=Paper=
{{Paper
|id=Vol-2728/paper2
|storemode=property
|title=A Prototypical Semantic Annotator for A Tribuna Newspaper
|pdfUrl=https://ceur-ws.org/Vol-2728/paper2.pdf
|volume=Vol-2728
|authors=Elias de Oliveira,Wesley Silva,Juliana C. Pirovani,Jean-Rémi Bourguet
|dblpUrl=https://dblp.org/rec/conf/ontobras/OliveiraSPB20
}}
==A Prototypical Semantic Annotator for A Tribuna Newspaper==
https://ceur-ws.org/Vol-2728/paper2.pdf
A Prototypical Semantic Annotator for A Tribuna Newspaper
Elias de Oliveira1 , Wesley Silva1 Juliana P. C. Pirovani2 , Jean-Rémi Bourguet3
1
Programa de Pós-Graduação em Informática
Universidade Federal do Espı́rito Santo
Av Fernando Ferrari, 514, Goiabeiras – Vitória, ES 29075-910
2
Departamento de Computação
Universidade Federal do Espı́rito Santo
Alto Universitário, s/n – Guararema – Alegre, ES 29500-000
3
Departmento de Ciência da Compuação,
Universidade Vila Velha, Vila Velha, Brasil
jean-remi.bourguet@uvv.br, {elias,juliana}@lcad.inf.ufes.br
Abstract. The issue of recommending an appropriate piece of information has
become essential for the news portals. In this context, a well founded ontologi-
cal layer represents actually an indispensable artifact to suggest relevant news
for the readers. However, news agencies still need to mine their data in order
to discover valuable knowledge. In this paper, we present a prototypal auto-
matic semantic annotator for the regional Brazilian newspaper called A Tri-
buna. Founded on a set of inductive algorithms allowing to classify newspapers
in Portuguese and extract named entities from them, our approach describes the
standardized categorization and the semantic matching with DBpedia, the so-
called nucleus of the Linked Open Data Cloud. We discuss the limitation of our
prototype and draw some challenging perspectives to face them. Finally, our
proposal paves the way to a new kind of recommendation-based systems.
1. Introduction
Nowadays, we surely can encounter a large amount of information from various news
portals around the world. News agencies have become modern mining platforms by con-
tinuously gathering new facts and producing new narratives into their printed or digital
materials. One can use this font to look into what is happening in a city, state, or country.
The traditional way of reading newspapers is by browsing their pages in order to discover
interesting items. Nevertheless, with the recent development of advanced methodolog-
ical and computational artifacts, the newspaper portals grow into producers of sugges-
tions for their readers. As mentioned, this huge bunch of data has to be well managed
to perform accurate recommendations by selecting particular items and discarding oth-
ers. For example, if a given user explicitly indicates a preference for a kind of news
concerning innovations in Information Technology, the recommendation system may rec-
ommend some new Artificial Intelligence-based tools. Such an approach is founded on
content-based filtering [Pazzani and Billsus 2007]. On the other hand, it exists a collab-
orative filtering approach [Herlocker et al. 2004] in which the system generates a group
of similar users in terms of interests producing a recommendation based on the analysis
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
of their characteristics. In order to be able to release a competitive recommendation-
based system, a well founded ontological layer represents an indispensable contemporary
artifact [Cantador et al. 2008]. Indeed, by taking advantage of accurate semantic annota-
tions [Wetzker et al. 2009] and domain-specific semantic networks [Nguyen et al. 2014],
such systems are able to recommend items which a priori would not be revealed through
classical Vector Space Model techniques [Baeza-Yates and Ribeiro-Neto 2011].
In [Branquinho-Filho and Oliveira 2017], an experimental approach is released to
classify journalistic documents published on a newspaper A Tribuna. Founded in 1938,
A Tribuna is currently the main regional newspaper of Espı́rito Santo - Brazil. Later,
another work extended this approach by providing a framework extracting named enti-
ties [Pirovani and Oliveira 2017]. In order to enrich semantically the news metadata, a
Named Entity Recognizers (NER) is classically used to map elements of a document with
some well known instances on the Semantic Web (see [Troncy 2008] for example). Al-
though complex, NER is an important task to the Natural Language Processing (NLP).
It can be understood as the task of identification and annotation of the Named Entities
(NEs) in free-written text corpora. In turn, a NE can be defined as a sequence of words
that is capable of representing a real-world entity [Zhang et al. 2019]. In Portuguese,
few works have tackled NER-based tasks [Pirovani et al. 2019]. Among these proposals,
the hybrid approach CRF+LG proposed in [Pirovani and Oliveira 2017] outclassed the
results obtained by other systems performing under equivalent conditions. Conditional
Random Fields (CRF) is a supervised statistical algorithm to predict an output vector
y = {y0 , y1 , · · · , yT } based on the random variables given an observed features’ vec-
tor x, an input vector of features {x0 , x1 , x2 , · · · , xT } [Sutton and McCallum 2011]. One
of the features, ys , can conceptualize the NE class. Then, the goal is to maximize the
number of labels ys ∈ y that are correctly classified, mapping x 7→ ys , for each s. The
CRF+LG algorithm works first classifying the entities using Local Grammars (LG), us-
ing the Unitex tool1 . In addition to post-tagging structures, the LG result is used as an
additional feature for training the CRF model. The key strength of this approach is the
combination of the probabilistic and linguistic models. Even if the newspaper texts have a
generic structure, they gather heterogeneous data with content related to economics, pol-
itics, sports, entertainment, among others. The literature already pointed out that the NEs
present in these texts can represent efficient supports for information retrieval, whether
used as indexing items [Pirovani et al. 2018], for clustering tasks [Spalenza et al. 2019],
for classification tasks [Nadeau and Sekine 2007], or for automatic question generations
[Pirovani et al. 2017].
In this work, we present a prototypal automatic semantic annotator for A Tribuna.
Supported by the aforementioned seminal proposals, our approach describes a standard-
ized categorization of the news articles and a semantic matching of their contents with
DBpedia.
The rest of this paper is structured as follows: Section 2 presents some related
works. Section 3 describes the upstream inductive approach of our automatic annotator
while Section 4 presents the downstream semantic categorization and matching. Finally,
Section 5 presents the main conclusions so far achieved at this phase of our prototype
release.
1
https://unitexgramlab.org/
2. Related Works
Semantic annotation is not a novelty for the journalists. For a long a time, they have been
using their proprietary semantic tools to manually annotate contents of news items by
filling in some forms. For example, BBC created its own ontology2 for modeling its own
news articles.
Early in 2000, the project PlanetOnto [Domingue and Motta 2000] extended a
news server by providing support for ontology-driven document formalization integrat-
ing browsing and deductive knowledge retrieval, personalized news feeds and alerts, and
proactive identification of potentially interesting news items. Soon after, NAMIC (News
Agencies Multilingual Information Categorisation) [Basili et al. 2001] was released as
an architecture to extract relevant facts from the news streams of large European news
agencies and to support semantic inferences by aligning the extracted concepts with Eu-
roWordNet objects [Vossen 1998]. Neptuno [Castells et al. 2004] presents an emergent
semantic-based technologies to improve the processes of creation, maintenance, and ex-
ploitation of the digital archive of a newspapers based on a knowledge base supported
by an ontology for the description of journalistic information, a semantic search module
and a module for content browsing and visualization. PENG (Personalised nEws coNtent
programminG) [Pasi et al. 2006] provides a set of functionalities for gathering, classify-
ing and filtering heterogeneous news materials (television, radio, magazine) considering
a number of individual interests.
Indeed, news items can be represented in a lot of formats like XML-based ones for
instance. Nevertheless, News Industry Text Format (NITF) or NewsML remains two of
the most widespread formats standardized by the International Press Telecommunications
Council (IPTC). Thus, a relevant initiative related in [Troncy 2008] tries to bring the IPTC
news architecture into the Semantic Web by designing a workflow to populate computa-
tional ontologies and enriching semantically the news metadata by processing text and
performing visual analysis of photo and video of news items. Named Entity Recognizers
such as GATE3 , SPROUT4 or OpenCalais5 have been used. Once the named entities are
extracted, they are mapped to well known instances on the web (using Geonames for the
locations and DBPedia for the persons and organizations).
As it is mentionned, the field of newspapers has already been tackled by
approaches dealing with artificial intelligence and semantic web techniques in reason
of potential social fallouts. For example, some works relate how the journalists can
use the Semantic Web standards to support the news angles creation and news pro-
duction [Heravi et al. 2012, Opdahl and Tessem 2020, Panagiotidis and Veglis 2020].
But, in [Moreno et al. 2015], the authors point out a certain distance between metadata
standards identified in the literature review and those in the HTML tags of the newspaper
industry emphasizing the importance and needs of semantic alignments. Different
initiatives have proposed ontological-based infrastructures in the journalism domain
focused on linked open data-based strategies.
2
http://www.bbc.co.uk/ontologies/storyline
3
http://gate.ac.uk/
4
http://sprout.dfki.de/
5
http://www.opencalais.com/
In [Hopfgartner and Jose 2010], the authors extract named entities from news
videos teletext using OpenCalais. Moreover, OpenCalais WebService is used to compare
the actual entity string with an up-to-date database of entities and their spelling variations.
This disambiguation maps these entities with a Uniform Resource Identifier (URI) and
their representation in DBpedia. Then, the authors exploit the Linked Open Data Cloud
(i.e. by using the SKOS vocabulary in DBpedia) to identify similar news stories that match
the users interest. In [Aksaç et al. 2012], the authors release a semantic web browser that
allows users to browse news web pages and also access related data resources via annota-
tion and a side-bar listing all found linked data resources. In [Papadokostaki et al. 2017],
the authors present an integrated platform dedicated to news articles, providing storage,
indexing and searching functionalities by using semantic web technologies and services.
Finally, one of the last and most complete proposal was realized through the Eu-
ropean project NEWS (News Engine Web Services) [Garcı́a et al. 2006] consisting of a
set of facilities like automatic extraction of metadata from news items’ contents, named
entity disambiguation [Garcı́a et al. 2012], storage, retrieval of news items. Their NEWS
ontology [Garcı́a et al. 2010] covers the different types of metadata that can be attached
to a news item: management, categorization and content metadata. The project also pro-
duced components and algorithms [Garcı́a et al. 2007] that automatically detect entities
and events mentioned in a newspaper text and link them to instances in their NEWS on-
tology. Notice that the content annotation module of the NEWS ontology is partially
inspired by SUMO [Niles and Pease 2001] and MILO [Niles and Terry 2004].
3. Upstream Inductive Approach
Our goal has two folds. Firstly, we want to assign one of the twenty-one possible subject
topics to the news article: 1) Atualidades (Current Affairs), 2) Qual a Bronca? (What’s
up?), 3) Cidades (Cities), 4) Ciência e Tecnologia (Science and Technology), 5) Concur-
sos (Public-Exam Competitions), 6) Economia (Economy), 7) Esporte (Sports), 8) Espe-
cial (Special), 9) Famı́lia (Family), 10) Imóveis (Real State), 11) Informática (Computers
& Electronics), 12) Internacional (International), 13) Minha Casa (My Home), 14) Mul-
her (Woman) 15) Opinião (Opinion), 16) Polı́cia (Police), 17) Polı́tica (Politics), 18) Re-
gional (Regional County), 19) Sobre Rodas (On Wheels), 20) Tudo a Ver (Everything to
do with), 21) TV Tudo (All TV). To accomplish this goal, we will apply a similar but im-
proved approach used in [Branquinho-Filho and Oliveira 2017], where each news article
document was turned into a vector of weighted word-frequency, known as the bag-of-
words approach.
Secondly, we use some NLP tools and methodologies to perform the meta-
information extraction from the news free-text format. The meta-information we are
interested in are any of the five possible named entities: 1) Organization (ORG), 2) Per-
son (PER), 3) Local (PLC), 4) Time (TME), and 5) Value (VAL), mentioned in the news-
article texts.
The news-article objects are all in PDF format at the site
https://tribunaonline.com.br/. So, we downloaded them and extracted 45,908 arti-
cles to perform the undermentioned algorithms.
3.1. The Classification of Topics Problem
The classification of documents is a hard task these days of information overload
[Bawden and Robinson 2009]. The problem we have at hand is to deal with 45,908 news
articles, a tiny portion of the total newspaper archive of only one information source.
Hence, arguments in favor of automation of this activity are unnecessary. Table 1 shows
the number of documents in each class of this data set used for our experiments.
Class #Docs Class #Docs Class #Docs
Atualidades 5617 Especial 1470 Opinião 1634
Qual a Bronca? 346 Famı́lia 442 Polı́cia 4671
Cidades 5234 Imóveis 124 Polı́tica 5918
Ciência e Tecnologia 470 Informática 1506 Regional 1802
Concursos 309 Internacional 2187 Sobre Rodas 352
Economia 6558 Minha Casa 37 Tudo a Ver 30
Esporte 6657 Mulher 103 TV Tudo 440
Table 1. The number of documents within each class.
While in the first, third, and fifth columns, we show the name of those classes also
presented in the introductory part of Section 3, in the second, fourth, and sixth columns the
quantities of documents for their respective class. Note that the majority of news article
documents are about Esporte (Sports) class, with 6,657 files – shown in the last line of the
first column. Whereas, the least populated is Tudo a ver, with only thirty document files,
in the line before the last, in the fifth column.
After transforming each news article in a weighted word-frequency vector, we se-
lected a sample of these documents to serve as training, and the remaining used to test the
classification algorithm. Within the training sample, we also subdivide this sample into
two sets: one for the actual training and another for validation, plying as testing to maxi-
mize the classifier performance. We used the Gradient Boosting algorithm implemented
into the scikit-learn6 for the Python programming language.
The goal is thus to mimic humans assigning each news articles to the already
known subject class to which they belong. The process took all together less than fifty
minutes, and the quality measured – more than 98% of accuracy – is much encouraging,
as to the best of our knowledge we do not know a similar performance figure to do a pair
comparison when a similar task is carried out totally by humans.
3.2. The Named Entity Recognition Problem
Mining pieces of meta-information from free-texts is still a challenging task in the
academia and industry [Augenstein et al. 2017, Nadeau and Sekine 2007]. Researchers
are trying to catch up with the performance level reached by NERs systems in English
also for the Portugues language [Collovini et al. 2019].
A hybrid approach combining CRF+LG proposed in [Pirovani et al. 2019] is the
same strategy adopted in our experiments. This approach, besides the advantage already
6
https://scikit-learn.org/stable/
mentioned in Section 1, requires lesser training data to achieve competitive results. For the
sake of illustration, in our experiments, we manually annotated 100 newspaper documents
of the A Tribuna7 dataset. In total, we found 1,354 NEs of the class Person, for instance.
In Table 2, we depicted the results when using 80% of documents for training and the
remaining for test.
Method Metrics
P R F1
LG 60.72 46.41 52.61
CRF+LG 55.00 58.97 56.92
ORG 18.17 47.35 26.26
PER 58.64 70.45 64.01
PLC 43.24 41.27 42.23
TME 78.76 79.92 79.34
VAL 53.80 33.21 41.07
Table 2. Results of automatic extracting NEs
The first column, in Table 2, shows the strategy used to yield the results in the
following columns. The LG and the CRF+LG approaches are respectively in the third and
fourth lines. From the fifth line onward, are the best results by the CRF+LG approach.
The Precision, Recall, and F1 metrics are respectively in the third, fourth, and
fifth columns. The worst results for F1, an average of 26.26%, is that carried out by the
CR+LG for the ORG entity class, in the fifth line. The best figure for recall, 79.34%, in
the fifth column, eighth line, is obtained for the TME class. The LG was superior to the
CRF+ÇG in precision, but the later was superior in all the remaining metrics.
Once the newspaper item output the upstream inductive approach, our prototype
will perform standardized categorizations and semantic matchings with DBpedia.
4. Downstream Semantic Alignments
The initial approach presented in the Section 3 was thought to support an internal catego-
rization of the news article for archival data storage. As presented in the subsection 3.1,
there were 21 selected categories. Our approach plugs a semantic annotator downstream
of the inductive algorithms. The IPTC NewsCodes defining 36 thesauri, we will focus on
items subjects consisting of about 1400 terms organized into a taxonomy of three levels.
Each Subject Reference is identified by an eight-digit decimal string. The terms are orga-
nized in a taxonomy as described in the Figure 1. Originally, the subsumption relationship
is not explicit but instead encoded into the coding scheme identifying the terms. For ex-
ample, ”survey” (subj:13006001) is narrower than ”research” (subj:13006000)
which is narrower than ”science and technology” (subj:13000000) because they share
the two and the four first digits.
7
http://www.inf.ufes.br/∼elias/dataSets/aTribuna-21dir.tar.gz
Figure 1. IPTC NewsCodes Subjects Code Taxonomy
IPTC shares its Controlled Vocabularies (CV) by a server at http:
//dev.iptc.org/NewsCodes-CV-Server. The users can use this server
for the retrieval of full CVs or only single concepts. The datasets of the CVs and
concepts are delivered in five different formats: HTML as human readable variant, and
NewsML-G2 Knowledge Items (XML), RDF/XML or RDF/Turtle and JSON/JSON-
LD as primarily machine readable variants. Thus, the thesauri are extractable into
SKOS, an application of RDF, making the subsumption relationships explicit (i.e.
skos:narrower, skos:broader). Each term is thus identified by a dereferencable
URI. Moreover, SKOS allows to encode other semantic relations with other controlled
vocabularies (from IPTC or from other vocabularies in the LOD).
For example, the two triples presented below and extracted from the IPTC News-
Codes Subjects vocabulary called cptall-en-GB.rdf described the fact that the topic
scientific research (scientific and methodical investigation of events, procedures and in-
teractions to explain why they occur, or to find solutions for problems) with the URI
medtop:20000735 is exactly similar to the subject research (a methodical investiga-
tion of events or procedures to explain why they occur, or to find solutions for problems)
with the URI subj:13006000; and presents a semantic similarity with the subject sur-
vey (examination of public attitudes on various subjects or issues, such as the quality of
goods, the value of services) with the URI subj:13006001.
subj:13006000 skos:exactMatch medtop:20000735
subj:13006001 skos:closeMatch medtop:20000735
One of the objectives of our framework is to provide an automatic semantic
news categorization. Our semantic categorization module is producing assertions by us-
ing a own made controlled vocabulary called cjat.rdf. In this vocabulary, we en-
coded some alignments between the subjects from a Tribuna (those presented in Ta-
ble 1) with the IPTC NewsCodes Subjects. In Figure 2, the vocabularies cjat.rdf,
cptall-en-GB.rdf and their alignments are visualized by the sparna-labs skos-
play 8 .
Figure 2. Alignments in cjat.rdf
All the outputs obtained by the NER algorithm presented in previous section will
be used as potential matchers with resources on DBpedia. We use the DBpedia Lookup
Service9 to look up DBpedia URIs by related keywords (label or anchor text). Note
that, in this prototype version, we retain the first ranked resource as matcher. Once the
type of the article is encoded and all the matchers are gathered, our writer module will
build the RDF representation of the news article. The URI of the article is formed by the
concatenation of the namespace da Tribuna with the date of the edition, the beginning
8
http://labs.sparna.fr/skos-play/
9
https://wiki.dbpedia.org/lookup/
page and an integer representing the id of the article in this page. In the Figure 3, we
present an article published on the 1st of March 2019 in the fourth page and identified by
the URI https://tribunaonline.com.br/010319p04a2. After inputting the
automatic semantic annotator and then the writer module, the RDF excerpt is produced.
We chose to use the BBC Creative Work Ontology 10 to support this phase. The reasons
are multiple: this is a core ontology well established and recognized, the object property
category (resp. tag) allows to encode properly the categorization of the article (resp.
the relation with the extracted NEs) and alignments are already encoded towards other
vocabularies.
At the end of our current workflow, the RDF Graph of the article is obtained by
using the ontology-visualization API11 .
Figure 3. Prototype
Even if our approach is promising, some important issues have to be
solved. A disambiguation can be required to select the right matcher. For exam-
ple, http://dbpedia.org/page/Vale maps actually to more than 30 possible
resources. Note that, such a disambiguation process could also be performed among
the ranked output resources of the DBpedia Lookup output. We intend to browse
a small part of the DBPedia semantic network in order to find some evidence to
select (or not) the best matcher. Another recurrent issue is the absence of the proper
resources in DBpedia but not in Wikipedia. In the news presented above, it was
the case for https://pt.wikipedia.org/wiki/EF-118, https://pt.
wikipedia.org/wiki/Estrada_de_Ferro_Vit%C3%B3ria_a_Minas and
10
https://www.bbc.co.uk/ontologies/creativework
11
https://github.com/fatestigma/ontology-visualization
https://pt.wikipedia.org/wiki/Ferrovia_Litor%C3%A2nea_Sul.
As wikipedia webpages represent semi-structured semantic datasets, it could be interest-
ing to process them also.
Finally, we plan to perform matchings with other dataset among the LOD Cloud.
An intuitive perspective would be to perform the disambiguations of the locations by
consulting Geonames or Geoplanet in addition. Implementing a system of vote among
the matchers could support such a task.
5. Conclusions
In this paper, we presented a prototypal semantic annotator from a free-text archive of the
regional Brazilian newspaper called A Tribuna. Founded on a set of inductive algorithms
allowing to classify newspapers in Portuguese and extract NEs from them, our approach
describes both an upstream inductive approach and a downstream semantic categorization
alignments with IPTC NewsCodes Subjects taxonomy and NEs matching with DBpedia.
We also discussed the current limitation of our prototype and draw some challenging
perspectives to face them. Nevertheless, up to this stage, we are ready to automatically
produce recommendations supported by an ontological layer browsing. In our example,
we could easily imagine to recommend news concerning past governors by using the
richness of the semantic network in DBpedia.
For future work, we intend to improve the non-symbolic part of our approach.
This is a promising line of research especially with respect to the minimization of the
manpower efforts [Oliveira et al. 2014, Spalenza et al. 2019]. We also intend to deal with
a larger and more precise NER to improve the quality of the performance of our algo-
rithms [Collovini et al. 2019, Pirovani et al. 2019]. We also project to deal with other do-
mains like institutional repositories, educational materials and jobs offerings. Mentioning
this, one of the possible horizons in terms of potentially suitable products would be to
release a Graphical User Interface supporting the Semantic Web ideals [Bourguet 2017].
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