=Paper=
{{Paper
|id=Vol-3320/paper11
|storemode=property
|title=SemInt at SemTab 2022
|pdfUrl=https://ceur-ws.org/Vol-3320/paper11.pdf
|volume=Vol-3320
|authors=Abhisek Sharma,Sumit Dalal,Sarika Jain
|dblpUrl=https://dblp.org/rec/conf/semweb/SharmaD022
}}
==SemInt at SemTab 2022==
https://ceur-ws.org/Vol-3320/paper11.pdf
SemInt at SemTab 2022
Abhisek Sharma1,∗,† , Sumit Dalal1,† and Sarika Jain1,†
1
National Institute of Technology Kurukshetra, India.
Abstract
In this paper we present SemInt, for SemTab 2022 challenge of ISWC 2022. This is SemInt’s first
participation to the challenge. This challenge is about annotating tabular data from publically available
knowledge graphs (such as Wikidata/DBPedia). We propose a model named as SemInt that runs iterative
SPARQL query over Wikidata/DBPedia SPARQL endpoints for each term available a given table. For
handling misformed or differing representations of terms or entities in the table, SemInt queries the
Wikidata or DBPedia API’s and find the suitable matches for them. It also employs a search engine to
address typos in the terms. This year SemInt participated for CTA task and got some encouraging results
with 0.794 Precision and F-measure. We plan to extend it for CEA and CPA as well.
Keywords
Entity annotation, Table interpretation, Knowledge graph, SemInt, SemTab
1. Introduction
Web pages contains information of various dimensions. However, most of this information
is present in the tables. Tables occupies relational data in various fields and are sources of
high-quality data with lesser noise than unstructured text which is useful for various tasks
knowledge graph augmentation [1] and knowledge extraction [2]. Hence tables can not be
ignored while moving to the Web 3.0. Simple data (without any annotation) from tables don’t
have much meaning, but annotated tables are valuable sources and has critical research value.
Semantic annotation of the tabular data has gained much attention in recent years. Most of the
works employs probabilistic graphical models for the annotation purpose [3], [4]. There are
several units in a table which can be annotated like cells, columns. A column or pair of columns
can be assigned to entities, while relationship between two columns can be annotated to two
cells from these columns. Though there are many benefits of annotating tables and employing
them in knowledge extraction assignments. However, due to diverse languages and noise
mentions, interpreting semantic data from tables by machines is not easy. SemTab chellenge is
organized every year since 2019 on tabular data to Wikidata or DBpedia matching [5]. This
year’s challenge is to match the tabular data to Wikidata, DBpedia and Schema.org properties
or classes depending on the rounds of the challenge. A new set of difficulties such as larger-
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open abhisek_61900048@nitkkr.ac.in (A. Sharma); sumitdalal9050@gmail.com (S. Dalal); jasarika@nitkkr.ac.in
(S. Jain)
Orcid 0000-0003-1568-2625 (A. Sharma); 0000-0002-8736-2148 (S. Dalal); 0000-0002-7432-8506 (S. Jain)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Property in KG
(CPA)
Country State Capital
Entity in KG
(CEA) India Rajasthan Jaipur
USA California Sacramento
Germany Bayern München
France Normandy Rouen
Type in KG
(CTA)
Figure 1: Tasks in SemTab 2022
scale knowledge graph setting, knowledge graph data shifting, and noisy schema structure of
multiple knowledge graphs have followed. Additionally, this year’s challenge also has a more
challenging dataset (the tough tables [6]), which is manually curated, offering realistic issues
than the last challenge. The Semantic Web Challenge on Tabular Data to Knowledge Graph
Matching (SemTab 2022) aims at benchmarking tabular data to knowledge graph matching
systems. The challenge consists of three tasks: Column Type Annotation (CTA), Cell Entity
Annotation (CEA) and Column Property Annotation (CPA). The CTA task is assigning a semantic
type to a column, the CEA task is matching cells to entities in a specific KG, and the CPA task is
assigning a KG property to the relationship between two columns. These three tasks and their
formal definitions can be illustrated by Figure 1.
We have proposed an approach to solve the CTA task, where internally as insights we have
used approach that gives some results for the CEA task, though we have not individually
participated for CEA. For CTA task, we have used Wikidata/DBPedia SPARQL endpoint to
query individual entities from each column and proceed from there.
Outline. The rest of the paper is organised as follows: Section 2 of the paper presents work
from previous year SemTab challenges. Section 3 defines the proposed approach to solve the
CTA task while Section 4 discusses the results for one rounds. Conclusion and future direction
of this work is given in the last Section number 5.
2. Related Work
MTab tool supports multilingual tables and could process various table formats [7]. Referent
entity for a cell in table is detected using a graphical model with iterative probability propagation
algorithm in [8]. MTab4Wikidata [9] considers statement search and fuzzy search to handle
noise mentions which improves entity search. Some works provided new formula for ranking
the matching results such as DAGOBAH [10], MantisTable SE [11]. MTab system [12] is based
on an aggregation of multiple cross-lingual lookup services and probabilistic graphical model.
CSV2KG (IDLab) also uses multiple lookup services to improve matching performance [13].
Tabular ISI implements the lookup part with Wikidata API and Elastic Search on DBpedia
Refined Term Extracting "Including
results for"
Outer Loop: Select a column If first time results
pt are e
Inner Loop: Select a cell of the are empty, refine
em lts tim
su nd
Tables column terms
re eco
with DB API
y
s
If
Type with maximum
Result Table
Preprocessed frequency selected
Empty?
Table If results are as column type
Term1 Type fetched from KG
not empty
Term2 Type fetched from KG
If results
are empty No
third time
Term3 Type fetched from KG
Skip Term
Leave term
Yes
Figure 2: SemInt Architecture
labels, and aliases [14]. ADOG [15] system also uses Elastic Search to index knowledge graph.
LOD4ALL first checks whereas there is available entity which has a similar label with table cell
using ASK SPARQL, else perform DBpedia entity search [16]. DAGOBAH system performs
entity linking with a lookup on Wikidata and DBpedia; the authors also used Wikidata entity
embedding to estimate the entity type candidates [17]. Mantis Table provides a Web interface
and API for tabular data matching [18].
3. Proposed Model
This section describes the architecture of our proposed system, named SemInt, whose various
components are depicted in Figure 2. We have participated for the first time in SemTab, in the
CTA task only. SemInt follows a simple, yet with decent results, majority-voting-based lookup
approach: Cell contents are looked up in the SPARQL endpoint of the target KG, and in case of
null results, looked up again on a search engine (DuckDuckGo) for fixing typos. The returned
entity type with the highest number of votes per column is assigned as the type of that column.1
Assumptions
SemInt is developed keeping some assumptions in mind.
1. Assumption 1 We assume that the input table contains values horizontally, i.e., column
represent values of same type.
2. Assumption 2 The cell and column types defined in Wikidata/DBPedia uses rdf:type
and are of type owl:class.
3.1. Loading of tables and Selection of terms
A set of file with tables are provided in the beginning. Iteratively single files are selected and
loaded as dataframe. SemInt then iterate over columns of loaded table selecting one at a time.
Terms are then selected out of the selected column.
1
Can be accessed through: https://github.com/abhiseksharma/SemInt
3.2. Lookup
The chosen term is supplied through a SPARQL query to retrieve various term types from the
online DBpedia/Wikidata repository. If no result is received from the knowledge graph for
any term then that term will be passed via respective API (DBpedia API or Wikidata API) to
obtain the candidate representation of the term. This is done because an empty result may
be caused by a difference in representation between the term stored in DBPedia/Wikidata
and the representation in the table(like lowercase or camelcase, use of punctuations). Out of
all the returned terms, first term is selected as in The query is then executed again once the
candidate term has been obtained. If the result is still empty, the term is passed through a
search engine (this version of SemInt uses DuckDuckGo search engine) to catch any typos by
extracting ”including results for” part of the search result. DBPedia/Wikidata may have some
representations that are accurately listed in the table but on which search engines may become
confused, because of which this was not done in the first place. After the search engine has
corrected any typos, the query is run one last time to seek for results that aren’t empty. SemInt
skips it and proceeds on to the following term in the line if the result is still empty.
When a result is not empty, it is saved as a table with terms in one column and types returned
by the repository in the other.
We have used following SPARQL Query for the above lookup:
select DISTINCT ?o where
{?s rdfs:label @en . ?s
wdt:P31 ?o .}
The in the above query is the entry/concept/term in the cell of the dataset which will
be queried for its type in DBPedia or WikiData (based on the dataset).
3.3. Type Selection
The frequency of entity types in the saved term-type table is taken into consideration while
choosing the column type (for the CTA task). The column type is determined by the entity type
with the highest frequency.
4. SemInt Performance and Results
This sections presents the performance and result of SemInt at SemTab 2022 in 1 out of the 3
rounds (i.e., Round 1) in which SemInt participated.
SemInt did went through the execution on dataset of round 2 and 3. In round 2, SemInt was
able to get partial results locally, though was unable to execute completely due to some external
factors. So, we had to skip submission for round 2. For round 3, SemInt ran completly on the
dataset and produced some results, though after submission the evaluation scores (F1, recall,
precision) came out as 0, we suspect the output KG types were represented in wrong format in
the submitted CSV file.
Round 1
This year first round has 3 tasks, CTA-WD(Column Type Annotation using Wikidata), CEA-WD
(Cell Entity Annotation using Wikidata), and CPA-WD (Annotating two columns with property
on Wikidata). SemInt submitted results for CTA-WD task of Round 1 this year. The comparative
results are presented in table 1
Table 1
Result of Round 1 for CTA-WD task
System Precision F1
DAGOBAH 0.975 0.975
s-elBat 0.951 0.957
Kepler-aSI 0.944 0.944
KGCODE-Tab 0.944 0.942
JenTab 0.940 0.938
AMALGAM 0.793 0.786
Laurent 0.785 0.770
SemInt 0.794 0.794
5. Conclusion
This paper presented the first version of SemInt approach. We are participating in this challenge
for the first time. We have used a combination of strategies and treatment to tackle the tasks
of SemTab 2022 and achieved encouraging performance. We have performed preprocessing,it-
erative term improvement techniques, and then iterative querying over SPARQL endpoint of
Wikidata/DBPedia.
SemInt injects cell contents of a table into a generic SPARQL query. SemInt at SemTab 2022
is a promising approach, but which will be further improved. Our focus will be to decrease the
complexity of the system in terms of space and time requirements. We will try to incorporate
some Big Data or machine learning approaches to improve data processing. To speed up the
process and handle the problem of large data we will employ parallel processing techniques and
varying search strategies. Eventually, we want to cater the system for all the tasks i.e., CTA,
CEA, and CPA over all the data sources.
References
[1] D. Ritze, O. Lehmberg, Y. Oulabi, C. Bizer, Profiling the potential of web tables for
augmenting cross-domain knowledge bases, in: Proceedings of the 25th international
conference on world wide web, 2016, pp. 251–261.
[2] D. Wang, P. Shiralkar, C. Lockard, B. Huang, X. L. Dong, M. Jiang, Tcn: Table convolutional
network for web table interpretation, in: Proceedings of the Web Conference 2021, 2021,
pp. 4020–4032.
[3] G. Limaye, S. Sarawagi, S. Chakrabarti, Annotating and searching web tables using entities,
types and relationships, Proceedings of the VLDB Endowment 3 (2010) 1338–1347.
[4] V. Mulwad, T. Finin, A. Joshi, Semantic message passing for generating linked data from
tables, in: International Semantic Web Conference, Springer, 2013, pp. 363–378.
[5] E. Jiménez-Ruiz, O. Hassanzadeh, V. Efthymiou, J. Chen, K. Srinivas, Semtab 2019: Re-
sources to benchmark tabular data to knowledge graph matching systems, in: European
Semantic Web Conference, Springer, 2020, pp. 514–530.
[6] V. Cutrona, F. Bianchi, E. Jiménez-Ruiz, M. Palmonari, Tough tables: Carefully evaluating
entity linking for tabular data, in: International Semantic Web Conference, Springer, 2020,
pp. 328–343.
[7] P. Nguyen, I. Yamada, N. Kertkeidkachorn, R. Ichise, H. Takeda, Semtab 2021: Tabular data
annotation with mtab tool., in: SemTab@ ISWC, 2021, pp. 92–101.
[8] L. Yang, S. Shen, J. Ding, J. Jin, Gbmtab: A graph-based method for interpreting noisy
semantic table to knowledge graph., in: SemTab@ ISWC, 2021, pp. 32–41.
[9] P. Nguyen, I. Yamada, N. Kertkeidkachorn, R. Ichise, H. Takeda, Mtab4wikidata at semtab
2020: Tabular data annotation with wikidata., SemTab@ ISWC 2775 (2020) 86–95.
[10] V.-P. Huynh, J. Liu, Y. Chabot, T. Labbé, P. Monnin, R. Troncy, Dagobah: Enhanced scoring
algorithms for scalable annotations of tabular data., in: SemTab@ ISWC, 2020, pp. 27–39.
[11] M. Cremaschi, R. Avogadro, A. Barazzetti, D. Chieregato, E. Jiménez-Ruiz, O. Hassanzadeh,
V. Efthymiou, J. Chen, K. Srinivas, Mantistable se: an efficient approach for the semantic
table interpretation., in: SemTab@ ISWC, 2020, pp. 75–85.
[12] P. Nguyen, N. Kertkeidkachorn, R. Ichise, H. Takeda, Mtab: matching tabular data to
knowledge graph using probability models, arXiv preprint arXiv:1910.00246 (2019).
[13] B. Steenwinckel, G. Vandewiele, F. De Turck, F. Ongenae, Csv2kg: Transforming tabular
data into semantic knowledge, SemTab, ISWC Challenge (2019).
[14] A. Thawani, M. Hu, E. Hu, H. Zafar, N. T. Divvala, A. Singh, E. Qasemi, P. A. Szekely,
J. Pujara, Entity linking to knowledge graphs to infer column types and properties.,
SemTab@ ISWC 2019 (2019) 25–32.
[15] D. Oliveira, M. d’Aquin, Adog-annotating data with ontologies and graphs, in: SemTab@
ISWC, 2019.
[16] H. Morikawa, Semantic table interpretation using lod4all., SemTab@ ISWC 2019 (2019)
49–56.
[17] J. Liu, R. Troncy, Dagobah: an end-to-end context-free tabular data semantic annotation
system, SemTab@ ISWC (2019).
[18] M. Cremaschi, R. Avogadro, D. Chieregato, Mantistable: an automatic approach for the
semantic table interpretation., SemTab@ ISWC 2019 (2019) 15–24.