1. Little Knowledge Rules The Web:
Domain-Centric Result Page Extraction
Tim Furche, Georg Gottlob, Giovanni Grasso,
Giorgio Orsi, Christian Scallhart, Cheng Wang
Department of Computer Science
University of Oxford
Cheng.wang@trinity.ox.ac.uk

2. Result Page Understanding

3. Outline
Adaptable Model-Based Extraction of
Result Pages (AMBER)
• System Overview
• Experiments
• Current Work

4. AMBER: System Overview
Needs only Very high
one clue precision & recall
Adaptable Model-Based Extraction of Result Pages
Implemented Domain-Parameterized tool,
in rules currently aimed at UK real-estate
Part of DIADEM | Domain-centric Intelligent
Automated Data Extraction Methodology

5. AMBER: System Overview

6. Fact Generation & Annotation
• Live browser (Mozilla XUL-Runner)
• Extract DOM tree
• CSS box information
• Textual annotation with GATE (domain dep.)
– Gazetteers
– Regular expression like rules
• All represented as facts in the Page Model

7. Phenomenological Mapping
Fact  Attribute
• Attribute Model:
– Types & constraints
• Dom node and attribute
• Attribute Creation Constraints:
– Required Annotations
– Disallowed Annotations

8. Segmentation Mapping: Identification
Attribute  Data area
• From bottom phenomena to data area
• Little knowledge rules the web 
Only one domain concept
(mandatory attribute)
– Price
– Location
– Title

9. Segmentation Mapping: Identification
• Multi data area identification

10. Segmentation Mapping: Understanding
• Data area  Record
• Domain independent
• Identify leading nodes
• Two problems
– Superfluous nodes
– Correct shift

11. Segmentation Mapping: Understanding

12. Segmentation Mapping: Understanding

13. Experiments
100.0%
99.0%
98.0%
Precision
Recall
97.0%
F-measure
96.0%
95.0%
Data Area Record Attribute Price Location

14. Summary
• AMBER - Adaptable Model-based Extraction of
Result Pages
– Domain knowledge  simple heuristic
– Using DLV  compact & easy implementation
– Understanding phase: only one domain clue 
quickly adaptable to new domains
– Very High precision (99.4%) recall (99.0%)

15. Current Work
• Testing AMBER on another domain
• Integrate visual information in understanding
phase
• Use probabilistic logic programming to improve
the whole system

16. Thanks!