=Paper=
{{Paper
|id=Vol-2980/paper317
|storemode=property
|title=Demonstration of
MTab: Tabular Data Annotation with Knowledge Graphs
|pdfUrl=https://ceur-ws.org/Vol-2980/paper317.pdf
|volume=Vol-2980
|authors=Phuc Nguyen, Ikuya Yamada, Natthawut Kertkeidkachorn, Ryutaro Ichise, Hideaki Takeda
|dblpUrl=https://dblp.org/rec/conf/semweb/NguyenYKI021
}}
==Demonstration of
MTab: Tabular Data Annotation with Knowledge Graphs==
https://ceur-ws.org/Vol-2980/paper317.pdf
Demonstration of MTab: Tabular Data
Annotation with Knowledge Graphs
Phuc Nguyen1 , Ikuya Yamada2 , Natthawut Kertkeidkachorn3 ,
Ryutaro Ichise1 , and Hideaki Takeda1
1
National Institute of Informatics, Japan
2
Studio Ousia, Japan,
3
Japan Advanced Institute of Science and Technology, Japan
Abstract. This paper presents a demonstration of MTab, a tabular
data annotation toolkit with knowledge graphs: Wikidata, Wikipedia,
and DBpedia. MTab is the best performance system for all semantic
annotation tasks at the Semantic Web Challenges on tabular data to
knowledge graph matching SemTab 2019 and SemTab 2020. This pa-
per introduces MTab’s public APIs capable of structural and semantic
annotations for tabular data. We also provide a graphical interface to vi-
sualize the annotation results. The tool supports multilingual tables and
could process many table formats such as Excel, CSV, TSV, markdown
tables, or a pasted table content. MTab’s repository is publicly available
at https://github.com/phucty/mtab_tool.
Keywords: tabular data annotation · knowledge graph · semantic an-
notation · structural annotation · Wikidata · Wikipedia · DBpedia
1 Introduction
Many valuable tabular resources have been made available on the Internet and
Open Data Portals, thanks to the Open Data movement. However, the usage of
the tabular data is very limited in applications due to lacking or insufficient data
descriptions, various data formats, vocabulary issues. Tabular data usually do
not have a description, or the description does not cover data content. Tabular
data also lack specification on table structure, and layout. Moreover, many tables
do not use a standard vocabulary such as expressed in non-English, abbreviation,
ambiguous or contain many misspellings, encoding problems. It is crucial to have
a tabular data annotation system that could provide explicit information about
table content to improve tabular data usability.
Previous studies addressed many tabular data annotation tasks such as struc-
tural annotations [6], [9] or semantic annotations as the participant systems in
the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching:
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
SemTab 2019 [3], and SemTab 2020 [4]. Unfortunately, most solutions or sys-
tems are not available to use, or require extensive configuration, setup, high
computing power, or high time complexity [10].
This paper introduces MTab, a public service that generates structural and
semantic annotations for tabular data. The structural annotations provide in-
formation about table headers, the table core attribute. The semantic annota-
tions offer table elements matching knowledge graph concepts: cell-entity (CEA
task), column-type (CTA task), and CPA task where the relation between core
attribute to another column is annotated with a property. We also provide a
graphical interface to visualize the annotation results.
The major advantages of MTab compared to other systems are as follows.
– Effectiveness: MTab tool is the best performance system in SemTab 2019
[5], [3] and SemTab 2020 [7], [4]. The key success of MTab is on the entity
search modules with multilingual support (a keyword search with BM25
algorithm, a fuzzy search with edit distances, and an aggregation search
with weighted fusion of keyword search and fuzzy search). The fuzzy search
could support up to six edits (on the low-budget mac mini M1 2021), while
most other systems only support two edits. As a result, MTab could address
a higher level of noisiness compared to other systems. The entity search
module achieves 87.98% on average of the top 1 accuracy (the top 1000
accuracy is 99.7%) [8] on Semtab 2020 [4] and Tough Tables datasets [1].
– Efficiency: MTab fuzzy search implementation works efficiently with candi-
date filtering based on entity labels and hashing with pre-calculating entity
label deletes as the Symmetric Delete algorithm [2]. Moreover, the state-
ment search also gives a tremendous efficient improvement where it could
eliminate non-statements entity candidates. Additionally, we use a light way
solution as the value matching to calculate the context similarity between en-
tity candidate statements and table row values. The experiments show that
our solution could improve efficiency without losing effective performance [4].
Overall, it takes only 1.52 seconds/table on average (SemTab 2020 dataset)
to annotate with MTab.
– Easy to use: We provide public APIs, graphical interfaces so that users
do not need to do intensive setup or configuration. MTab also supports
multilingual and could process many table formats such as Excel, CSV, TSV,
or markdown tables. According to Wang et al., they only could generate the
annotations using the MTab tool, while other systems require high time
complexity to process [10].
– Privacy Policy: MTab does not store any data from users. All users’ tabular
data files are completely deleted after the annotation.
MTab’s repository, API documents, and other information could be accessed
at https://github.com/phucty/mtab_tool; the demonstration video is avail-
able at https://youtu.be/0ibTWeObWaA.
2 MTab
2.1 Knowledge Graphs
We build a WikiGraph from the dump data of Wikidata, Wikipedia, and DB-
pedia as the target knowledge graph the annotation tasks. Wikidata is the cen-
tral knowledge graph because it has the largest number of entities among the
three graphs. With the dump data on 1 January 2021, we extracted 91.2 mil-
lion entities and 249.3 million entity labels in multilingual, including entity la-
bels, aliases, other names, redirect entity labels, and disambiguation entities. We
also extracted 3.5 billion triples in WikiGraph. Additionally, WikiGraph will be
updated frequently based on the future released dumps of knowledge graphs
(Wikidata, Wikipedia, and DBpedia).
2.2 Entity Search Modules
Entity Search on a Cell We introduce the search modes1 as follows [8].
– Keyword search with BM25 algorithm: We use the hyper-parameters
as b = 0.75, k1 = 1.2.
– Fuzzy search with edit distance: We use Damerau–Levenshtein distance
as the edit distance for fuzzy search. We also perform candidate filtering and
hashing with pre-calculating entity label deletes as the Symmetric Delete
algorithm [2] to reduce the number of operations on pairwise edit distance
calculation. Overall, MTab could support the fuzzy search up to six edits.
– Aggregation search: This module is a weighted fusion of the keyword
search and the fuzzy search results.
Statement Search on Two Cells This module is built on the assumption
that there is a logical relation between two cells of a table row, equivalent to a
knowledge graph triple. We only keep the candidates of the two cells that have
equivalent statements in the WikiGraph. We implement this statement search
with a sparse matrix of 91 million entities and around 500 million edges.
2.3 Table Annotation: Use Case and Demo
MTab demonstration is available at https://mtab.app. Users could submit ta-
ble files in various table formats, expressed in any language to MTab API, or
copy data content and paste it to the interface. Then, users could tap to the “An-
notate” button to get the annotation results. MTab will perform the following
steps.
The annotation procedure2 are as the following steps:
1
Entity Search Documents: https://mtab.app/mtabes/docs
2
Table Annotation Document: https://mtab.app/mtab/docs
Fig. 1: Example tabular data annotation with MTab
– Pre-processing The input tables are pre-processed with encoding predic-
tion, table type prediction, data type prediction for cells and columns.
– Structural Annotations: Then, we perform header detection based on
majority voting of column data type as [6]. The core attribute detection is
based on the uniqueness of cell values in a column as [6][9].
– Semantic Annotations: MTab automatically predicts the matching tar-
gets based on data types, when the input does not have matching targets.
The CEA matching targets are the table cells whose data types are strings.
The CTA matching targets are columns so that the column data types are
strings. The CPA matching targets are the relation between the core at-
tribute and the remaining table columns. Then, we perform entity candidate
generation for each table cell with entity search and two cells in the same
row with statement search. We calculate context similarities with the value
matching between statements of entity candidates in the core attributes with
table row values. Finally, generate the annotations for entities, properties,
and types based on majority voting of context similarities [7].
Fig. 1 illustrate an annotation example for a SemTab dataset’s table. MTab
took 0.49 seconds to annotate a pasted table from the text box (left picture).
The photo on the right is the annotation results. The table header is in the
first row, and the core attribute is in the first column. Entity annotations are in
red and located below the table cell value. The type annotation is in green and
located in the “Type” column. Finally, the relations between the core attribute
and other columns are in blue and located in the property column.
3 Conclusions
This paper presents a demonstration of the MTab toolkit for table annotation
with knowledge graphs of Wikidata, DBpedia, and Wikipedia. MTab is effective,
efficient, and easy to use.
In the future work, we will focus on building downstream applications based
on MTab’s annotations such as question answering, and data analysis.
Acknowledgements
The research was supported by the Cross-ministerial Strategic Innovation Pro-
motion Program (SIP) Second Phase, “Big-data and AI-enabled Cyberspace
Technologies” by the New Energy and Industrial Technology Development Or-
ganization (NEDO).
References
1. Cutrona, V., Bianchi, F., Jiménez-Ruiz, E., Palmonari, M.: Tough tables: Care-
fully evaluating entity linking for tabular data. In: The Semantic Web - ISWC
2020. Lecture Notes in Computer Science, vol. 12507, pp. 328–343. Springer (2020),
https://doi.org/10.1007/978-3-030-62466-8_21
2. Garbe, W.: Symspell: Symmetric delete algorithm. https://github.com/
wolfgarbe/SymSpell (2012)
3. Jiménez-Ruiz, E., Hassanzadeh, O., Efthymiou, V., Chen, J., Srinivas, K.: Semtab
2019: Resources to benchmark tabular data to knowledge graph matching systems.
In: The Semantic Web - 17th International Conference, ESWC 2020. Lecture Notes
in Computer Science, vol. 12123, pp. 514–530. Springer (2020), https://doi.org/
10.1007/978-3-030-49461-2_30
4. Jimenez-Ruiz, E., Hassanzadeh, O., Efthymiou, V., Chen, J., Srinivas, K., Cutrona,
V.: Results of semtab 2020. In: SemTab@ISWC. CEUR Workshop Proceed-
ings, vol. 2775, pp. 1–8. CEUR-WS.org (2020), http://ceur-ws.org/Vol-2775/
paper0.pdf
5. Nguyen, P., Kertkeidkachorn, N., Ichise, R., Takeda, H.: Mtab: Matching tabu-
lar data to knowledge graph using probability models. In: SemTab@ISWC 2019.
CEUR Workshop Proceedings, vol. 2553, pp. 7–14. CEUR-WS.org (2019), http:
//ceur-ws.org/Vol-2553/paper2.pdf
6. Nguyen, P., Kertkeidkachorn, N., Ichise, R., Takeda, H.: Tabeano: Table to knowl-
edge graph entity annotation. CoRR abs/2010.01829 (2020), https://arxiv.
org/abs/2010.01829
7. Nguyen, P., Yamada, I., Kertkeidkachorn, N., Ichise, R., Takeda, H.:
Mtab4wikidata at semtab 2020: Tabular data annotation with wikidata. In:
SemTab@ISWC. vol. 2775, pp. 86–95 (2020), http://ceur-ws.org/Vol-2775/
paper9.pdf
8. Nguyen, P., Yamada, I., Takeda, H.: Mtabes: Entity search with keyword search,
fuzzy search, and entity popularities. In: The 35th Annual Conference of the
Japanese Society for Artificial Intelligence, JSAI 2021. vol. 2021. The Japanese So-
ciety for Artificial Intelligence, https://www.jstage.jst.go.jp/article/pjsai/
JSAI2021/0/JSAI2021_1N4IS1a02/_pdf
9. Ritze, D., Lehmberg, O., Bizer, C.: Matching html tables to dbpedia. In: Pro-
ceedings of the 5th International Conference on Web Intelligence, Mining and
Semantics, WIMS 2015. pp. 10:1–10:6. ACM (2015), https://doi.org/10.1145/
2797115.2797118
10. Wang, D., Shiralkar, P., Lockard, C., Huang, B., Dong, X.L., Jiang, M.: TCN:
table convolutional network for web table interpretation. In: WWW ’21: The Web
Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021. pp. 4020–
4032. ACM / IW3C2 (2021), https://doi.org/10.1145/3442381.3450090