=Paper=
{{Paper
|id=Vol-2984/paper4
|storemode=property
|title=From web-tables to a knowledge graph: prospects of an end-to-end solution
|pdfUrl=https://ceur-ws.org/Vol-2984/paper4.pdf
|volume=Vol-2984
|authors=Alexey O. Shigarov,Nikita O. Dorodnykh,Aleksandr Yu. Yurin,Andrey A. Mikhailov,Viacheslav V. Paramonov
|dblpUrl=https://dblp.org/rec/conf/itams/ShigarovDYMP21
}}
==From web-tables to a knowledge graph: prospects of an end-to-end solution==
https://ceur-ws.org/Vol-2984/paper4.pdf
From web-tables to a knowledge graph:
prospects of an end-to-end solution
Alexey Shigarov1 , Nikita Dorodnykh1 , Alexander Yurin1 , Andrey Mikhailov1 and
Viacheslav Paramonov1
1
Matrosov Institute for System Dynamics and Control Theory, Siberian Branch of the Russian Academy of Sciences,
134 Lermontov St, Irkutsk, 664033, Russian Federation
Abstract
The Web stores a large volume of web-tables with semi-structured data. The Semantic Web community
considers them as a valuable source for the knowledge graph population. Interrelated named entities can
be extracted from web-tables and mapped to a knowledge graph. It generally requires reconstructing
the semantics missing in web-tables to interpret them according to their meaning. This paper discusses
prospects of an end-to-end solution for the knowledge graph population by entities extracted from
web-tables of predefined types. The discussion covers theoretical foundations both for transforming
data from web-tables to entity sets (table analysis) and for mapping entities, attributes, and relations to a
knowledge graph (semantic table annotation). Unlike general-purpose text mining and web-scraping
tools, we aim at developing a solution that takes into account the relational nature of the information
represented in web-tables. In contrast to the table-specific proposals, our approach implies both the table
analysis and the semantic table annotation.
Keywords
table understanding, semantic table interpretation, web-tables, data extraction, knowledge graph popula-
tion, semantic web
1. Introduction
The Web stores a large volume of tables. The exploration of the Web crawl discovered hundreds
of millions of web-tables containing relational data [1, 2]. There are at least billiards of valuable
facts that can be extracted from web-tables. All of these make web-tables an attractive data
source in various applications, such as knowledge base construction [3, 4], question-answering
systems, and table augmentation [5, 6, 7]. However, in general, web-tables are not interpretable
by computer programs. Their original representation does not provide all explicit semantics
required to interpret them according to their meaning. This hinders the wide usage of such
tabular data in practice.
Reconstruction of the semantics missing in tables is commonly referred to as the “table
understanding”. This problem was first formulated by M. Hurst in 2000 [8, 9]. Over the two last
ITAMS 2021: Information Technologies: Algorithms, Models, Systems, September 14, 2021, Irkutsk, Russia
" shigarov@icc.ru (A. Shigarov); tualatin32@mail.ru (N. Dorodnykh); iskander@icc.ru (A. Yurin);
mikhailov@icc.ru (A. Mikhailov); slv@icc.ru (V. Paramonov)
~ http://td.icc.ru (A. Shigarov)
0000-0001-5572-5349 (A. Shigarov); 0000-0001-7794-4462 (N. Dorodnykh); 0000-0001-9089-5730 (A. Yurin);
0000-0003-4057-4511 (A. Mikhailov); 0000-0002-4662-3612 (V. Paramonov)
© 2021 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)
Data extraction from web-tables
Web-table Web-table classification by Extracting tuples from
(HTML or CSV) predefined types web-table Entity sets
(relations in 3NF)
ANN-models (like DeepTable) Predefined rules
Semantic table interprertation
Matching attributes to KG-classes and KG-datatypes Matching attribute pairs to
entity mentions with KG-instances KG-properties
Entity lookup, Entity embedding, and Entity relatedness metrics
Semantic similarity metrics
RDF-triples Knowledge graph population (by extracted entities)
Knowledge
graph
Figure 1: Steps for the knowledge graph population with entities extracted from web-tables.
decades, hundreds of papers devoted to its issues were published [6, 7, 10, 11]. The literature
survey shows that this topic continues to rapidly develop in several communities such as
document understanding, semantic web, and end-user programming. The last 3 years were
marked by an extraordinary growth of proposals based on a novel apparatus, namely, deep
learning, word and entity embedding, and knowledge graphs. The two highly-rated conferences,
“Int. Semantic Web Conf.” and “Int. Conf. Document Analysis and Recognition”, recently conducted
competitions related to this problem.
The complexity of the problem is determined by two factors: (i) a wide variety of tricks to
present table layout, formatting, and content; (ii) limited representation formats (such as Excel or
HTML) that do not provide all semantics needed for data interpretation. Generally, the solution
requires all stages of the table understanding: (i) table detection or discrimination; (ii) table
structure recognition and cleaning; (iii) role and structural analysis (i.e. extracting interrelated
data and metadata values from the content); (iv) semantic interpretation (i.e. matching the
semantic table structure with an external dictionary).
There several are challenges for the expert community. One of them is to develop of a common
theoretical and technological basis applicable to various digital environments and formats for
representing tabular data in the Web (such as print-oriented documents, spreadsheets, and
web-pages). Our approach is addressed to this challenge, namely the extraction and semantic
interpretation of data from web-tables represented in HTML-format (Fig. 1).
The recent surveys of the thematic literature [6, 7, 10, 11] note that the problem of table
understanding remains open. The review [6] revealed that the majority of the works focuses
mainly on the tasks of discrimination and semantic interpretation of web-tables. Roldán et
al. [7] indicated that none of the known solutions is complete. They do not provide all steps of
the table understanding. This is also confirmed by Burdick et al. [10]. As reported in [7], many
table design properties are not taken into account by the state-of-the-art solutions. This often
hinders their practical application.
The existing models of table representation do not completely reflect the complexity of the
structure of real tables. One of the commonly-used assumptions is “all cell values are atomic”.
They assume that any non-blank cell contains only one functional data item. To the best of our
knowledge, all competitive solutions follow this simplification. However, a real cell can have
several data items with the same or different functions. The latter should be taken to account in
order to extend the range of cases for table processing.
The novelty of our proposal is established by the following. First, we propose an end-to-end
solution covering the stages from extracting data from syntactically tagged tables to their
semantic interpretation (i.e. mapping extracted data and metadata to a cross-domain knowledge
graph). Second, we take into account structured cells which content should be decomposed
into several atomic data items with different functional roles. Third, our proposal can show the
applicability of some promising techniques (cell embeddings, contextualized word embeddings,
entity embedding) to the tasks of table understanding.
Unlike the general-purpose text mining and web-scraping tools, our solution takes into
account the relational nature of the information represented in web-tables. In contrast to
similar proposals that target data extraction from web-tables, we cover a wider range of cases by
involving the structured content of a cell. Moreover, the competitive techniques are limited either
by data extraction stage or the stage of semantic table interpretation, whereas, our approach
implies both of them. Therefore, the expected results could be applied in the knowledge base
population.
2. Data extraction from web-tables
The approach to the data extraction from web-tables includes two stages: (i) classifying web-
tables by predefined types; (ii) extracting entity sets from web-tables, using algorithms appro-
priated to the corresponding types.
2.1. Web-table classification
In the last decade, several taxonomies of web-table types were published in the last decade
[12, 13, 14]. All taxonomies describe three common types of web-tables with relational data
(Fig. 2). Eberius et al. refer to them as “vertical listing”, “horizontal listing”, and “matrix”. This
taxonomy is used by the latest proposals for the table type classification [15, 16, 17, 18]. We
also rely on this taxonomy. This will allow us to quantitatively compare our results with others.
We plan to develop a deep neural network model for classifying web-tables based on DeepT-
able1 [17], the ad-hoc architecture that provides four main blocks: (i) Embedding layer for
extracting vector representation of cell tokens; (ii) LSTM (recurrent neural network) for iden-
tifying semantic dependencies between tokens in a cell; (iii) MLP (multilayer perceptron) for
1
https://github.com/marhabibi/deeptable
a b c
Figure 2: Web-tables taxonomy: vertical listing — (a), horizontal listing — (b), and matrix — (c).
identifying non-linear dependencies between all cells in a table; (iv) Softmax as a classification
layer.
An open collection of tagged tables extracted from biomedical research papers (PubMed Cen-
tral2 ) can be used as training data. To select a basic tool for contextualized vector representation
of words, we propose to try several variations (ELMo3 [19], fastText4 [20], etc.). Some classifiers
can be trained for each variation. This allows us to compare their accuracy and choose the best
for this task.
2.2. Transformation of entity sets from web-tables
We propose to develop algorithms that target three table types of [14]. The algorithms should
analyze the logical structure of web-tables by using built-in rules and trained classifiers dealing
with these types. It is important to note that web-tables mix data and metadata. Moreover,
one cell may contain several values of both data and metadata. The extraction of the logical
structure requires: (i) to associate each cell value (data item) with its functional role (data and
metadata); (ii) to associate data values with metadata ones; (iii) to group data belonging to one
record (entity).
After the table type classification, the extraction of data and metadata values becomes a
“cornerstone” step. We plan to implement a classifier based on machine learning algorithms
to assign functional roles to data items. A promising approach to encoding cell context in the
vector representation named “cell embedding” was recently proposed in [21, 22]. In our case,
the cell context can be deduced automatically by using the built-in type-specific table structure
analysis. This approach should allow cells classification taking into account the properties
of their layout and formatting, as well as the semantic similarity of their text content. We
plan to reduce the number of possible false-positive and false-negative errors by using some
table-specific constraints. To associate data with metadata and group data values with records,
we also suggest using rule-based analysis of the table structure. The extracted entity sets can be
represented in JSON notation compatible with TOMATE5 [18], a recently published framework
for the performance evaluation of web-table data extraction tools.
2
https://www.ncbi.nlm.nih.gov/pmc
3
https://allennlp.org/elmo
4
https://fasttext.cc
5
http://tomatera.tdg-seville.info
Num. of
University Location Established Tuition fee Degree powers
students
University of Oxford Oxford 1096 25,910 £9,250 Full
University of Cambridge Cambridge 1209 21,340 £9,250 Full
London School of Economics London 1895 12,050 £9,250 Full, London
Durham University Durham, Stockton 1832 19,520 £9,250 Full
a
o:EducationalInstitution o:Established o:NumOfStudents o:Tuition(£)
(entity) (property) (property) (property)
r:UniversityOfOxford 1096 25910 9250
r:UniversityOfCambridge 1209 21340 9250
r:LondonSchoolOfEconomics 1895 12050 9250
r:DurhamUniversity 1832 19520 9250
o:EducationalInstitution o:Location o:EducationalInstitution o:DegreePowers
(entity) (entity) (entity) (entity)
r:UniversityOfOxford r:Oxford r:UniversityOfOxford r:Full
r:DurhamUniversity r:Durham r:LondonSchoolOfEconomics r:Full
r:DurhamUniversity r:Stockton r:LondonSchoolOfEconomics r:London
... ... ... ...
b
o:EducationalInstitution o:Rank o:Position
(entity) (entity) (property)
University THE Guardian r:UniversityOfOxford r:THE 1
r:UniversityOfOxford r:Guardian 1
University of Oxford 1 1
r:UniversityOfCambridge r:THE 1
University of Cambridge 1 3
r:UniversityOfCambridge r:Guardian 3
London School of Economics 4 5
r:LondonSchoolOfEconomics r:THE 4
c
r:LondonSchoolOfEconomics r:Guardian 5
d
Figure 3: Example of the semantic table annotation: origin web-tables (vertical listing —a and matrix
— c) and their normalized and annotated forms (b, d); the prefix o (“ontology”) denotes a KG-class
or KG-property of entities defined in the terminological component (TBox) while r (“resource”) is a
KG-instance from the assertion component (ABox).
3. Semantic annotation of entity sets
There are 3 main approaches to the semantic table interpretation, namely: (i) ontology matching;
(ii) entity lookup and wikification; (iii) vector representations of knowledge graphs (entity
embedding). The recent study [23] showed experimentally that a hybrid approach combining
lookup services and entity embedding is one of the most efficient ways. We plan to exploit such
a hybrid using the available toolset (DBpedia Lookup6 , DBpedia SPARQL Endpoint7 , DBpedia
Spotlight8 , RDF2Vec9 , KGloVe10 , and Wikipedia2Vec11 ).
The end-to-end semantic table interpretation includes 3 stages:
• Entity linking — CEA (Cell-Entity Annotation).
• Attribute-concept matching — CTA (Column-Type Annotation).
• Relation extraction — CPA (Column-Property Annotation).
As a result, this enables knowledge graph augmentation. Fig. 3 shows two examples where web-
tables from Wikipedia1213 (Fig. 3, a, b) are normalized and enriched by the semantic annotation
(Fig. 3, c, d), i.e. links to a knowledge graph.
3.1. Entity linking — CEA
The proposed solution should provide for the following: (i) identifying a subject column con-
taining names of entities listed in a table; (ii) lookup a set of candidate 𝐾𝐺-instances for each
entity; (iii) entity disambiguation in cases when several candidate KG-instances are associated
with an entity.
The subject column is selected among potential keys that contain entity mentions. We are
limited by the trivial case when there is only one candidate subject column. (Note that the
general case requires the end-to-end semantic table interpretation).
As the main tool for linking entities, we propose to use the vector representations of subsets
from a knowledge graph. The initial lookup of candidate KG-instances can be performed by
using SPARQL-queries to the knowledge graph. Such queries are composed of surface forms
contained in the text of cells. Each KG-instance can be encoded as a vector representation of the
entity by the existing algorithms, such as RDF2Vec [24], KGloVe [25], or Wikipedia2Vec [26].
The formed vector model should allow us to use some semantic similarity metrics [27] to rank
candidate KG-instances by relevance to the entity.
The approach to the entity disambiguation relies on the assumption proposed by [28] which
implies that that the most relevant KG-instances from the candidate sets have the highest
semantic similarity values in pairwise matching. This can be explained by the following
example from [28]. Let a column contain 3 mentions: “USA”, “China”, and “India”. They should
be matched to 3 sets of candidate KG-instances respectively: “USA” → [“University of South
Alabama (University)”, “United States of America (Country)”], “China” → [“People’s Rep. of China
(Country)”, “China (Band)”, “China, Kagoshima (City)”], “India” → [“India (Country)”, “India
(George W. Bush’s cat)”, “India (Xandria album)”]. Among all pairs of KG-instances, “United
6
https://lookup.dbpedia.org
7
https://dbpedia.org/sparql
8
https://www.dbpedia-spotlight.org
9
http://rdf2vec.org
10
https://datalab.rwth-aachen.de/embedding/kglove
11
https://wikipedia2vec.github.io/wikipedia2vec
12
https://en.wikipedia.org/wiki/List_of_universities_in_England
13
https://en.wikipedia.org/wiki/Rankings_of_universities_in_the_United_Kingdom
States of America (Country)”, “People’s Rep. of China (Country)” and “India (Country)” would be
the most semantically similar (they mean a common concept in the knowledge graph).
Thus, this approach should allow us to rank candidate KG-instances and select from them
the reference KG-instances for specific mentions. For example, the table showed in Fig. 3, a
contains the surface form “London” that can mean “Location” or “Degree powers”. Obviously,
in the context of the column [Oxford, Cambridge, London, Durham, Stockton] it should be
assigned to the instance of “Location” while in the context of the column [Full, London, Taught]
it corresponds to the instance of “Degree powers”.
3.2. Attribute-concept matching — CTA
In practice, many tables are not accompanied by metadata (named attributes). Generally, to map
a column to a KG-class, first it is needed to associate the entities listed in the column with the
reference KG-instances. After that, it is possible to form an index of all candidate KG-classes to
which the reference KG-instances belong. Among them, the KG-class which is most relevant
to all column values is selected. For example, in Fig. 3, a three columns should be matched to
KG-classes (Fig. 3, b) as follows:
"University" -> o:EducationalInstitution
"Location" -> o:Location
"Degree powers" -> o:DegreePowers
While the rest of columns are corresponded to KG-properties of o:EducationalInstitution
(KG-class) as follows:
"Established" -> o:Established
"Num. of students" -> o:NumOfStudents
"Tuition fee" -> o:Tuition(£)
In the cases when entity linking (CEA-stage) fails, we propose to use ANN-models to predict
the KG-class of a column based on ColNet algorithms [29, 30]. To map a column of literal values
(NUMERIC, DATE, CURRENCY, etc.) to a KG-datatype, it is enough to recognize standard named
entities. This is reached by using regular expressions and NER-models available in popular
NLP-libraries (e.g. Stanford CoreNLP14 , AllenNLP15 ).
3.3. Relation extraction — CPA
To map pairs of columns (< 𝐸, 𝑃 >, where 𝐸 is a subject, 𝑃 is not a subject) with KG-properties,
we plan to use entity relatedness metrics [31]. It is assumed that these metrics will allow ranking
the index of candidate KG-properties and choosing the most relevant ones. For example, two
web-tables showed in Fig. 3 contain the following relationships:
<, o:positioned_at, 1>
<, o:positioned_at, 1>
14
https://stanfordnlp.github.io/corenlp
15
https://github.com/allenai/allennlp
position in Rank
team 1
University challenge
predecessor of team 2
part of
Nobel laureate
Educational institution winner speaker Person
campus Observer mace
associated with
Location
representation in
Student union
Parliament
Figure 4: An example of the terminological level (TBox) of a knowledge graph constructed from tables
of Wikipedia pages in “Category: Universities in the United Kingdom”.
3.4. Knowledge graph augmentation
An entity set represented as linked data (RDF-triples with URI-references to concepts in a
knowledge graph) should be suitable for further interpretation. In particular, other facts (RDF-
triples) can be inferred from them and asserted to the knowledge graph. Such restored semantics
would provide populating the existing knowledge graphs with new entities extracted from
web-tables. For example, Fig. 4 shows the terminological level (TBox) of a knowledge graph
constructed by using 49 tables scrapped from Wikipedia pages in “Category: Universities in the
United Kingdom”. The facts extracted from these tables can be asserted into the ABox component
of the knowledge graph.
We plan to demonstrate the applicability of the proposed solution by an illustrative example of
populating a domain-specific knowledge graph (ABox component) in the area of industrial safety
expertise. This should cover aligning entity set records with the structure of the knowledge
graph (row-to-instance matching) and synthesizing new KG-instances and KG-properties. Tables
extracted from real reports on industrial safety expertise may be used as a source of domain
data.
4. Conclusions
Our previous work [32, 33, 34] was aimed at data extraction from spreadsheets driven by
user-defined rules. We proposed end-user programming as the main approach. This allowed
us to support specific tricks of table layout, formatting, and content. However, scaling such
solutions may be challenging when there are ambiguous tricks applied within source tables.
Nonetheless, a solution intended for the Web should be easily scaled. This is possible when
there are pre-defined types of web-tables. The latter is needed to classify them and select
type-specific algorithms of analysis and interpretation. Thus, our previous approach is suitable
for spreadsheet sources, but not for the Web.
The current proposal aims to fill this gap by the development of a scalable solution for
web-tables. The expected results contribute to the following: (i) data extraction, including
algorithms for classifying web-tables by types of taxonomy and extracting entity sets from
tables of predefined types, (ii) semantic table annotation, including algorithms for mapping
entities, attributes, and relations to concepts of an external knowledge graph, (iii) open software
for implementing the functionality of the extraction and semantic annotation of tabular data in
applications of the knowledge graph population.
To the best of our knowledge, all existing proposals for data extraction from web-tables
exploit a specific constraint: “any cell contains only one atomic data item”. This constraint can
be eliminated in the proposed solution. We argue that the structured content of a cell can be
decomposed into several data items. Moreover, all proposals implement the semantic table
interpretation only for entity sets, not pivots. We plan to study both kinds of tabular data. We
think this can expand the range of cases to be processed.
We propose to apply the state-of-the-art methods and tools, including contextualized word
embeddings, vector representations of knowledge graphs, entity lookup services, as well as
metrics of semantic similarity and entity relatedness. The applicability of some of these tools
for the considered issues remains poorly studied. The expected results could demonstrate the
promise of the use of these techniques.
The expected results could be useful to intellectualize software for tabular data extraction and
integration in scientific and industrial applications. It can be of particular interest in areas with
the intensive use of tabular data (e.g., finance, government statistics, and business management)
to form linked open data.
Acknowledgments
This work was supported by the Russian Science Foundation (Grant No. 18-71-10001).
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