale
• User needs a role to guide learning

8
Content Cleaning and Normalization

• Armadillo
• Noise added during unsupervised (LP)²
• Use the multiple weak evidence to help avoid poor
  seeds
• Requirements
• Handle noisy training data

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Conclusion

• Semantic Web
• Meta-Data
• Armadillo a tool for IE
• Evidence Building and Validation
• Extraction of knowledge (LP)²
• A survey of requirements in mining web content
  for SW meta-data