=Paper= {{Paper |id=Vol-3320/paper5 |storemode=property |title=KGCODE-Tab Results for SemTab 2022 |pdfUrl=https://ceur-ws.org/Vol-3320/paper5.pdf |volume=Vol-3320 |authors=Xinhe Li,Shuxin Wang,Wei Zhou,Gongrui Zhang,Chenghuan Jiang,Tianyu Hong,Peng Wang |dblpUrl=https://dblp.org/rec/conf/semweb/LiWZZJHW22 }} ==KGCODE-Tab Results for SemTab 2022== https://ceur-ws.org/Vol-3320/paper5.pdf 
KGCODE-Tab Results for SemTab 2022
Xinhe Li1 , Shuxin Wang1 , Wei Zhou3 , Gongrui Zhang2 , Chenghuan Jiang2 ,
Tianyu Hong1 and Peng Wang1,2,3,βˆ—
1
  School of Computer Science and Engineering, Southeast University, China
3
  College of Software Engineering, Southeast University, China
2
  Chien-Shiung Wu College, Southeast University, China


                                         Abstract
                                         This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest
                                         SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in
                                         three tasks of the content: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns
                                         Property Annotation (CPA). The specific techniques used by KGCODE-Tab will be introduced briefly.
                                         The strengths and weaknesses of KGCODE-Tab will also be discussed.

                                         Keywords
                                         Tabular Data, Knowledge Graph, Entity Linking, KGCODE-Tab, Semantic Annotation




1. Presentation of the system
KGCODE-Tab, as a novel table annotation system, can efficiently deal with three tabular data to
knowledge graph matching (TDKGM) [1] tasks: Column Type Annotation (CTA), Cell Entity
Annotation (CEA), and Columns Property Annotation (CPA). Our system fully utilizes the
structure of tabular data and the information provided by knowledge graphs (KGs). Exper-
imental results on the SemTab 20221 datasets demonstrate that KGCODE-Tab has excellent
disambiguation ability and achieves outstanding performance with less query time.

1.1. State, purpose, general statement
The core principle of matching strategies of KGCODE-Tab is utilizing the structure of tabular
data and the information provided by KGs correctly and effectively. KGCODE-Tab mainly
consists of three modules: tabular data preprocessing, entity disambiguation, and task analysis.
SemTab@ISWC 2022, October 23–27, 2022, Hangzhou, China (Virtual)
βˆ—
    Corresponding author.
Envelope-Open lixinhe669@gmail.com (X. Li); shuxinwang662@gmail.com (S. Wang); zhouweiseu@seu.edu.cn (W. Zhou);
grzhang@seu.edu.cn (G. Zhang); quadnucyard@gmail.com (C. Jiang); tianyuhong677@gmail.com (T. Hong);
pwang@seu.edu.cn (P. Wang)
GLOBE https://github.com/Xinhe-Li (X. Li); https://github.com/A-BigTree (S. Wang); https://github.com/MyWhiteLip
(W. Zhou); https://github.com/TideDra (G. Zhang); https://github.com/QuadnucYard (C. Jiang);
https://github.com/Tianyu-Hong (T. Hong)
Orcid 0000-0002-6299-4229 (X. Li); 0000-0002-3677-8477 (S. Wang); 0000-0002-4558-245X (W. Zhou);
0000-0002-8342-5834 (G. Zhang); 0000-0002-3583-3569 (C. Jiang); 0000-0002-3773-7108 (T. Hong)
                                       Β© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
             CEUR Workshop Proceedings (CEUR-WS.org)
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073




1
    Semantic Web Challenge on Tabular Data to Knowledge Graph Matching-https://www.cs.ox.ac.uk/isg/challenges/
    sem-tab/
   KGCODE-Tab combines several effective tabular data preprocessing techniques, which are
fundamental for TDKGM. We analyze the structure of tabular data, which is helpful to extract
the subject column and non-subject columns, correct the spelling of texts in cells, and recall all
candidate entities and their information needed in the later modules. In the entity disambigua-
tion module, preliminary scores are assigned to all candidate entities of the cells in the subject
column, based on the similarities between tabular cells and property values in KGs. In each task,
a ranking algorithm is designed according to the preliminary scores, and finally we obtain the
semantic annotation based on the ranks. KGCODE-Tab separates the look-up step and entity
linking step, the latter can directly use the intermediate results produced by the former in JSON
files.
   In SemTab 2022, KGCODE-Tab is an efficient tabular data linking system, and some algorithms
and matching strategies of it have been designed for high efficiency.

1.2. Specific techniques used
KGCODE-Tab aims to provide high-quality semantic annotation of tabular data. The main
specific techniques used by KGCODE-Tab are as follows.

1.2.1. Table Structure Analysis
Firstly, KGCODE-Tab classifies each column into entity column and non-entity column. It
employs spaCy 2 , a python package for Named Entity Recognition (NER), to give each cell a
tag. A cell is an entity cell if it is tagged with P E R S O N , N O R P , F A C , O R G , G P E , L O C , P R O D U C T , E V E N T ,
W O R K _ O F _ A R T , L A W or L A N G U A G E . A cell is a non-entity cell if it is tagged with D A T E , T I M E , P E R C E N T ,
M O N E Y , Q U A N T I T Y , O R D I N A L or C A R D I N A L . Cells that cannot be recognized by spaCy are classified
into entity cells to prevent omissions. Then a column is an entity column if more than half of
its cells (except the header) are entity cells. Otherwise, it is a non-entity column.
    Secondly, KGCODE-Tab selects the subject column from the entity columns. It defines the
Column Entropy, which describes the diversity of contents in a column. The subject column
commonly has a higher value of the Column Entropy. If more than one subject columns exist,
then KGCODE-Tab selects the one with the smallest index.

1.2.2. Spell Correction
Tables on the Internet usually have misspelled words, and researches [2, 3] show that spelling
mistakes can make a huge difference to entity recall. Some systems [4, 5] remove special
characters in the text, but have no idea about the wrong words. Inspired by [6], KGCODE-Tab
utilizes search engines to find the correct words.
   For a tabular cell 𝑐𝑖𝑗 , KGCODE-Tab uses Bing3 to search it and obtains the result page in HTML
format. Secondly, it extracts the titles of websites in the HTML and splits them into words
𝒲 = {𝑀1 , 𝑀2 , … , 𝑀𝑛 }, where 𝑛 is the total number of words. Thirdly, it calculates the Levenshtein
Distance between 𝑀𝑖 , 𝑖 = 1, 2 … , 𝑛 and 𝑐𝑖𝑗 . Finally, the word with the shortest Levenshtein Distance

2
    https://github.com/explosion/spaCy
3
    https://www.bing.com/search
to 𝑐𝑖𝑗 is selected as the correct mention of 𝑐𝑖𝑗 , and words whose Levenshtein Distance to the correct
word are no more than 2 are also appended to the list of candidate mentions of 𝑐𝑖𝑗 , preventing
omissions.

1.2.3. Entity Recall
Entity recall aims to select several candidate entities from a given KG. If the system cannot even
recall the ground truth entities, then all the subsequent work is in vain. For the data source
of KG, Some systems [7, 8, 9] build their database using the Wikidata local dump. However,
the method requires high storage and IO performance of computers due to the huge size of
local dump files. Therefore, we use the look-up services MediaWiki Action API 4 and DBpedia
Lookup 5 to access the data of KGs online. We use 100 threads in entity query to improve query
speed and obtain up to 50 candidate entities for each query text.
   Furthermore, we find that the look-up services of KGs (Wikidata/DBpedia) are sensitive to
the noise in the query text, such as adverbs, adjectives, prepositions, and so on. They may lead
to wrong or empty results.
   To tackle this problem, we introduce the tokenization technique. For the text of cell 𝑐𝑖𝑗 with
𝑙 words t = [𝑑1 , 𝑑2 , … , 𝑑𝑙 ], KGCODE-Tab constructs a query set 𝒬 = {qπ‘–βˆΆπ‘— = [𝑑𝑖 , 𝑑𝑖+1 , … , 𝑑𝑗 ] | 𝑖, 𝑗 =
1, 2, … , 𝑙 and 𝑖 β©½ 𝑗}. Then it sends each qπ‘–βˆΆπ‘— in 𝒬 to the spell correction module and obtains the
candidate mention set β„³ of 𝑐𝑖𝑗 . Finally, it sends β„³ into the KGs API and gets the candidate
entities set β„°. It also collects the information of each entity into a dictionary containing its
label, description, statements, identifiers, and so on.

1.2.4. Entity Disambiguation
Entity disambiguation is to select the ground truth entity from candidate entities. The archi-
tecture of existing systems can be classified into two categories: Graph-based [7, 8, 10] and
Score-base [2, 4, 5, 11], and we design an algorithm to calculate the similarity score.
  Commonly, a table has at least one subject column, and the others are non-subject columns.
The non-subject columns are generally properties of subject columns. Therefore, KGCODE-
Tab can exclude some candidate entities of subject columns by comparing their properties
with the content of related non-subject columns. There are mainly six data types in Wikidata:
wikibase-entityid, string, time, globecoordinate, quantity, and multilingualtext, so we need to
design different formulas to calculate the similarity score according to different data types. Let
an entity 𝑒 has 𝑃 properties, and π‘£π‘˜ denotes the π‘˜-th property.
  For the string and multilingualtext data types, it is enough to rely on Levenshtein Distance.
For the wikibase-entityid data type, they need to be converted to labels firstly. The similarity
score formula is shown as follows:

                                                 πΏπ‘’π‘£π‘…π‘Žπ‘‘π‘–π‘œ(𝑐𝑖𝑗 , π‘£π‘˜ ), πΏπ‘’π‘£π‘…π‘Žπ‘‘π‘–π‘œ(𝑐𝑖𝑗 , π‘£π‘˜ ) β©Ύ 𝛼
                            π‘†π‘–π‘š(𝑐𝑖𝑗 , π‘£π‘˜ ) = {                                                           (1)
                                                 0,                   otherwise

where the optimal value of parameter 𝛼 is 0.98 which is obtained by experiments.
4
    https://wikidata.org/w/api.php
5
    https://lookup.dbpedia.org/
   For the quantity data type, we define the Number Relevance Degree (NRD) which is shown as
follows:
                                        |π‘Žβˆ’π‘|                        |π‘Žβˆ’π‘|
                                ⎧1 βˆ’ max(|π‘Ž|,|𝑏|) , π‘Žπ‘ β‰  0 and 1 βˆ’ max(|π‘Ž|,|𝑏|) β©Ύ 𝛽
                   𝑁 𝑅𝐷(π‘Ž, 𝑏) = 1 βˆ’ |π‘Ž βˆ’ 𝑏|,               π‘Žπ‘ = 0 and 1 βˆ’ |π‘Ž βˆ’ 𝑏| β©Ύ 𝛽                 (2)
                               ⎨
                               ⎩0,                         otherwise
                                          π‘†π‘–π‘š(𝑐𝑖𝑗 , π‘£π‘˜ ) = 𝑁 𝑅𝐷(𝑐𝑖𝑗 , π‘£π‘˜ )                            (3)
where the optimal value of parameter 𝛽 is 0.98 which is also obtained by experiments.
   For the globecoordina data type which contains longitude and latitude, we directly use NRD
to calculate the similarity score. The similarity score formula is shown as follows:

                           π‘†π‘–π‘š(𝑐𝑖𝑗 , π‘£π‘˜ ) = max (𝑁 𝑅𝐷(𝑐𝑖𝑗 , π‘£π‘˜π‘™π‘Ž ), 𝑁 𝑅𝐷(𝑐𝑖𝑗 , π‘£π‘˜π‘™π‘œ ))                (4)

   For the time data type, we define a list T which contains year, month, day, hour, minute, and
second to represent the time value. In tabular data, we use regular expressions for extracting
time information as a T. The similarity score formula is shown as follows:

                                                        1,     T𝑐𝑖𝑗 βŠ† Tπ‘£π‘˜
                                      π‘†π‘–π‘š(𝑐𝑖𝑗 , π‘£π‘˜ ) = {                                              (5)
                                                        0,     otherwise
  After the similarity scores calculation, each candidate entity has a final score calculated by
the formula:
                                               𝑁
                                         1
                              𝐹 𝑆(𝑒) =         βˆ‘ maxπ‘†π‘–π‘š(𝑐𝑖𝑗 , π‘£π‘˜ )                            (6)
                                       𝑁 βˆ’ 1 𝑗=1,𝑗≠𝑠 π‘£π‘˜ βˆˆπ‘ƒπ‘’
where 𝑒 is the candidate entity of the 𝑖-th cell in the subject column, 𝑠 denotes the column index
of subject column, and 𝑃𝑒 is the set of properties in 𝑒.

1.2.5. Task Analysis
                                                                                              β€²
In our system, we utilize a cooperative score mechanism. Let 𝑀(π‘’π‘–π‘˜ , 𝑐𝑖𝑗 ) and 𝑀(π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ ) denote
                                                 β€²
the matching score of (π‘’π‘–π‘˜ , 𝑐𝑖𝑗 ) or (π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ ) used later. We use a normalization function

                                                 πœ™(π‘₯) = (π‘Žπ‘₯)𝑏                                         (7)

to widen the gap between high and low matching score, where π‘Ž = 1.1 and 𝑏 = 8.

Column Type Annotation Let π‘’π‘–π‘˜ denote the π‘˜-th candidate entity of the 𝑖-th cell in the
subject column. Then the set of candidate types is π’žsub = {𝑑|(π‘’π‘–π‘˜ , InstanceOf, 𝑑) ∈ 𝐾 𝐺, 𝑖 =
1, 2, … , π‘š, π‘˜ = 1, 2, … 𝑁 (𝑐𝑖 )}, where 𝑁 (𝑐𝑖 ) is the number of candidate entities of the 𝑖-th cell. We
assign a score to each type 𝑑 in π’žsub by Eq.9.
                                        1
                                            βˆ‘ 𝑀(π‘’π‘–π‘˜ , 𝑐𝑖𝑗 ),
                                       𝑁 βˆ’1 𝑗≠𝑠
                                                                (π‘’π‘–π‘˜ , InstanceOf, 𝑑) ∈ 𝐾 𝐺
                               π‘˜
                     𝐼sub (𝑑, 𝑒𝑖 ) = {                                                                (8)
                                     0,                         otherwise
                                                             π‘š
                                                                 𝑁 (𝑐𝑖 )
                               𝐢𝑇 π΄π‘†π‘π‘œπ‘Ÿπ‘’π‘ π‘’π‘ (𝑑) = βˆ‘ max πœ™ (𝐼sub (𝑑, π‘’π‘–π‘˜ ))                        (9)
                                                           𝑖=1 π‘˜=1
  For non-subject columns, the score of candidate types in π’žnon are assigned by Eq.11.
                                                         β€²             β€²
                               π‘˜    π‘˜ β€²      𝑀(π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ ),      (π‘’π‘–π‘—π‘˜ , InstanceOf, 𝑑) ∈ 𝐾 𝐺
                     𝐼non (𝑑, 𝑒𝑖 , 𝑒𝑖𝑗 ) = {                                                     (10)
                                              0,                  otherwise
                                                         𝑀
                                                                                     β€²
                           𝐢𝑇 π΄π‘†π‘π‘œπ‘Ÿπ‘’π‘›π‘œπ‘› (𝑑𝑗 ) = βˆ‘ max
                                                    β€²
                                                      πœ™ (𝐼non (𝑑𝑗 , π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ ))                (11)
                                                         𝑖=1 π‘˜,π‘˜

Cell Entity Annotation For an entity in the subject column, we enumerate all types 𝑑 𝑒 of
candidates to take advantage of CTA scores, as shown in Eq.12, where the parameter πœ† is a
cooperative factor set to 0.1. We skip the items that makes 𝐼sub (β‹…, β‹…) or 𝑀(β‹…, β‹…) equals 0.

                 πΆπΈπ΄π‘†π‘π‘œπ‘Ÿπ‘’π‘ π‘’π‘ (π‘’π‘–π‘˜ ) = max {πœ™ (𝐼sub (𝑑 𝑒 , π‘’π‘–π‘˜ )) + πœ† β‹… 𝐢𝑇 π΄π‘†π‘π‘œπ‘Ÿπ‘’π‘ π‘’π‘ (𝑑 𝑒 )}      (12)
                                              𝑑,𝑒

  For a non-subject column with index 𝑗, we give the entity π‘’π‘–π‘—π‘˜ score by Eq.13.

                                     β€²       𝑁 (𝑐𝑖𝑗 )                  β€²
                πΆπΈπ΄π‘†π‘π‘œπ‘Ÿπ‘’π‘›π‘œπ‘› (π‘’π‘–π‘—π‘˜ ) = max
                                       β€²
                                          {πœ™ (𝑀(π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ )) + πœ† β‹… πΆπΈπ΄π‘†π‘π‘œπ‘Ÿπ‘’π‘ π‘’π‘ (π‘’π‘–π‘˜ )}          (13)
                                             π‘˜ =1

Columns Property Annotation The set of candidate properties is denoted by
𝒫 {𝑝 | (𝑒𝑖𝐺 , β„Žπ‘Žπ‘ π‘ƒπ‘Ÿπ‘œπ‘π‘’π‘Ÿπ‘‘π‘¦, 𝑝) ∈ 𝐾 𝐺, 𝑖 = 1, 2 … , π‘š}. We assign a score to each property 𝑝 in 𝒫
with respect to the 𝑗-th column by:
                                                             β€²              β€²
                                                𝑀(π‘’π‘–π‘˜ , π‘’π‘–π‘—π‘˜ ), (π‘’π‘–π‘˜ , 𝑝, π‘’π‘–π‘—π‘˜ ) ∈ 𝐾 𝐺
                              𝐼 (𝑝, π‘’π‘–π‘˜ ) = {                                                    (14)
                                                0,              otherwise

  The CPA matching score is calculated by Eq.15.
                                         𝑀
                                               𝑁 (𝑐𝑖 )
                 πΆπ‘ƒπ΄π‘†π‘π‘œπ‘Ÿπ‘’(𝑝𝑗 ) = βˆ‘ max {πœ™ (𝐼 (𝑝𝑗 , π‘’π‘–π‘˜ )) + πœ† β‹… πΆπΈπ΄π‘†π‘π‘œπ‘Ÿπ‘’π‘ π‘’π‘ (π‘’π‘–π‘˜ )}              (15)
                                         𝑖=1 π‘˜=1


2. Results
In the Accuracy Track of SemTab 2022, participants compete with each other for three rounds.
In each round, different datasets are provided to evaluate their systems on CTA, CEA, and CPA
tasks.
   Table.1 shows the results of KGCODE-Tab in all datasets of SemTab 2022. Since our system
evolved as the competition went on, its rank and performance were on the rise during the whole
competition.
                     Task                CTA                          CEA                          CPA
 Dataset                    APrecision     AF1    Rank   APrecision     AF1    Rank   APrecision     AF1    Rank
          Round1
 HardTablesR1(WD)             0.944       0.942    4       0.916       0.893    4       0.918       0.906    5
          Round2
 HardTablesR2(WD)             0.971       0.968    1       0.875       0.856    2       0.943       0.916    3
 ToughTables(WD)              0.546       0.543    1       0.913       0.905    3         /           /      /
 ToughTables(DBP)             0.485       0.480    1       0.830       0.827    1         /           /      /
          Round3
 BiodivTab(DBP)               0.867       0.867    1       0.911       0.911    1         /           /      /
 GitTables(DBP)               0.608       0.587    2         /           /      /         /           /      /
 GitTables(SCH)(class)        0.716       0.693    1         /           /      /         /           /      /
 GitTables(SCH)(property)     0.665       0.618    2         /           /      /         /           /      /

Table 1
Results of KGCODE-Tab obtained in SemTab 2022.

2.1. Round 1
In Round 1, tables of HardTables datasets have small numbers of rows and columns, and the
subject columns of most tables are the first columns. Thus KGCODE-Tab processes tables
in batches and sets the first columns as subject columns by default. Experiments show that
processing in batches dramatically improves the efficiency of spell correction and entity recall,
fully utilizing the multithreading technology. Fixing subject columns also reduce the error
caused by the table structure analysis module.

2.2. Round 2
In Round 2, the subject columns of tables in ToughTables datasets are not always the first
columns, and non-subject columns are not necessary to be the properties of subject columns but
can be their descriptions. Hence, the table structure analysis module comes into play, and the
descriptions of entities participate in the calculation of similarity scores. Results show that these
modifications largely increase the accuracy of the entity disambiguation module, improving the
ranking of our system.
   In addition, the number of rows in each table in ToughTables datasets fluctuates greatly,
and some tables have extremely large numbers of rows. Hence, adaptive batch processing is
introduced according to the size of the tabular data, and for the table with a large number
of rows, only part of the representative rows are randomly selected for CTA task annotation,
improving the efficiency of tabular data in spell correction and entity recall.

2.3. Round 3
In Round 3, tables in the BiodivTab datasets are about biodiversity, so KGCODE-Tab constructs
a biodiversity corpus for abbreviations and aliases commonly used in the field of biodiversity.
Furthermore, many cells contain noise like adverbs and adjectives, and most headers have
semantic information. Therefore, tokenization is introduced to reduce the effect of noise, and
KGCODE-Tab converts CTA task into CEA task for headers.
   For Gittables datasets, by observing the annotation results of its training dataset, we find
that the number of its labels is small and the type of annotation is relatively general, so we
consider using a text classification algorithm to solve the problem. After preliminary analysis
and research, we select the FastText [12] model. Firstly, original words are divided into several
tokens, and the CTA results are used as labels. Then the spaCy is used for word recognition,
and the results are used as keywords. They are put into the FastText model for training. After
training, it is used to annotate the test dataset.


3. General comments
In SemTab 2022, our KGCODE-Tab team participating in SemTab for the first time has a good
result. Among all the participating teams, we achieve first-place results in multiple tasks.
   KGCODE-Tab has some strategies to improve performance with less query time. The task
analysis of the top layer can directly call the interface of the bottom layer, which increases the
maintainability of the system. The tabular data preprocessing module makes full use of several
tools like search engines, KGs API, and spaCy library to generate structured JSON files for each
tabular data to increase reusability. To achieve the semantic annotation of tabular data, three
tasks of CEA, CTA, and CPA are closely combined to deal with. As a whole, KGCODE-Tab fully
utilizes the context of the whole table and the information provided by KGs to achieve a high
accuracy.
   However, the entity disambiguation module can continue to be optimized, and machine
learning algorithms can be used to train parameters.


4. Conclusion
In this paper, we propose a novel table annotation system, KGCODE-Tab, which can deal
with three TDKGM tasks: CTA, CEA, and CPA. We propose several effective tabular data
preprocessing techniques, which consist of table structure analysis, spell correction, and entity
recall. KGCODE-Tab emphasizes entity disambiguation with table context, which reduces much
noise and remains candidate entities with high confidence. For each task, we design a scoring
formula to select the right answer among candidate entities, which utilizes the results from
other tasks. Results of SemTab 2022 show that KGCODE-Tab has excellent disambiguation
ability and achieves outstanding performance.

Supplemental Material Statement:          Source code and constructed datasets will be released
on GitHub soon.


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