The document describes AMBER, a system for extracting structured data from result pages using little domain knowledge. AMBER uses a browser to extract the DOM tree and annotate facts. It maps facts to attributes and attributes to data areas using domain-specific rules. Experiments show AMBER achieves very high precision and recall of over 99% in extracting attributes like price and location from real estate pages. Current work involves testing AMBER on other domains and improving the system using visual information and probabilistic logic programming.

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!