Radar Station is a cell-entity disambiguation plugin for semantic table interpretation (STI) systems. It leverages graph embeddings to enhance the table context in order to more accurately annotate very ambiguous cell entities.

1. Orange restricted
Radar Station
Using KG Embeddings for Semantic Table
Interpretation and Entity Disambiguation
Jixiong Liu Viet-Phi Huynh Yoan Chabot Raphaël Troncy
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26 October 2022

2. Radar Station-ISWC 2022
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What does this table mean?
Can the machine automatically interpret it?
… … …
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Context & Motivation

3. author
(P50)
Radar Station-ISWC 2022
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The New Jedi Order
(Q2743959)
Traitor
(Q7833036)
Ylesia
(Q8053998)
(P179)
Part of the series
30 July
2002
publication date
(P577)
3 September
2002
Matthew Stover
(Q1909623)
Walter Jon Williams
(Q714485)
author
(P50)
… … …
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Semantic Table Interpretation using Knowledge Graphs

4. … … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
• Column-Type Annotation (CTA)
• Columns-Predicate Annotation (CPA)
• Cell-Entity Annotation (CEA) - Our focus
• Table Topic Annotation
• Row-to-Instance Traitor (literary work):
Q7833036 on Wikidata
author: Matthew Stover
part of the series: The New Jedi Order
publisher: Del Rey Books
publication date: 30 July 2002
media franchise: Star Wars …
Traitor (literary work):
Q21161161 on Wikidata
author: Stephen Daisley
country of origin: Australia
publication date: 2010
language of work or name: English …
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Traitor (literary work):
Q7833036 on Wikidata
author: Matthew Stover
part of the series: The New Jedi Order
publisher: Del Rey Books
publication date: 30 July 2002
media franchise: Star Wars …
Semantic Table Interpretation – Up to Five Tasks

5. Interne Orange
Semantic Table Interpretation - Related Work
• Heuristic-Based Approaches:
• Rely on features (e.g. relevance score) provided by a lookup service
• E.g., ADOG [1], BBW [2]
[1] Oliveira, D., d’Aquin, M.: Adog-annotating data with ontologies and graphs. In: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) (2019)
[2] Shigapov, R, Zumstein, P, Kamlah, J, Oberländer, L, Mechnich, J, Schumm, I., d’Aquin, M.: bbw: Matching CSV to Wikidata via Meta-lookup. In: Semantic Web Challenge on Tabular Data to Knowledge
Graph Matching (SemTab) (2020)
𝑠𝑖𝑚 = 1 − (
𝐿𝑒𝑣𝑒𝑛𝑠ℎ𝑡𝑒𝑖𝑛𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑠1, 𝑠2)
ma𝑥(𝑙𝑒𝑛𝑔𝑡ℎ 𝑠1 , 𝑙𝑒𝑛𝑔𝑡ℎ(𝑠2))
)
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s1: Label of the entity from the KG (string)
s2: Mention from the table cell (string)

6. Interne Orange
Semantic Table Interpretation - Related Work
• Heuristic-Based Approaches
• Iterative Disambiguation:
• Use the results of the CEA, CTA, CPA annotation tasks, in order to mutually
reinforce the compatibility between annotations
• Main shortcomings:
• Error propagation
• Background knowledge hidden in
the table is not used (e.g. all books
belong to a series)
• E.g., DAGOBAH [3], Mtab [4]
[3] Huynh, V.P., Liu, J., Chabot, Y., Deuzé, F., Labbé, T., Monnin, P., Troncy, R.: DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data. In: Semantic Web Challenge on
Tabular Data to Knowledge Graph Matching (SemTab) (2021)
[4] Nguyen, P., Yamada, I., Kertkeidkachorn, N., Ichise, R., Takeda, H.: Mtab4wikidata at semtab 2020: Tabular data annotation with wikidata. In: Semantic Web Challenge on Tabular Data to Knowledge
Graph Matching (SemTab) (2020)
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7. Interne Orange
Semantic Table Interpretation - Related Work
• Heuristic-Based Approaches
• Iterative Disambiguation
• Usage of Graph Embeddings:
• Use pre-trained graph embeddings for
augmenting information about entities
• Main shortcoming: the embeddings
quality depends on the density of the
graph
• E.g., Vasilis et al [5], DAGOBAH-
Embeddings [6]
[5] Efthymiou, V., Hassanzadeh, O., Rodriguez-Muro, M., Christophides, V.: Matching web tables with knowledge base entities: from entity lookups to entity embeddings. In: 16th International Semantic
Web Conference (ISWC). pp. 260–277. Springer (2017)
[6] Chabot, Y., Labbe, T., Liu, J., Troncy, R.: DAGOBAH: an end-to-end context-free tabular data semantic annotation system. In: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching
(SemTab). pp. 41–48 (2019)
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8. Our Approach: Radar Station
Annotation
System
Tables
Ambiguity
Detection
Radar Station
Disambiguation
Output
KG Embeddings
Candidate Scores Ambiguities
& Context
Context Entities
Selection
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Ambiguity
Detection
Context Entities
Selection
Radar Station
Disambiguation
Detect potential errors caused by error propagation
Capture more semantic similarities (from the embeddings)
Disambiguation by hybridizing entity scores and embeddings distance
Radar Station is a plug-in module for an
existing STI system (typically using iterative
disambiguation) that will benefit from pre-
trained embeddings as data augmentation

9. … … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
Why is this table difficult to interpret?
• The table lacks context, e.g., for the target column (book titles), who are the authors?
• The information about the book series (Star Wars) is not present in the table
• Matching “2002” with “30 July 2002” is not trivial
Traitor (Literary work):
Q7833036 on Wikidata
author: Matthew Stover
part of the series: The New Jedi Order
publisher: Del Rey Books
publication date: 30 July 2002
media franchise: Star Wars …
Traitor (Literary Work):
Q21161161 on Wikidata
author: Stephen Daisley
country of origin: Australia
publication date: 2010
language of work or name: English …
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10. DAGOBAH SL results: 2 candidates with an equal score
Mtab results: the correct candidate is at the 4th rank
BBW results: no output for this cell
We use DAGOBAH SL as input system to illustrate this presentation
… … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
DAGOBAH SL scores:
{‘id': 'Q21161161’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q7833036’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q1536329’, ‘score’: 0.01164}, (Traitor - film)
…... ,
MTab scores:
{‘id’: ‘Q2435622’, ‘score’: 0.02546}, (Traitor - television series episode)
{‘id’: ‘Q16746183’, ‘score’: 0.02545}, (Traitor - television series episode)
{‘id’: ‘Q7833042’, ‘score’: 0.024468}, (Traitor - fictional character)
{‘id’: ‘Q7833036’, ‘score’: 0.024467}, (Traitor - literary work)
…... ,
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11. … … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
We aim to detect the cell annotations that need to be disambiguated
We set a tolerance t to select the top candidates
Example:
• If t = 1, Q21161161 and Q7833036 are top candidates
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DAGOBAH SL scores:
…
{‘id': 'Q21161161’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q7833036’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q1536329’, ‘score’: 0.01164}, (Traitor - film)
…... ]},
...

12. We aim to detect the cell annotations that need to be disambiguated:
We set a tolerance t to select the top candidates
Example:
• If t = 1, Q21161161 and Q7833036 are the top candidates
• If t = 0.7, Q1536329 is also considered among the top candidates (0.1164>0.16*0.7)
Top candidates are ambiguities that we need to disambiguate
… … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
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DAGOBAH SL scores:
…
{‘id': 'Q21161161’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q7833036’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q1536329’, ‘score’: 0.01164}, (Traitor - film)
…... ]},
...

13. … … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
If we only have one candidate in top candidates (e.g., row “Destiny’s Way” with
t = 1), we directly output the entity without Radar Station.
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DAGOBAH SL scores:
…
{‘id': ‘Q5265233’, ‘score’: 0.01600}, (Destiny’s Way - literary work)
{‘id’: ‘Q60172766’, ‘score’: 0.0102}, (Destiny - literary work)
{‘id’: ‘Q17010392’, ‘score’: 0.0102}, (Destiny - literary work)
…... ]},
...

14. … … …
2002 Enemy Lines: Rebel Dream
2002 Enemy Lines: Rebel Stand
2002 Traitor
2002 Destiny’s Way
2002 Ylesia E-book
Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
We aim to build a column-wised representation of table context with
candidates from the same column.
Collect all top candidates and their scores for a given t from the same column as
the context entities (e.g., t =1)
…
{‘row’: 15, ‘column’ : 1,
‘Annotations’: [
{‘id': 'Q21161161’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q7833036’, ‘score’: 0.01600}, (Traitor - literary work)
{‘id’: ‘Q1536329’, ‘score’: 0.01164}, (Traitor - film) …... ]},
{‘row’: 16, ‘column’ : 1,
{‘Annotations’: [
{‘id’: ‘Q5265233’, ‘score’: 0.01600}, (Destiny’s Way - literary work)
{‘id’: ‘Q60172766’, ‘score’: 0.0102}, (Destiny - literary work)
{‘id’: ‘Q17010392’, ‘score’: 0.0102}, (Destiny - literary work) ….. ]},
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15. Radar Station - Intuition Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
Distance
Initial
power
receiver sender
Distance Embeddings distance
Signal power scoring from a previous annotation system
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16. Radar Station - Intuition Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
Star by Star
Enemy Lines: Rebel Stand
Enemy Lines: Rebel Dream
Destiny’s Way
Ylesia
Dark Journey
Traitor
Q7833036
Traitor
Q21161161
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17. Experiment - Embeddings Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
Leverage Pytorch-Biggraph [7] for training embeddings
Experiment with:
• 2 translational distance models:
TransE, RotatE (GraphVite [8] pre-trained embeddings)
• 2 semantic matching models: DistMult, ComplEx
[7] Lerer, A., Wu, L., Shen, J., Lacroix, T., Wehrstedt, L., Bose, A., Peysakhovich, A.: Pytorch-biggraph: A large scale graph embedding system. In: Conference onMachine Learning and
Systems (MLSys). vol. 1, pp. 120–131 (2019)
[8] Zhu, Z., Xu, S., Tang, J., Qu, M.: Graphvite: A high-performance cpu-gpu hybrid system for node embedding. In: The World Wide Web Conference (WWW). pp. 2494–2504 (2019)
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18. Radar Station Annotation
System
Tables
Candidate
Scores
Context Entities
Selection
Ambiguity
Detection
Radar Station
Disambiguation
Ambiguities
& Context
KG Embeddings
Output
Using table context to disambiguate ambiguities
𝑎𝑚𝑖: An ambiguity (one of the top candidates
previously selected)
𝑒𝑗: A context entity, i.e. a candidate entity for
another cell from the same column
𝑆𝑐(𝑒𝑗): Score of the context entity 𝑒𝑗
…
{‘row’: 15, ‘column’ : 1,
‘Annotations’: [
{‘id': 'Q21161161’, ‘score’: 0.01600},
{‘id’: ‘Q7833036’, ‘score’: 0.01600},
{‘row’: 16, ‘column’ : 1,
{‘Annotations’: [
{‘id’: ‘Q5265233’, ‘score’: 0.01600},
{‘id’: ‘Q60172766’, ‘score’: 0.0102},
{‘id’: ‘Q17010392’, ‘score’: 0.0102}, ….. ]},
...
Table context
Incorrect candidate
Correct candidate
-- Ambiguities
-- Context
𝐹 𝑎𝑚𝑖 =
1
𝐾
෍
𝑗<𝐾
(
𝑆𝑐(𝑒𝑗)
𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑎𝑚𝑖, 𝑒𝑗)
)
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19. Experiment – Dataset
Limaye, T2D - Web tables
2T_v2 - Synthetic tables
ShortTables - Built from T2D (each table only contains two rows, reduce context)
Gold
standard
# Table Avg.
#Rows
Avg. #Col #Entities Ambiguities (t=1) Ambiguities (t=0.9)
Limaye 437 37 2 5,143 181 (3.52%) 685 (13.31%)
T2D 762 157 5 18,589 2,322 (12.49%) 8,852 (47.62%)
2T_v2 180 1080 5 661,297 30,686 (4.64%) 86,739 (13.11%)
ShortTables 2237 2 5 4,474 1422 (31.78%) 1822 (40.72%)
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20. Evaluation - Metrics
𝐴𝑃 =
# 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑐𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒 𝑠𝑒𝑡 𝑜𝑓 𝑎𝑚𝑏𝑖𝑔𝑢𝑖𝑡𝑖𝑒𝑠
# 𝐴𝑚𝑏𝑖𝑔𝑢𝑖𝑡𝑖𝑒𝑠
𝑃𝐴 =
# 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑎𝑚𝑏𝑖𝑔𝑢𝑖𝑡𝑦 𝑑𝑖𝑠𝑎𝑚𝑏𝑖𝑔𝑢𝑎𝑡𝑖𝑜𝑛𝑠
# 𝐴𝑚𝑏𝑖𝑔𝑢𝑖𝑡𝑖𝑒𝑠
GP =
# 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑎𝑛𝑛𝑜𝑡𝑎𝑡𝑖𝑜𝑛𝑠
# 𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑒𝑙𝑠
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AP: the quality of the ambiguity set
PA: the precision for Radar Station over the ambiguity set
GP: the global precision among all annotations with or without Radar Station

21. Evaluation - Improvements on all datasets (regardless of the embeddings type)
Methods
Limaye T2D 2T_v2 ShortTable
AP PA GP AP PA GP AP PA GP AP PA GP
DAGOBAH SL 0.296 0.853 0.180 0.785 0.208 0.870 0.302 0.654
RS+TransE 0.528 0.872 0.312 0.815 0.230 0.872 0.414 0.673
RS+RotatE 0.614 0.542 0.873 0.332 0.312 0.815 0.327 0.235 0.872 0.671 0.418 0.674
RS+DistMult 0.377 0.860 0.230 0.797 0.213 0.870 0.328 0.659
RS+ComplEx 0.435 0.864 0.233 0.798 0.219 0.870 0.334 0.660
Radar Station evaluation based on DAGOBAH SL scores, t=0,95.
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AP: the quality of the ambiguity set
PA: the precision for Radar Station over the ambiguity set
GP: the global precision among all annotations with or without Radar Station

22. AP: the quality of the ambiguity set
PA: the precision for Radar Station over the ambiguity set
GP: the global precision among all annotations with or without Radar Station
Evaluation - Improvements for all base input systems
Dataset System t AP
Original Output Radar Station
PA GP PA GP
Limaye
DAGOBAH SL 0.9 0.653 0.432 0.853 0.578 (+0.146) 0.873 (+0.020)
MTab 0.83 0.820 0.705 0.857 0.787 (+0.082) 0.875 (+0.018)
BBW 0.65 0.587 0.359 0.563 0.507 (+0.148) 0.597 (+0.034)
T2D
DAGOBAH SL 0.95 0.332 0.180 0.785 0.312 (+0.132) 0.815 (+0.030)
MTab 0.71 0.385 0.295 0.837 0.346 (+0.051) 0.857 (+0.020)
BBW 0.65 0.263 0.192 0.364 0.253 (+0.061) 0.382 (+0.018)
Radar Station evaluation on Web tables with DAGOBAL SL, Mtab and BBW, with RotatE
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23. AP: the quality of the ambiguity set
PA: the precision for Radar Station over the ambiguity set
GP: the global precision among all annotations with or without Radar Station
Evaluation - More improvements on Web tables than on synthetic tables
More improvements on Web tables (Max +3%) than synthetic tables (Max +0.2%)
- Synthetic tables lack the inclusion of common themes.
Web Tables Synthetic Tables
Methods Limaye T2D 2T_v2
AP PA GP AP PA GP AP PA GP
DAGOBAH SL 0.296 0.853 0.180 0.785 0.208 0.870
RS+TransE 0.528 0.872 0.312 0.815 0.230 0.872
RS+RotatE 0.614 0.542 0.873 0.332 0.312 0.815 0.327 0.235 0.872
RS+DistMult 0.377 0.860 0.230 0.797 0.213 0.870
RS+ComplEx 0.435 0.864 0.233 0.798 0.219 0.870
Radar Station evaluation based on DAGOBAH SL scores. t=0.95
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24. Evaluation - Not specific improvements over simulated extreme conditions
The contribution of Radar Station is minimal in T2D and ShortTable (Max +3%)
• More ambiguities +
• Less context -
Methods
T2D ShortTable
AP PA GP AP PA GP
DAGOBAH SL 0.180 0.785 0.302 0.654
RS+TransE 0.312 0.815 0.414 0.673
RS+RotatE 0.332 0.312 0.815 0.671 0.418 0.674
RS+DistMult 0.230 0.797 0.328 0.659
RS+ComplEx 0.233 0.798 0.334 0.660
Radar Station evaluation based on DAGOBAH SL scores. t=0.95
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AP: the quality of the ambiguity set
PA: the precision for Radar Station over the ambiguity set
GP: the global precision among all annotations with or without Radar Station

25. The results are similar for embeddings from the same family
Translational distance models are better than semantic matching models
t
Models Limaye
Class System AP PA GP
0.95
- DAGOBAH SL 0.296 0.853
Translational
Distance
RS+TransE 0.528 0.872
RS+RotatE 0.614 0.542 0.873
Semantic
Matchin
RS+DistMult 0.377 0.860
RS+ComplEx 0.435 0.864
Evaluation - Translational distance models are better
Illustration of the Kappa test between different outputs,
t = 0.95.
Radar Station evaluation based on DAGOBAH SL scores.
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26. Interne Orange
Conclusion & Future Work
▪ Radar Station is a useful plug-in module for improving cell annotations!
Github: https://github.com/Orange-OpenSource/radar-station
Data and Models: https://zenodo.org/record/6522985
& https://zenodo.org/record/6522921
Slides: https://tinyurl.com/radar-station-iswc2022
▪ Future Work:
▪ Handle additional tables (beyond relational tables)
▪ Handle additional context (e.g. table caption, text surrounding the table, etc.)
▪ Downstream tasks (e.g., schemas augmentation, data imputation)
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