Annotation for the Semantic Web

1
Annotation for the Semantic Web

• Yihong Ding
• A PhD Research Area Background Study

2
Introduction

• Current web is designed for humans
• Semantic web (next-generation web) is designed
  for both humans and machines
• Semantic annotation
• Disclose semantic meanings of web content
• Convert current HTML web pages to
  machine-understandable semantic web pages

3
Outline

• Historical Review
• Current Status
• Related Research Fields
• Future Challenges

4
Semantic Annotation in Ancient Ages

• No evidence when humans started to annotate text

history of semantic annotation history of
ontologies
5
The First Dream of Modern Semantic Annotation

• July 1945, Vannevar Bush, As We May Think, The
  Atlantic Monthly
• Bush's dream device
• humans could acquire information (World Wide Web)
• humans could contribute their own ideas (Web
  Annotation)
• from/to the community

6
Web Annotation before 1999

• Heck et. al., 1999
• Developing better user interfaces
• Improving storage structures
• Increasing annotation sharability
• Example systems
• ComMentor, AnnotatorTM, Third Voice, CritLink,
  CoNote, and Futplex

7
Semantic Labeling before 1999

• Dublin Core Metadata Standard http//dublincore.o
  rg/
• 15 element sets encapsulate data
• Superimposed
• Information
• Delcambre et. al., 2001

• Type
• Format
• Identifier
• Source
• Language
• Relation
• Coverage
• Rights

• Title
• Subject
• Description
• Creator
• Publisher
• Contributor
• Date

Superimposed Layer
marks
Base Layer
Information Source1
Information Source2
Information Sourcen

8
Status of Current Web Semantic Annotation Studies

• Interactive annotation
• Automatic annotation

9
Interactive Annotation Systems

• Lets humans interact through machine interfaces
  to annotate documents
• Problems
• Inconsistency
• Error-proneness
• Lack of scalability
• Values
• Easy to implement
• Suitable for small-scale tasks and experiments
• Helpful to build corpora for evaluations

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Interactive Annotation Systems

• Annotea Kahan et. al., 2001
• W3C project
• An open RDF infrastructure for shared web
  annotations
• SHOE (Simple HTML Ontology Extensions) Heflin
  et. al., 2000
• University of Maryland, College Park
• Manual annotator using SHOE ontologies

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Automatic Annotation Systems

• Common feature use of ontologies
• Typical approaches
• Annotation with automatic ontology generation (1
  system)
• Annotation with automatic information extraction
  (6 systems)

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Annotation with Ontology Generation

• SCORE (Semantic Content Organization and
  Retrieval Engine) Sheth et. al., 2002
• Voquette (now acquired by Semagix Co.),
  University of Georgia

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Annotation with Automatic IE

• Ont-O-Mat Handschuh et. al., 2002
• University of Karlsruhe at Germany
• MnM Vargas-Vera et. al., 2002
• Open University of United Kingdom
• Common features
• DAMLOIL ontologies
• Supervised adaptive learning with Lazy-NLP
  (Amilcare)
• Annotation stored inside web pages
• Differences
• MnM allows multiple ontologies at one time
• MnM also stores annotations in a knowledge base
• Ont-O-Mat uses OntoBroker both as an annotation
  server and as a reasoning engine

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Annotation with Automatic IE

• KIM Platform Kiryakov et. al., 2004
• Ontotext Lab., Sirma Group, a Canadian-Bulgarian
  joint venture
• SemTag Dill et. al., 2003
• IBM Almaden Research Center
• Similar features
• Use one special designed upper-level ontology,
  KIM ontology vs. TAP ontology
• Specific features
• KIM uses an NLP tool (GATE) to extract
  information
• KIM stores annotations in a separate file
• SemTag uses inductive learning to extract
  information
• SemTag annotates 264 million Web pages and
  generate approximately 434 million semantic tags

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Annotation with Automatic IE

• Stony Brook Annotator Mukherjee et. al., 2003
• Stony Brook University
• Structural analysis of DOM tree for HTML pages
• Drawbacks
• Taxonomic relationships only
• No generic labeling algorithm disclosed
• RoadRunner Labeller Arlotta et. al., 2003
• Università di Roma Tre and Università della
  Basilicata
• Automatic assign label names based on image
  recognition
• Drawbacks
• Semantic meaning of labels unknown
• Difficulty in associating labels with ontologies

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Related Research Fields

• Semantic Web
• Information extraction
• Ontology related topics
• Conceptual modeling
• Logic languages
• Web services

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Semantic Web

• Weaving the Web Berners-Lee 1999, birth of the
  Semantic Web
• The Semantic Web Berners-Lee et. al., 2001

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Information Extraction Laender et. al., 2002

• Human-guided approaches
• Wrapper languages, Modeling-based tools
• No annotation examples
• Too heavily human involvement
• Non-ontology-based approaches
• HTML-aware tools StonyBrook tool Mukherjee et.
  al., 2003,
• RoadRunner Labeller Arlotta
  et. al., 2003
• NLP-based tools Ont-O-Mat Handschuh et.
  al., 2002,
• MnM Vargas-Vera et. al., 2002,
• KIM platform Kiryakov et. al., 2004
• ILP-based tools SemTag Dill et. al.,
  2003
• Require extra alignment between extraction
  categories in wrappers and concepts in ontologies
• Ontology-based Approaches
• Ontology-based tools my proposal
• Not require alignment, resilient to web page
  layouts
• Slow in execution time

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Ontology Related Topics

• Ontology languages W3C, OWL
• Knowledge representation and reasoning
• Ontology generation Ding et. al., 2002a
• Annotation domain specification
• Ontology enrichment Parekh et. al., 2004
• Annotation domain specification expanding
• Ontology population Alani et. al., 2003
• Annotation result output
• Ontology mapping and merging Ding et. al.,
  2002b
• Large-scale annotation requires large-scale
  ontologies
• Small-scale ontologies are less expensive to
  build
• Ontology mapping creates the links among
  small-scale ontologies
• Ontology merging fuses small-scale ontologies
  into a large-scale ontology

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Conceptual Modeling

• Annotation requires knowledge modeling
• Ontology is a type of conceptual modeling
• ER Model Chen 1976
• The most influential conceptual model
• Influence OSM model, basis of data-extraction
  ontology

21
Logic Languages

• Logic foundation provides reasoning and inference
  power for modeling languages
• Examples
• First-order logic Smullyan 1995
• Description logics Brachman et. al., 1984

22
Web Services

• More and more, web services become the typical
  application in semantic web scenario.
• Two ways aligning web services with semantic
  annotation
• Web service annotation Brodie 2003
• Semantic annotation web service

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Summary and Future Challenges

• Annotation for the semantic web
• Enable machine-understandable web
• Support semantic searching
• Support global-wide web services
• Still an unsolved problem
• Main technical challenges
• Direct ontology-driven annotation mechanism
• Concept disambiguation
• Automatic domain ontology generation
• Scalability