The document discusses ongoing work to develop a taxonomy for 2xN web tables based on their semantic content. The proposed taxonomy aims to classify tables by their "message" or core information, such as social networks, spatio-temporal data, products, resources, universal facts, and events. The work involves manually tagging over 174,000 web tables to analyze their distribution across the proposed taxonomy classes and identify the most common classes. This content-based taxonomy seeks to improve over prior approaches that classified tables based primarily on their syntactic structure.

1. Mining Semi-structured Data:
Understanding Web-tables – Building a
Taxonomy for 2xn Tables
Emir Mu˜noz, MSc.
emir.munoz@deri.org
Galway, Ireland – 19 July 2012
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2. Outline
1 Introduction
2 WTT-Detection
3 WTT-Interpretation
4 On-going work
5 Future work
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3. Introduction I
Tables
They are used as a compact and efficient way to present
relational information.
They are inherently concise as well as information rich.
The automatic understanding of tables has many applications
including:
Knowledge management
Information retrieval
Web and text mining
Summarization, and
Content delivery to mobile devices.
Interesting for domains like: medicine, health-care, finance,
e-science (e.g., biotechnology), and public policy.
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4. Introduction II
Web-tables (WTT) examples
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5. Introduction III
Table understanding in Web documents include [WH02]:
Table detection,
Functional and structural analysis, and
Table interpretation.
Cafarella in [CHW+08] estimated that there are around 14.1
billion HTML tables, out of which 154 million contain high
quality relational data.
This represents a large source of knowledge, yet we do not
have systems that can understand and exploit this knowledge
properly.
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6. Outline
1 Introduction
2 WTT-Detection
3 WTT-Interpretation
4 On-going work
5 Future work
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7. Table detection I
In practice, tables are not only used to present relational
information,
... they are also used to create multiple-column layouts to
facilitate easy viewing.
The presence of the HTML tag <table> does not ensure a
relational table, or more general, a table with content.
[WH02] A ML approach for Table Detection
Wang and Hu discriminated genuine and non-genuine tables on the
grounds of their content. They checked as to whether they contain
logical relations among the cells, or they are just used as a
mechanism for grouping content. In so doing, they used a tree
classifier.
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8. Table detection II
In genuine or relational tables there are logical relations
among the cells.
Non-genuine or non-relational tables are used as a mechanism
for grouping contents.
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9. Table detection: [WH02] I
A Machine Learning Based Approach for Table Detection on The Web
Then, they define weights derived from the traditional tf ∗ idf
measures used in IR, and define similarity based on the vector
space model.
Their initial database contains a total of 2,851 pages
harvested from Google directory and News using predefined
keywords known to have a higher chance to recall genuine
tables, from around 200 web sites.
They selected 1,393 pages out of these database (chosen
randomly). (11,477 <table> nodes.)
For training they used 9-fold cross validation.
They experimented with decision trees and SVMs for
separating genuine and non-genuine tables.
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10. Table detection: [WH02] II
A Machine Learning Based Approach for Table Detection on The Web
1,740 are genuine (15.16%) and 9,737 are non-genuine
(84.84%) tables.
The results reported are
R = 94.25%, P = 97.50%, F = 95.88%.
(The pages was obtained by querying Google using keywords
like “table”, “stock”, “weather”.)
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11. Table detection: [CP10b] I
Web-Scale Knowledge Extraction from Semi-Structured Tables
Tables called Attribute/Value
They propose a classification algorithm
for recognizing layout tables and
attribute/value tables. In their work,
they adopted the Gradient Boosted
Decision Tree classification model, with
classes ATTRIBUTE/VALUE,
LAYOUT, and OTHER (e.g., calendars,
forms, enumerations).
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12. Table detection: [CP10b] II
Web-Scale Knowledge Extraction from Semi-Structured Tables
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13. Table detection: [CP10b] III
Web-Scale Knowledge Extraction from Semi-Structured Tables
Tables list attributes but rarely contain the subject in the
table proper.
Their focus is on detection of the subject of the table. They
call this open research problem: Protagonist Detection.
Relational tables considered in their work encode facts, or
semantic triples of the form < p, s, o >.
There are three different places where the protagonist could
be found:
a) within the table (occasionally found in the table with a generic
attribute such as name or model);
b) within the document or the HTML <title> tag; and
c) anchor texts offer well defined boundaries for identifying
protagonist candidates, the document body proposes fewer
clues.
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14. Table detection: [CP10b] IV
Web-Scale Knowledge Extraction from Semi-Structured Tables
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15. Table detection: [CP10a, CP11] I
Web-scale Table Census and Classification
They extend their previous work, proposing a much
finer-grained table-type classification and report an overall
accuracy of 75.2%.
From a total of 1.2 billion documents, they extracted 8.2
billion tables (2.6 billion unique tables).
In detail, 75% of the pages contain at least one table with an
average of 9.1 tables per document.
In preliminary experiments, when trying to identify the
protagonist of A-V tables, they use an N-gram based
approach using a commercial search engine’s web link graph.
They find the correct protagonist in 90% of the cases in its
top-20 ranked candidates, and in 79% of the cases in its top-3.
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16. Table detection: [CP10a, CP11] II
Table classes
[CP10a, CP11] propose the following table type taxonomy.
(This proposal and others are only based on a syntactic
structure of tables.)
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17. Outline
1 Introduction
2 WTT-Detection
3 WTT-Interpretation
4 On-going work
5 Future work
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18. WTT-Interpretation I
Recovering Table Semantics
There are some works focused on mapping spreadsheets into
RDF, but such systems require human intervention.
[MFSJ10] proposed an approach that uses linked data to
interpret tables and associate their components with nodes in
a reference linked data collection.
To provide general purpose knowledge as well as specific facts
about significant people, places, organizations, events and
many other entities of interest.
[SFMJ10] used RDF for exporting and encoding the
information embodied in tables.
Describing techniques to automatically infer a (partial)
semantic model for information in tables using both table
headings, if available, and the values stored in table cells.
The techniques have been prototyped for a subset of linked
data that covers the core of Wikipedia.
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19. WTT-Interpretation II
Recovering Table Semantics
City Mayor State Population
Boston T. Menino MA 610,000
New York M. Bloomberg NY 8,400,000
Philadelphia M. Nutter PA 1,500,000
Baltimore S. Dixon MD 640,000
Washington A. Fenty DC 595,000
@prefix dbp: <http://dbpedia.org/resource/> .
@prefix dbpo: <http://dbpedia.org/ontology/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix cyc: <http://www.cyc.com/2004/06/04/cyc#> .
dbp:Boston dbpo:leaderName
dbp:Thomas_Menino;
cyc:partOf dbp:Massachusetts;
dbpo:populationTotal "610000"^^xsd:integer .
dbp:New_York_City ...
...
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20. WTT-Interpretation III
Recovering Table Semantics
When predicting entity classes in a column, [SFMJ10] used
DBpedia (85.71%), Yago (71.42%), Word-Net (71.42%) and
Freebase (90.47%).
Entity types and their correct prediction: Places (61.64%),
Persons (90.76%) and Organizations (66.667%).
To describe relations between columns in a table, they take all
pairs of entities in the same row (already linked to Wikipedia)
and query DBpedia for the set of relations.
http://dbpedia.org/ontology/largestCity
http://dbpedia.org/ontology/PopulatedPlace/largestCity
http://dbpedia.org/ontology/capital
http://dbpedia.org/ontology/PopulatedPlace/capital
http://dbpedia.org/property/capital
http://dbpedia.org/property/largestcity
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21. WTT-Interpretation IV
Recovering Table Semantics
The relation that appears the maximum number of times is
the selected.
The evaluation test set is very small, just 5 tables taken from
Google Squared.
Another example about basketball players:
Name Team Position
Michael Jordan Chicago Shooting guard
Allen Iverson Philadelphia Point guard
Yao Ming Houston Center
Tim Duncan San Antonio Power forward
It is important to discover relations between the table
columns, but not only 2-ary relations.
[MFJ11] also analyzed the Government Linked data.
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22. WTT-Interpretation I
WTT as a very large repository of facts
[YTT01] focused their work on a probabilistic method to
integrate tables according the category of objects represented
in each table. (Performing an attribute clusterization.)
[YT01, TI06] proposed methods to ontology extraction from
web-tables using the relations represented by structures into
the table. (The table structures have to be given by humans.)
An IR approach presented in [YTL11], extracts structured
data from WTT, aggregates and cleans such data and stores
them in a database. They create a very large repository of
entity-attribute-value triples.
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23. WTT-Interpretation II
WTT as a very large repository of facts
(How this works?) A good example is the query “Saint
Patrick’s day”, any search engine could directly show “17
March” within their top-ranked results.
http://en.wikipedia.org/wiki/Public_holidays_in_the_Republic_of_Ireland
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24. WTT-Interpretation III
WTT as a very large repository of facts
(Hypothesis) Recovering semantics guide to a better search
and quality filter. Some enunciated problems:
Take for instance, a table about trees and a piece of text like
“...North America species such as Green Ash...”. From the
WTT we could infer that “Green Ash” is a species of tree
a.k.a. “Fraxinus pennsylvanica”.
Use schema statistics to automatically compute attribute
synonyms (more complete than thesaurus).
e.g., e-mail—email, phone—telephone, e-mail address—email
address, date—last-modified
It is still necessary to recover large fractions of binary
relationships and techniques for recovering numerical
relationships (e.g. population, GDP) [VHM+11].
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25. Outline
1 Introduction
2 WTT-Detection
3 WTT-Interpretation
4 On-going work
5 Future work
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26. On-going work I
Introduction
Our initial aim was to understand relational tables.
We parse HTML pages to extract HTML tables using
NekoHTML library.
We have a corpus comprising 8.2 billion tables.
A table is parsed as a matrix using Tartar [PCS+07]. Dealing
with the cell spans.
We manually annotated 14,695 randomly chosen tables:
10,923 content-poor (74.33%) and 3,785 content-rich
(25.76%) tables.
We made a content-poor and content-rich table predictor
using the same features as [CP11] using a max-entropy model,
and 10-fold cross-validation.
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27. On-going work II
Introduction
From a set of 115 features, we selected 19 via a greedy
property selection algorithm. The 19 achieved an accuracy of
89.46%.
Most important features:
Presence of the <select> tag in a column
Distinct strings in the 1st column
Distinct tags in a column
Distinct tags in a row
Non-empty cells in columns or rows
Presence of links
Presence of colon “:”
Presence of break line <br>
Presence of input fields (HTML)
Presence of numbers in a rows
Presence of the <th> tag
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28. On-going work III
Introduction
Our aim is proposing a based-in-content taxonomy for WTT
instead of previously based in syntax structure.
We are now developing a 2xn table predictor with classes
focused in content.
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29. On-going work I
Why this work?
Why a new taxonomy?
[WH02] classify WTT as genuine or non-genuine tables.
[CHW+
08] classify WTT as relational or no-relational tables.
Crestan and Pantel’s taxonomy is a more general purpose
taxonomy for tables, focused on the syntax of the tables, not
in their semantic.
Intuitively, all the classes of the taxonomy of [CP11] are not
useful, and only A-V class is used.
Moreover, What it means to say that a table is A-V?
A A-V table could have spatio-temporal attributes or universal
facts or, even describe a person or a company or a product.
All the previous approaches needs a little bit of focus given for
the “message” of the tables.
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30. On-going work II
Why this work?
Why 2xn tables are important?
2xn class is larger than A-V class.
They are about 20% of all tables in the Web.
Previous A-V tables were identified by the presence of “:”
(colon).
We hope that extending the research to 2xn tables, discover
those A-V tables that are not indicated by the “colon rule”.
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31. On-going work
Proposed Taxonomy
We introduce new classes, that could be important, e.g., to be
used with ontologies (e.g., FOAF) in a search engine.
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32. On-going work
WTT examples according to our taxonomy
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33. On-going work
Distribution
We are manually tagging 174,748 unique WTT.
The distribution per class until now is:
Class %
Social networks 34.2%
Spatio-temporal information 28.9%
Products 28.4%
Resources 4.3%
Universal facts 3.2%
Other 1.0%
Events 0.03%
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34. Outline
1 Introduction
2 WTT-Detection
3 WTT-Interpretation
4 On-going work
5 Future work
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35. Future work – Open problems I
Tables in web-pages can be used to model the data of
web-sites, in particular, its main entities and the key relations
thereof. This entails:
a) Web tables bear syntactic and semantic information that it is
useful for determining what they are talking about. Thus,
patterns across web-tables can be exploited to automatically
understand their “message”.
b) Once the ”message” of the tables of a specific web-site is
determined, it is possible to infer the main entities that this
web-site talks about.
c) Once the relevant entities of a web-site are detected, it is
plausible to recognize prominent relationships between these
entities. Thus, we will be able to link data between the chief
entities of a web-site.
d) Once predominant relations and entities of a web-site are
determined, it is possible to link data between different
web-sites.
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36. Future work – Open problems II
Extracting RDF from wikipedia tables (not only infobox).
Relation extraction – all kind of relations.
Taxonomies definition.
Other levels, like: rankings, definitions.
Complex table understanding.
Table integration.
Protagonist detection for web-tables.
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37. References I
If you want to go further
Michael J. Cafarella, Alon Y. Halevy, Daisy Zhe Wang, Eugene Wu, and Yang Zhang.
Webtables: exploring the power of tables on the web.
PVLDB, 1(1):538–549, 2008.
Eric Crestan and Patrick Pantel.
A fine-grained taxonomy of tables on the web.
In Jimmy Huang, Nick Koudas, Gareth J. F. Jones, Xindong Wu, Kevyn Collins-Thompson, and Aijun An,
editors, CIKM, pages 1405–1408. ACM, 2010.
Eric Crestan and Patrick Pantel.
Web-scale knowledge extraction from semi-structured tables.
In Proceedings of the 19th international conference on World wide web, WWW ’10, pages 1081–1082, New
York, NY, USA, 2010. ACM.
Eric Crestan and Patrick Pantel.
Web-scale table census and classification.
In Irwin King, Wolfgang Nejdl, and Hang Li, editors, WSDM, pages 545–554. ACM, 2011.
Varish Mulwad, Tim Finin, and Anupam Joshi.
Automatically Generating Government Linked Data from Tables.
In Working notes of AAAI Fall Symposium on Open Government Knowledge: AI Opportunities and
Challenges. November 2011.
Varish Mulwad, Tim Finin, Zareen Syed, and Anupam Joshi.
Using linked data to interpret tables.
In Proceedings of the the First International Workshop on Consuming Linked Data, November 2010.
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38. References II
If you want to go further
Aleksander Pivk, Philipp Cimiano, York Sure, Matjaz Gams, Vladislav Rajkovic, and Rudi Studer.
Transforming arbitrary tables into logical form with TARTAR.
Data Knowl. Eng., 60(3):567–595, 2007.
Zareen Syed, Tim Finin, Varish Mulwad, and Anupam Joshi.
Exploiting a Web of Semantic Data for Interpreting Tables.
In in Proceedings of the WebSci10: Extending the Frontiers of Society On-Line, Raleigh NC, USA, April
26–27th 2010.
Masahiro Tanaka and Toru Ishida.
Ontology Extraction from Tables on the Web.
In in Proceedings of the International Symposium on Applications on Internet, pages 284–290, Washington
DC, USA, 2006.
Petros Venetis, Alon Y. Halevy, Jayant Madhavan, Marius Pasca, Warren Shen, Fei Wu, Gengxin Miao, and
Chung Wu.
Recovering semantics of tables on the web.
PVLDB, 4(9):528–538, 2011.
Yalin Wang and Jianying Hu.
A Machine Learning Based Approach for Table Detection on the Web.
In In Proceedings of the 11th Int’l Conf. on World Wide Web (WWW’02), pages 242–250. ACM Press,
2002.
Minoru Yoshida and Kentaro Torisawa.
Extracting Ontologies from World Wide Web via HTML Tables.
In In Proceedings of the Pacific Association for Computational Linguistics (PACLING 2001, pages 332–341,
2001.
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39. References III
If you want to go further
Xiaoxin Yin, Wenzhao Tan, and Chao Liu.
FACTO: a fact lookup engine based on web tables.
In Proceedings of the 20th international conference on World wide web, WWW ’11, pages 507–516, New
York, NY, USA, 2011. ACM.
Minoru Yoshida, Kentaro Torisawa, and Jun’ichi Tsujii.
A method to integrate tables of the World Wide Web.
In In Proceedings of the International Workshop on Web Document Analysis (WDA 2001, pages 31–34,
2001.
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