=Paper=
{{Paper
|id=Vol-2553/paper3
|storemode=property
|title=MantisTable: an Automatic Approach for the Semantic Table Interpretation
|pdfUrl=https://ceur-ws.org/Vol-2553/paper3.pdf
|volume=Vol-2553
|authors=Marco Cremaschi,Roberto Avogadro,David Chieregato
|dblpUrl=https://dblp.org/rec/conf/semweb/CremaschiAC19
}}
==MantisTable: an Automatic Approach for the Semantic Table Interpretation==
https://ceur-ws.org/Vol-2553/paper3.pdf
MantisTable: an Automatic Approach for the
Semantic Table Interpretation
Marco Cremaschi, Roberto Avogadro, and David Chieregato
University of Milan - Bicocca, Viale Sarca 336, 20126 Milan, Italy
marco.cremaschi@unimib.it
{r.avogadro,d.chieregato}@campus.unimib.it
1 Presentation of the system
1.1 State, purpose, general statement
On the Web we can find a vast amount of structured data, represented in ta-
bles that contain relevant information. Despite the huge corpus of such tables
on different topics, they set limitations on artificial intelligence tasks, such as
semantic search and query answering. This is the reason why some approaches
started to propose extraction, annotation and transformation of tabular data
into machine-readable formats. In particular, in the last years, there has been a
ton of works on the annotation of tabular data, also known as Semantic Table
Interpretation (STI), which can be mainly classified as supervised (they exploit
already annotated tables for training) [5,10,7,2] or unsupervised (they do not
require training data) [13,8,1,6]; and as automatic [5,7,4] and semi-automatic
[2]. Moreover, some approaches [3,13,8,1,6,12,11] focus mainly on the analysis
of Web tables’ context such as Web page title, table caption, or surrounding
text, while others [5,10,7,4,9] address independent tables which can only rely
on their own data. We identify some limits of the state-of-the-art approaches as
follows: i) they adopt lexical comparisons for matching which ignore the contex-
tual semantics; ii) they rely on metadata like column names and sometimes even
external information like table descriptions, both of which are often unavailable
in real world applications; iii) they use personalised Knowledge Graph (KG);
iv) they perform only a few steps of STI. To overcome such limitations, we pro-
pose a comprehensive approach and a tool named MantisTable 1 , which provides
an unsupervised method to annotate independent tables, possibly without a
header row or other external information. MantisTable takes a well-formed and
normalised relational table (i.e. a table with headers and simple values, thus
excluding nested and figure-like tables), and a KG which describes real-world
entities in the domain of interest (i.e. a set of concepts, datatypes, predicates,
entities, and the relations among them) in input, and returns a semantically an-
notated table in output. This process comprises different steps to semantically
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
1
mantistable.disco.unimib.it
Cremaschi M. et al.
annotate tables, such as semantic classification of columns, which classifies a col-
umn either as a literal or as a named entity. Besides the approach, we propose
MantisTable tool, a web interface and an open source Semantic Table Interpre-
tation tool that automatically annotates, manages and makes the semantic of
tables accessible to humans and machines. This tool is independent of any partic-
ular context. Additional built-in guidance functionalities help to avoid common
pitfalls and to create correct annotations. Although a STI contains several steps,
as will be explained in the next section, the key feature of our approach is the
involvement of all the STI steps that run fully automatically. This approach and
tool were developed by one PhD student and two master’s students.
1.2 Specific techniques used
The MantisTable approach implements STI steps through five phases:
0. Data Preparation, which aims to prepare the data inside the table;
1. Column Analysis, whose tasks are the semantic classification, that assigns
types to columns (NE-column or L-column), and the detection of the subject
column (S-column);
2. Entity Linking, which deals with mappings between cells and entities in a
KG;
3. Predicate Annotation, whose task is to find relations, in the form of
predicates, between the main column and the other columns to set the overall
meaning of the table;
4. Concept and Datatype Annotation, which deals with mappings between
columns and semantic elements (concepts or datatypes) in a KG.
To describe each phase of the STI approach we consider Table 12 , which lists
video games with additional information, such as publisher, release date, etc.
Table 1. Table with a list of video games extracted from Round 1.
Title Publisher EU Release Date AU Release Date PEGI ACB
Donkey Kong Country Nintendo 2006-12-08 2006-12-07 7 G
Super Castlevania IV Konami 2006-12-29 2006-12-29 3 PG
DoReMi Fantasy:
Milon’s DokiDoki Adventure Hudson Soft 2008-09-05 2008-09-05 3 G
(900 Wii Points)
...
Data Preparation aims to clean and uniform data inside the table. Trans-
formations applied to tables are as follows: deletion of HTML tags and some
characters (i.e. ” ‘), transformation of text into lowercase, deletion of text in
brackets, resolution of acronyms and abbreviations, and normalisation of units
of measurement. To decipher acronyms and abbreviations, the Oxford English
Dictionary3 is used. The normalisation of units of measurement is performed by
applying regular expressions, as described in [8]. MantisTable extends the orig-
inal set of regular expressions to cover a complete set of units, which includes
2
Round 1 table index: 11833461 1 3811022039809817402
3
public.oed.com/how-to-use-the-oed/abbreviations/
MantisTable: an Automatic Approach for the STI
area, currency, density, electric current, energy, flow rate, force, frequency, fuel
efficiency, information unit, length, linear mass density, mass, numbers, popu-
lation density, power, pressure, speed, temperature, time, torque, voltage and
volume.
Column Analysis whose tasks are the semantic classification that assigns
types to columns that are named entity (NE-column) or literal column (L-
column), and the detection of the subject column (S-column). The first step of the
Column Analysis phase is to identify good L-column candidates. To accomplish
this task, we consider 16 regular expressions that identify several Regextypes
(e.g. numbers, geo coordinate, address, hex color code, URL). If the number
of occurrences of the most frequent Regextype in a column exceeds a given
threshold, that column is annotated as L-column, otherwise, it is annotated as
NE-column. The second step deals with the subject column detection that takes
into account the identified NE-columns. We can define the S-column as the main
column of the table based on different statistic features, like Average Number of
Words (aw) in each cell, Fraction of Empty Cells (emc) in the column, Fraction
of Cells with Unique content (uc) and Distance from the First NE-column (df).
These features are combined to compute the subcol(cj ) score for each NE-column
as follows: 2ucnorm (cj ) + awnorm (cj ) − emcnorm (cj )
subcol(cj ) = p (1)
df (cj ) + 1
The column with the highest score will be selected as the S-column for the
considered table. The values of the features for the S-column detection related
to the video games table (Table 1) are shown in Table 2. In this case the Title
column is the S-column of the table (Table 3).
Table 2. Values of the features of the S-column detection for the video games table.
Feature Title column Publisher column
emc 0 0
uc 1 0.21
df 1 2
aw 1 0.37
final 3 0.57
Table 3. Table 1 after the Column Analysis phase.
S NE L L L NA
Title Publisher EU Release Date AU Release Date PEGI ACB
donkey kong country nintendo 2006-12-08 2006-12-07 7 g
...
Entity Linking deals with mappings between the content of cells and en-
tities in the KG. The original version of MantisTable used a naive approach:
we employed a SPARQL query considering only the entities for which, within
the values of rdf:type, it was possible to find the label of the winning class. If
more than one entity was returned for a cell, the one with a smaller edit dis-
tance (i.e. Wagner-Fischer distance) was taken. Between Round 3 and Round 4
we developed a more effective approach, which is described below. In the new
approach we consider the table entities row by row. To discover the mappings
for each cell in a row we take a set of eligible entities from the KG. We define
Cremaschi M. et al.
as eligible all the entities which label contains the cell’s content or contains the
cell’s tokens as shown in Listing 1.1. By converting this information to a graph
representation we find all the paths that, starting from a eligible entity of the
cell’s subject, enter into a eligible entity of the cell’s object of the row. To in-
crease the accuracy we also try to match the literal cells with the literals in the
subject cell’s candidate entities: that is, we seek for a match between literals in
the table and eligible entities by using a simple matching algorithm. This algo-
rithm distinguishes between three main datatypes: dates, numbers and strings.
While dates and strings are matched exactly, numeric matching is done using
an approximated matching algorithm: a pair of numbers makes a match if the
distance (the absolute difference) between the two is less than a threshold. To
pick the winning entities we apply to each path a score defined in the following
way:
||pi ||
X
P Sr (pi ) = ||pi || + (1 − editDistance(tx(j, r), ej,r )) (2)
j
where P Sr is the path score of row r, pi is an eligible path found, ||pi || is
the length of the path defined as the number of vertices contained in the path
and editDistance(tx(j, r), ej,r ) is the edit distance (Levenshtein) between cell
content and eligible entity. Then eventually we take the path with the maximum
score.
Listing 1.1. SPARQL query to retrieve a set of eligible entities for a cell’s content.
1 CONSTRUCT {
?s ?p ?o.
3 } WHERE {
{
5 {
?s ?p ?o.
7 ? s rdfs : label ? label .
? label < bif : contains > ’( < cell value >) ’.
9 } UNION {
?s ?p ?o.
11 ? s rdfs : label ? label .
? label < bif : contains > ’( < token > AND < token > [ AND < token >]) ’.
13 } UNION {
OPTIONAL {
15 ? altName rdfs : label ? lab .
? lab < bif : contains > ’( < token > AND < token > [ AND < token >]) ’.
17 ? altName dbo : w i k i P a g e R e d i r e c t s ? s .
?s ?p ?o.
19 }.
}
21 } UNION {
?o ?p ?s.
23 ? s rdfs : label ? label .
? label < bif : contains > ’( < token > AND < token > [ AND < token >]) ’.
25 }
[...]
27 }
Predicate Annotation, whose task is to find relations in the form of pred-
icates, between the Subject column and the other columns, to set the overall
meaning of the table. MantisTable approach considers the set of predicates found
in the Entity Linking phase and takes the predicate with maximum frequency.
MantisTable: an Automatic Approach for the STI
Concept and Datatype Annotation deals with mappings between
columns headers and semantic elements (concepts or datatypes) in a KG. Our
approach reuse the information extracted by the linking phase (linked entities).
For each winning entity (Eij ) we pick the associated concepts (rdf:type),
then for each column we build a dictionary Cj where each rdf:type have asso-
ciated the number of rows of the table containing that concept divided by the
total number of entities found for that column. We use a threshold set at 40%
of the maximum score, the concepts with a score lower then this threshold are
discarded. Table 4 show an example of this scoring.
Table 4. Example for Cj
Concept Score
Person 0.7
Politician 0.5
Athlete 0.3
GolfPlayer 0.3
Place 0.3
PopulatedPlace 0.3
Settlement 0.3
City 0.3
With the concept list obtained in this way, we build a concept graph repre-
senting the hierarchy between them as shown in Figure 1. To identify the winning
connected component within this graph, we take the maximum result of the fol-
lowing formula (connected component score) applied to each component:
X
CCScore(CCi ) = conceptScore(n) (3)
n∈CCi
Fig. 1. Example concepts graph’s paths
Cremaschi M. et al.
where CCi is the ith connected component and conceptScore is a function
that returns the corresponding concept score.
After this step, in order to complete the column annotation task we pick the
path of the winning connected component which maximizes the score. We define
the path’s score in the following way, where p is a path of concepts defined as
c1 , c2 , · · · , cn .
n
X
pathScore(p) = conceptScore(pi ) (4)
i
This method is being used to reinforce the identification of winning concepts
in spite of the presence of noisy data or contingent errors in the knowledge base.
Without loss of generality let’s consider the example in Table 4 and Figure
1. To calculate the score for the Person and Place connected components we
calculate CCScore as in Equation 3.
CCScore(P ersonCC) = conceptScore(P erson) + conceptScore(Athlete)+
conceptScore(P olitician) + conceptScore(Golf P layer)
CCScore(P laceCC) = conceptScore(P lace) + conceptScore(P opulatedP lace)+
conceptScore(Settlement) + conceptScore(City)
therefore
CCScore(P ersonCC) = 0.7 + 0.3 + 0.3 + 0.5 = 1.8
CCScore(P laceCC) = 0.3 + 0.3 + 0.3 + 0.3 = 1.2
so the winning connected component will be PersonCC.
Now let’s see how path’s score on PersonCC is computed. Let’s pick some
paths on this connected component defined as p1 = P erson, P olitician and
p2 = P erson, · · · , Golf P layer, then the score is computed in the following way:
score(p1 ) = conceptScore(P erson) + conceptScore(P olitician)
score(p2 ) = conceptScore(P erson) + conceptScore(Athlete)+
conceptScore(Golf P layer)
therefore
score(p1 ) = 0.7 + 0.5 = 1.2
score(p2 ) = 0.7 + 0.3 + 0.3 = 1.3
so our method choose the p2 path as the winning concept annotation.
MantisTable: an Automatic Approach for the STI
2 Link to the system
As described above, the MantisTable approach has been integrated into a web
application developed with Python and the Django. A MongoDB database acts
as table and KG repository. The code is freely available through a Git reposi-
tory4 . In order to achieve the scalability of the application, and therefore improve
efficiency, MantisTable has been installed in a Docker container to achieve par-
allelisms at the application level and to facilitate the deployment on servers. The
management of resources is performed by using Task Queues (i.e. Celery Work-
ers5 ). The five phases of the STI have been modularly implemented, allowing an
easy replacement or extension by other developers.
2.1 Adaptations made for the evaluation
To participate in the challenge we made some changes as follows: i) MantisTable
was originally developed to support the JSON format for loading tables and
for exporting results. In order to take part in the challenge, we developed a
new parser for managing the CSV tables. During this phase, we encountered
several problems in the management of different characters encoding; ii) since
target columns (columns to be annotated) were provided during the challenge,
we disabled most of the Column Analysis phase (i.e. subject column detection);
iii) our solution was made to identify just one correct Class per column, so we
made a new export script with different criteria for the selection of concepts,
also considering the hierarchy of these in the KG.
4
bitbucket.org/disco unimib/mantistable-tool.py
5
docs.celeryproject.org/en/latest/userguide/workers.html
Cremaschi M. et al.
3 Results
In this section, the MantisTable approach’s results in Round 1, Round 2, Round
3 and Round 4 of the challenge will be discussed. F1 and F2 in the following
tables are referred to F1-Score and Precision for CEA and CPA Tasks in every
Round and CTA in Round1 while they are referred to AH-Score and AP-score
for CTA task in the Rounds from 2 to 4.
In the first round MantisTable achieved the results in Table 5. The results are
good, in particular in the CEA task. In the second round MantisTable achieved
the results in Table 6.
Table 5. Results of Round 1. Table 6. Results of Round 2.
TASK F1 F2 TASK F1 F2
CTA 0.929 0.929 CTA 1.049 0.247
CEA 1.0 1.0 CEA 0.614 0.673
CPA 0.965 0.991 CPA 0.460 0.544
Table 7. Results of Round 3. Table 8. Results of Round 4.
TASK F1 F2 TASK F1 F2
CTA 1.648 0.269 CTA 1.682 0.322
CEA 0.633 0.679 CEA 0.973 0.983
CPA 0.518 0.595 CPA 0.787 0.841
In Round 2 we particularly focused on the CTA task because it was crucial
to our initial algorithm for the other steps; we used the results of the Concept
Annotation to filter the results in the CEA and CPA tasks. About the CEA task,
it is possible that during the parsing and the cleaning of data some rows were
misplaced in different indexes. We didn’t have much time to look into it but we
are sure that we could have done a better job in this task using other resources
for disambiguation (e.g. DBpedia or Wikidata). Most of the considerations we
did in Round 2 are no longer valid with the new approach.
In the third round MantisTable achieved the results in Table 7. The results
are better than the previous round both in the precision and in the F1-Score
but the CEA and CPA tasks still have low precision due to too many false-
positives: regarding the CEA task our approach did not have enough contextual
information to discriminate between eligible entities, while for the CPA task our
approach suffered for the entity linking.
In the fourth round MantisTable achieved the results in Table 8. To overcome
the lack of contextual information needed by the linking phase, we changed our
approach as described in the previous sections gaining a considerable precision
boost for both the CEA and CPA tasks compared to previous rounds.
MantisTable: an Automatic Approach for the STI
The results of CEA and CPA tasks with the new method are shown in tables
9 and 10.
Table 9. Results of Round 2 (recom- Table 10. Results of Round 3 (recom-
puted). puted).
TASK F1 F2 TASK F1 F2
CEA 0.799 0.897 CEA 0.94 0.977
CPA 0.663 0.702 CPA 0.723 0.759
4 Conclusions and General comments
Unlike the state of the art approaches, MantisTable i) provides a comprehensive
solution to support all annotations steps; ii) provides an unsupervised method
to annotate independent tables; iii) generates context for disambiguation; iv)
provides a tool to support STI workflow and a tool to support the evaluation
by providing validation indicators which are both publicly available. In relation
to the results obtained in Round 4, we are going to change the workflow of our
tool to better implement the methods for the CEA and CPA tasks. We are also
going to develop a way to process tables via remote API calls to allow easier
third party integration.
Cremaschi M. et al.
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