TAIPAN: Automatic Property Mapping for Tabular Data by Ivan Ermilov and Axel-Cyrille Ngonga Ngomo in Proceedings of 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW'2016)

1. TAIPAN: Automatic
Property Mapping for
Tabular Data
by Ivan Ermilov and Axel-Cyrille Ngonga Ngomo
November 22nd, 2016
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2. Web Scale Data Mining from Web Tables
Web Data Commons
Dresden Table Dataset
Other tables
The Web
TAIPAN
● Structured
● Schemaless
● Not using standards*
● SPARQL
● RDFS
● OWL
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3. TAIPAN Approach Overview
Identify
Subject
Column
Atomize a
Table
Identify
Property for
Each Table
Step 1 Step 2 Step 3 Step 4
Return
Mappings
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4. TAIPAN Approach Overview (example)
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3
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5. The Core of TAIPAN
Subject Column
Identification
● Unsupervised ML
● Structural features
● Semantic features
○ Support of a column
○ Connectivity
● Retrieve seed entities
● Rank entities
● Return top entity
Property Mapping
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6. Experimental setup
For T2K: 128GB, 4 Cores, Ubuntu 14.04
For TAIPAN: 16GB, 4 Cores Ubuntu 14.04
Dataset 1: curated T2D gold standard (T2D)
Dataset 2: DBpedia table dataset (DBD)
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7. Subject Column Identification Experiments
Rule-based approach achieves
only 51.72% accuracy
Using support and connectivity
increase precision
Observations
Can be further improved using
ML techniques
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8. Property Mapping Experiments
TAIPAN achieves better recall,
but lower precision than T2D
On the DBD dataset T2K could
match only 1 property
Observations
Overall TAIPAN performs
better than the state of the art
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9. Conclusions & Future Work
Curated T2D & DBD datasets
Novel TAIPAN approach
Open Table Extraction
Table Extraction Benchmark (HOBBIT)
Integration of TAIPAN into GEISER project 9

10. Thank you!
Follow us on twitter
:)
Ivan Ermilov <iermilov@informatik.uni-leipzig.de>
@hobbit_project
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