POPULATING A DOMAIN ONTOLOGY FROM A WEB
BIOGRAPHICAL DICTIONARY OF MUSIC
An Unsupervised Rule-based Method to Handle Brazilian Portuguese Texts
Eduardo Motta, Sean Siqueira and Alexandre Andreatta
Department of Applied Informatics, Federal University of the State of Rio de Janeiro (UNIRIO)
Av. Pasteur, 458, Urca, Rio de Janeiro, Brazil
Keywords: Information extraction, Ontology population, Natural language processing, Brazilian Portuguese.
Abstract: An increasing amount of information is available on the web and usually is expressed as text, representing
unstructured or semi-structured data. Semantic information is implicit in these texts, since they are mainly
intended for human consumption and interpretation. Since unstructured information is not easily handled
automatically, an information extraction process has to be used to identify concepts and establish relations
among them. Information extraction outcome can be represented as a domain ontology. Ontologies are an
appropriate way to represent structured knowledge bases, enabling sharing, reuse and inference. In this
paper, an information extraction process is used for populating a domain ontology. It targets Brazilian
Portuguese texts from a biographical dictionary of music, which requires specific tools due to some
language unique aspects. An unsupervised rule-based method is proposed. Through this process, latent
concepts and relations expressed in natural language can be extracted and represented as an ontology,
allowing new uses and visualizations of the content, such as semantically browsing and inferring new
knowledge.
1 INTRODUCTION
This paper describes an implemented method to
extract information from Portuguese texts on a rule-
based unsupervised manner. The information
extraction process output is represented as a domain
ontology.
An increasing amount of information is available
on the web and is frequently expressed as text,
representing semi-structured or unstructured data.
However, computational processes don’t easily
handle unstructured information. Information
extraction (IE) seeks to structure information by
adding meaning to raw data. It is defined by Moens
(2006) as “the identification, and consequent or
concurrent classification and structuring into
semantic classes, of specific information found in
unstructured data sources, such as natural language
text, making the information more suitable for
information processing tasks.”
A usual way to capture and represent knowledge
of a specific field (a knowledge domain) is through
the use of a domain ontology. According to Gruber
(2008), “an ontology defines a set of
representational primitives with which to model a
domain of knowledge or discourse”.
The
representational primitives are typically classes (or
sets), attributes (or properties), and relationships (or
relations among class members).
Therefore, the outcome of an IE process can be
represented as a domain ontology. Ontologies are an
appropriate way to represent structured knowledge
bases, enabling sharing, reuse and inference.
In this work, we present the implementation of
an IE process that populates a domain ontology. The
knowledge domain handled by our solution is the
biographical and artistic data on the music field.
The source of information is a biographical
dictionary of Brazilian popular music and the output
of the process is a populated domain ontology.
Through this process, latent concepts and
relationships expressed in natural language can be
extracted and represented, allowing new uses of the
available content, like integrating with other
information sources (based on semantic concepts) or
navigating the knowledge base on a hierarchical
manner. Moreover, after knowledge is represented
as a populated domain ontology that is enriched with
192
Motta E., Siqueira S. and Andreatta A.
POPULATING A DOMAIN ONTOLOGY FROM A WEB BIOGRAPHICAL DICTIONARY OF MUSIC - An Unsupervised Rule-based Method to Handle Brazilian Portuguese Texts.
DOI: 10.5220/0001842301920199
In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page
ISBN: 978-989-8111-81-4
Copyright
c
 2009 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

logical rules, inference engines can be used to derive
new knowledge not explicitly contained in the
source texts.
One of the benefits of representing the textual
content as a structured knowledge base is to allow
browsing through dimensions like year or place of
birth, type of instruments performed or even
navigating through relationships among artists, like
partnership.
The approach of this work is to exploit
information that is most relevant to the domain, such
as biographical data (e.g., date and place of birth and
death, and genealogical relations), artistic data (e.g.,
partnership) and career events (like recording or
releasing a CD, composing a song, appearing on a
TV show etc). To do so, we applied IE techniques to
obtain semantic information from text.
In particular, this work focuses on handling texts
in Portuguese for which it is necessary to deal with
some unique aspects while specific language tools
for Portuguese should be used.
The next section includes some background on
IE and ontology population. Section 3 is a
description of the dictionary structure. Section 4
presents the approach to this problem and the
methods applied. Section 5 presents implementation
issues and Section 6 summarizes some results.
Finally, Section 7 presents related works and
concluding remarks.
2 BACKGROUND
2.1 Information Extraction
Information extraction has been applied in the last
years in different contexts and using different
techniques. IE methods can be categorized in
learning and knowledge engineering approaches
(Kaiser and Miksch, 2005). Learning methods
require an annotated corpus, which is usually
expensive to obtain. Knowledge engineering
methods use rules that are hand crafted and
improved on an iterative process or obtained using
an unsupervised technique.
Information extraction can be used to obtain
entities (ontology classes), attributes (ontology
classes’ slots), facts (relations between classes) and
events (activities or events where entities take part)
(Feldman and Sanger, 2007).
Several tasks are necessary to accomplish IE,
such as named entity recognition, anaphora
resolution, coreference resolution, template element
task, template relationship task, and scenario
template production. Additionally, general-purpose
natural language pre-processing steps are necessary,
like tokenization, paragraph and sentence splitting,
PoS (part-of-speech) tagging, shallow or full parsing
etc.
2.2 Ontology Population
Ontologies have gained popularity in recent years
due to the emergence of the semantic web, where
ontologies play a central role. One of the bottlenecks
of the semantic web is acquiring semantic content,
i.e, semantically annotating the existing content on
the web. Text content available on the web is
primarily intended for human consumption which
means that the semantics contained in texts is not
explicitly represented but is in the reader’s mind.
Automatically (or semi-automatically) acquiring
domain knowledge through ontology population is
an important contribution towards semantic web
applications. After extracting semantic information
from text and representing it as a domain ontology,
new applications arise, such as integrating
information, navigating the knowledge base on a
structured manner and finding new relationships not
explicit from the source texts.
Ontology learning is the task that aims at
discovering concepts and relations from text. In
contrast, ontology population (OP) from text is the
task of acquiring instances of concepts and their
relations from text (Tanev and Magnini, 2006).
IE and OP are closely related in the sense that an
ontology can be used to represent the IE process
output and, on the other hand, knowledge
represented in the ontology can help the IE cycle.
This interaction between IE and OP is referred to as
ontology driven information extraction (Yildiz and
Miksch, 2007).
3 DICTIONARY DESCRIPTION
The Cravo Albin’s Dictionary of Brazilian Popular
Music (Dicionário Cravo Albin da Música Popular
Brasileira) (Albin, 2008) is maintained by the Cravo
Albin Cultural Institute, a civil, non-profit
organization headquartered in Rio de Janeiro City
and established in 2001. The dictionary contains
more than 5.000 entries with information on
biography, artistic data, shows, video clips and
related bibliography of Brazilian popular music
artists. It is available on the Internet, and it is
organized and accessed by artist noun entries. It is
possible to search for entries, but there is no way to
POPULATING A DOMAIN ONTOLOGY FROM A WEB BIOGRAPHICAL DICTIONARY OF MUSIC - An
Unsupervised Rule-based Method to Handle Brazilian Portuguese Texts
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navigate from entry to entry, since there are no
hyperlinks. Although the whole content of the
dictionary is available on the Internet, it is organized
much like as a paper dictionary, i.e., there is linkage
between topics or browsing facilities through its
contents. The only function available is a search by
main entry (artist noun). Each entry is presented on
one or more HTML pages.
Figure 1 shows the home page of the dictionary.
Figure 1: Dictionary’s home page.
Information is split into the following sections:
full name, birth date and place, death date and place,
biography, artistic data, work, discography, shows,
video clips, historical and artistic data, reviews and
bibliography.
Figure 2 shows a biography detail page example.
Figure 2: Biography detail page example.
According to Abiteboul et al (2000) data can be
classified, in terms of its structure, in three
categories. Data can be structured (both schema and
data types are known), semi-structured (schema or
data type is known) and unstructured (schema and
data type undefined). Typically, a web page contains
data in the latter two categories.
3.1 Semi-structured Information
Some data as name, full name, birth place and year
are presented as semi-structured information
(although with variable completeness). Some
examples are shown in figure 3.
Figure 3: Semi-structured data represented as text.
The star icon indicates birth date and place. Note
that completeness and precision may vary. The first
example has a full birth date (4/5/1953), birth city
(Rio de Janeiro) and birth state (RJ), while the
second shows only the year of birth and the last has
only city and state but no date information.
Information on discography, video clips, main
shows and bibliography are presented on a semi-
structured manner, as text, but following a standard
format (structure and style). For instance, for
discography section (discografia, in Portuguese),
records are listed with publication year, record
company and media type, like depicted in Figure 4.
Figure 4: Semi-structured data represented as text.
3.2 Unstructured Information
Most of information is presented as free, natural
language text, like in the artistic data and biography
sections, as shown in Figure 5.
Figure 5: Unstructured data, plain text.
The corpus used in this work is written in
Portuguese, but some translated English examples
are presented for the sake of readability.
Maria Bethânia
Singer. Considered to be one of the greatest
interpreters in the history of Brazilian popular music.
Sister of the singer and composer Caetano Veloso.
She began her artistic career in 1963, appearing in the
play “Boca de Ouro” by Nelson Rodrigues.
...
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4 ONTOLOGY POPULATION
Since the source information is a web site having no
previous annotation, we have implemented an
unsupervised semi-automatic method to generate
extraction rules and templates using heuristics based
on linguistic features, such as words and PoS tags.
4.1 Pre-processing
The first step was to fetch web pages and to identify
HTML tags to separate sections from the dictionary.
During analysis of the page structure all
necessary data to next steps were identified. Sections
were identified through formatting marks, such as
bullets and images. There was a one-to-one
relationship between these marks used in HTML
page and contents of the sections. These HTML tags
were used together with regular expressions to
determine sections boundaries. Table 1 shows some
mapping examples.
Table 1: Mapping examples.
Pattern used on source
Mapped
Content
HTML TAG
<IMG SRC="img/bul_nasc.gif">
plus
Regular Expression
Birth date
Birth place
HTML TAG
<IMG SRC="img/bio.gif">
plus
Other section beginning or page
end
Biography
free text
Each dictionary entry is composed by one or
more HTML pages that have a naming convention
that was used to extract the corresponding content.
During the extraction from HTML, non-
informative tags were filtered out in order to end
with plain text corresponding to each section. Plain
text was then loaded on a database to allow further
processing.
During this stage special characters were also
transformed to make them compliant to the linguistic
tools that were used. For example, special UTF-8
open (“) and close (”) quotation marks were
converted to ordinary quotation marks (").
4.2 Information Extraction
Information extraction requires some general
purpose NLP processing like tokenization and
domain specific tasks such as entity extraction. The
following subsections describe the steps performed
in the proposed method.
4.2.1 General Purpose NLP
Sentence splitting, tokenization and part of speech
tagging were executed.
Named entities were classified (according to the
domain ontology) in Artist (subclass of Person),
Company (companies are divided into Record labels,
Publisher companies and others) and Work (CDs,
DVDs, Vinyl etc).
4.2.2 Temporal Expression Identification
Two types of temporal expressions were handled.
Type 1 is formed by expressions containing dates
in numerical form, like “01/04/1970” or “1970,
January”. This kind of expression always includes at
least one numeric part. Nevertheless, many levels of
precision and uncertainty may be expressed in this
kind of expression, like “in the beginning of year
2000”, “circa 1870” or “in the middle of 1999”.
These levels of precision and uncertainty were taken
into account and represented on the ontology. Using
the numeric expression as a trigger, and
morphosyntactic rules, we constructed a list of
patterns and classified each expression according to
the ontology classes. These rules are based on word
and PoS features. Table 2 shows some temporal
expression patterns generated, indicating the chosen
class and precision.
Table 2: Temporal expression pattern examples.
Pattern Class Precision
em <ANO>
in <YEAR>
Instant Year
entre <ANO1> e
<ANO2>
between <YEAR1> and
<YEAR2>
Defined
Interval
Year
a partir de <MÊS> de
<ANO>
from <MONTH>
<YEAR> on
Interval,
left open
Month
circa <ANO>
circa <YEAR>
Around
Instant
Year
Type 2 contains anaphors of type “in the
following year” or “two years later”. This kind of
expression must be resolved, linking them to the
referent expression, in order to obtain a value for the
temporal attribute. This is performed using a simple
heuristic inspired in Mitkov’s algorithm (Chaves and
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Rino, 2008). The nearest previous date expression
identified as type 1 as described above was taken as
the antecedent.
4.2.3 Artistic Works Identification
and Classification
The main feature used for identifying artistic works
(e.g. songs, shows, CDs etc.) was a verb related to
an artistic activity, like the verbs to perform or to
record, that are related to artistic activities. In
particular, due to the dictionary’s descriptive nature,
verbs appear mainly in the past tense, such as
gravou (recorded) or lançou (released). These verbs
are used as triggers for frames defined to extract
information on the object of the action (the patient,
like a song or CD), who performed it (the agent, e.g.,
the artist involved in the action) and when such
activity represented by the verb was performed
(temporal adjunct). This task can be seen as a
simplified approach to the semantic role labelling
problem.
Figure 6 shows a frame example used to extract
information for the verb lançou (released).
Figure 6: Extraction frame sample.
In order to classify the artistic works, a gazetteer
of work types and song genres was used in
conjunction with morphosyntactic patterns. This
gazetteer was constructed and pre-loaded on the
ontology to support classification during information
extraction.
4.2.4 Genealogical Relations
Genealogical relations were also managed using
frames and a lexical database of expressions to
detect genealogical relations, such as filho de (son
of), mãe de (mother of) or neta de (granddaughter
of). These linguistic patterns are linked to the
corresponding ontology classes.
4.2.5 Partnership Relations
Partnerships (such as composing a song together or
participating on the same record) were approached
in a similar manner to genealogical relations.
4.3 Ontology Preparation
Music Ontology (Giasson and Raimond, 2007) was
selected as a base ontology to represent the specific
concepts of the music domain.
However, since some concepts necessary to
represent the extracted information were not
available on Music Ontology, a new, extended
ontology was constructed based on it, adding new
concepts and relationships.
From the Music Ontology the concepts of
MusicArtist, MusicGroup, SoloMusicArtist,
MusicalManifestation were used and translated to
Portuguese to support linkage to the text content.
OWL-DL was chosen to represent the ontology,
due to its sufficient expression power and tools
available to deal with it (Cardoso, 2007).
In order to get insight on the most relevant terms
(nouns and verbs) in this corpus, statistics on tokens
tagged as verb or nouns were performed.
Table 3 shows the top 5 verbs and top 5 nouns
(common names) found in the dictionary.
Table 3: Top 5 verbs and top 5 nouns.
PoS TAG Word
V
(Verb)
gravou (recorded)
lançou (launched)
participou (participated)
apresentou (presented)
interpretou (interpreted)
CN
(Common
Name)
ano (year)
disco (record)
samba
música (song)
parceria (partnership)
Due to the characteristics of events described in
this dictionary, a time ontology is essential for
capturing the semantic expressed in texts. Time is
represented based on Allen’s theory of time (Allen,
1991), where two kinds of entities exist: instant and
interval. An instant is characterized by a “point” in
time that can have different precision levels, like a
Verb (frame trigger)
lançou (launched)
Performer (agent)
In general, the dictionary entry is the performer, since
most of the time, narrative is elliptical. Exception
occurs when a named entity classified as an artist
immediately precedes the verb lançou.
Temporal adjunct
Determined as described in section 4.2.2
Work (patient)
Taken from the named entity recognition step.
Sentence structure analysis is performed to catch
multiple works enumerated with commas and
conjunctions.
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specific day, year or decade etc. An interval is
defined by a starting instant and an ending instant.
Furthermore, textual description of historical events
usually contains some vagueness like “in the
beginning of year 2000” or “circa 1890”. Thus, the
ontology must also be able to capture this kind of
statements with vague concepts. (Mani and Wilson,
2000).
Ontology population from text demands handling
imperfect information. Imperfection of information
can be categorized as imprecision (vagueness),
inconsistency (contradictory information) or
uncertainty (trust level) (Haase and Völker, 2005).
In the dictionary’s case, we dealt with
imprecision, as we took the authority of the
dictionary as trustable and assumed the content is
consistent, since it is maintained by a dedicated
group of music researchers.
4.4 Ontology Instantiation
After all the annotations described in section 4.2
were performed, the intermediate database was used
to create instances of classes and relations described
in the ontology. Before committing the extracted
information to the ontology, a manual validation
step was performed to ensure ontology consistency.
5 IMPLEMENTATION
To implement the described approach, a framework
was developed based on a subset of the conceptual
model proposed by Graça et al (2006). This
framework enables processing text and having
multiple annotating levels. It holds all the annotation
performed during 1the IE process that is used
afterwards to instantiate the ontology concepts and
relations. An overview of the framework is depicted
in figure 7.
Pre-processing task fetches and processes HTML
pages and then store the output to the MySQL
database. Information extraction tasks annotate text
on multiple levels, like PoS tags, lemmas and named
entities boundaries. During this phase, some
information is also retrieved from the ontology, like
gazetteers for work types and song genres, which
were pre-loaded on the ontology by a separate
process. The Ontology Instantiation step read
annotated data from the database and creates classes
and relations instances.
Figure 7: Framework overview.
The solution was implemented using MySQL 5.1
and programs were developed in Java and Python.
Paragraph splitting was performed based on
formatting characteristics of the HTML pages. To
perform sentence splitting, tokenization and PoS
tagging, LX-Suite was used (Branco and Silva,
2006). LX-Suite is a freely available online service
for the shallow processing of Portuguese.
To identify named entities, both structural hints
(like parenthesis and quotation marks) and PoS tags
output by LX-Suite (namely the PNM PoS tag for
part of a proper name) were used.
Lemmatization was performed using the NILC
lexical database (Muniz et al, 2005).
Protégé 3.3.1 (Protégé, 2008) was used to create
the ontology. To instantiate concept and classes, the
open-source Java library Protégé-OWL API was
used (Knublauch, 2006).
6 RESULTS
In order to evaluate the information extraction
process performance, a subset of 374 entries of the
dictionary was used. This subset contains 9.912
sentences and 295,977 tokens.
From these sentences, 1,102 contain the verb
gravou (recorded), 943 of them contain temporal
adjuncts. The other ones have no time information,
as they simple state facts without temporal
properties, like “Cláudia recorded a vinyl record in
Japanese”.
Using the proposed heuristics to generate time
expression extraction templates, a set of 33 different
patterns was obtained.
Applying these patterns to this test set, 89.9%
of time expressions were extracted (recall) and
86.3% (precision) were correctly mapped to the
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ontology. This result corresponds to a F1 score of
88.1% for time expressions extraction.
Precision and recall was evaluated by manually
tagging and inspection over this sample.
From a set of 260 sentences containing relative
temporal expressions (anaphoric), 80.4% were
correctly resolved using the proposed heuristic.
A small fragment of the populated domain
ontology represented as a graph is shown in Figure
8. It shows classes for Work (Record) and Artist
(SoloMusicArtist) and some of their corresponding
instances.
Figure 8: Ontology fragment sample.
7 DISCUSSION
This paper presented an unsupervised rule-based
method for information extraction from Brazilian
Portuguese texts that reaches a reasonable extraction
performance with no need for training data.
Although the herein proposed method is dependent
on the domain and the source, it allows extracting
relevant information to the domain without having to
fully annotate the corpus, what would be required if
a supervised method had been used. Other
contributions of this work are dealing with
Portuguese texts, and building a domain ontology
for the popular Brazilian music history.
7.1 Related Work
Other unsupervised approaches for OP are described
in (Hearst, 1992) and in (Cimiano and Völker,
2005). Hearst patterns were adapted in our method
to consider also structural patterns and verb valence.
Cimiano and Völker use a dependency parser which
is not available for Portuguese.
A weakly supervised method is described by
Tanev and Magnini (2006). Although it has better
performance than the unsupervised approaches, it
requires a training set.
7.2 Future Work
A potential use of the populated ontology is to
support a semantic browser, where the original text
from the dictionary can be visualized together with
the semantic labels, like described in (Quan and
Karger, 2004).
The populated ontology also permits visualize
links not explicitly expressed in the texts, like people
born at the same city or year. Navigating through
these discovered dimensions can reveal interesting
associations among artists.
Additionally, anchoring to other sources can
expand information on the entries. For example, the
CliqueMusic site (CliqueMusic, 2008) also contains
information on Brazilian musicians, so that the
ontology can be enriched (or at least linked to) the
corresponding entries on the other database.
To deal with multiple sources, the ontology must
be able to handle conflicting and different trust
levels. So another path for evolution is to extend the
ontology to support imperfect information, as
discussed in (Haase and Völker, 2005).
Inference engines like the one described in
(Haarslev and Möller, 2003) can be applied to the
OWL ontology in order to derive new knowledge.
Some examples include inference using genealogical
relations, temporal overlapping (that can be used to
determine that two artists were contemporaneous),
geographical hierarchies like city contained in a state
and so on.
Although in the case of dictionary it was not
necessary due to relatively simple and regular
HTML structure, another possible evolution is to
implement a wrapper induction process to generalize
HTML page extraction (Chang et al, 2006).
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