ON THE EXTENSION OF ARABIC WORDNET NAMED

ENTITIES AND ITS IMPACT ON QUESTION / ANSWERING

Lahsen Abouenour, Karim Bouzoubaa

Mohammadia School of Engineers, Med V Universit-Agdal, Agdal, Rabat, Morocco

Paolo Rosso

Natural Language Engineering Lab., ELiRF, Dpto. Sistemas Informáticos y Computación

Universidad Politécnica de Valencia, Valencia, Spain

Keywords: Arabic WordNet ontology, Question/Answering Systems, YAGO ontology, Named Entities, Semantic

Query Expansion, Passage Retrieval.

Abstract: Applying a semantic approach has improved performances in the context of the Arabic Question/Answering

(Q/A) systems. This approach is based on the current release of the Arabic WordNet (AWN) ontology. The

analysis of the results obtained in previous works has shown that extending the Named Entity (NE) content

of this ontology is a promising technique in order to further improve performances. In this paper, we

investigate through experimental results the impact of the AWN enrichment using the YAGO ontology

which presents a large coverage in terms of NE.

1 INTRODUCTION

As the construction of ontologies can be done using

Natural Language Processing (NLP) tools (Charlet

et al., 2009), there are also works related to NLP

where ontologies are used as semantic resources.

Indeed, researchers in the fields of NLP have

devoted more interest in integrating ontologies in

their works especially those belonging to particular

domains such as Information Retrieval (IR),

Information Extraction (IE), Machine Translation

(MT) and Question/Aswering (Q/A) (Buitelaar and

Simiano, 2006).

The usefulness of ontologies in NLP should be

evaluated with respect to a given task, resource

and/or language. In IR and Q/A systems, ontologies

are used mainly as resources for Query Expansion

(QE) process (Voorhees, 1994), question analysis

and answer extraction modules (Lopez and al.,

2009). This is due to the advantages presented by

ontologies and knowledge bases in terms of

conceptualization and semantic information.

In the context of the Arabic language, the Arabic

WordNet (AWN) ontology (Elkateb et al., 2006)

remains among the few available resources which

can be integrated in order to perform lexical and

semantic processing. In recent works (Abouenour et

al., 2009b), we have shown by experiments that

integrating a QE process (Abouenour et al., 2008)

based on the AWN ontology within an Arabic Q/A

module improves the relevance of returned passages.

Moreover, we have shown through a detailed

example that performances can be enhanced even

more by considering a similarity score calculated on

the basis of semantic relations in the AWN ontology

and Conceptual Graphs

(CG) representations.

However, this promising approach could not be

applied to a high number of the considered TREC

and CLEF

questions due to the low coverage of the

AWN ontology. Hence, despite its high accuracy

which is a feature of manually built resources, this

ontology has to be enriched in order to benefit from

its advantages in NLP works.

In (Abouenour et al., 2010a), we have analyzed

the questions for which the proposed semantic

A Conceptual Graph is a directed graph of nodes that

correspond to concepts, connected by labelled and oriented

arcs that represent conceptual relations (Sowa, 1984).

Text REtrieval Conference, http://trec.nist.gov/data/qa.html

Cross Language Evaluation Forum, http://www.clef-

campaign.org

424

Abouenour L., Bouzoubaa K. and Rosso P..

ON THE EXTENSION OF ARABIC WORDNET NAMED ENTITIES AND ITS IMPACT ON QUESTION / ANSWERING.

DOI: 10.5220/0003102004240429

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2010), pages 424-429

ISBN: 978-989-8425-29-4

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

approach could not be applied. This analysis showed

that a high number of these questions are formed of

Named Entities (NEs) or are expecting NEs (such as

persons and organization names) as answers.

Therefore, in that work, two tasks have been done on

the basis of this analysis:

• extending the NE content in AWN which is

related to the questions that could not be

processed using the proposed semantic

approach;

• conducting preliminary experiments in order

to evaluate performances after this

enrichment.

The first task has been performed by translating

a small part of the YAGO

ontology content

(Suchanek et al., 2007) to the Arabic language and

by mapping this content to the AWN ontology

synsets. At the end of this task, we were able to

apply the semantic approach on the non processed

questions (547 in number). The number of YAGO

entities concerned in this step is negligible (374 out

of 3 millions). Although AWN has been enriched

with a very small number of NEs, preliminary

experiments have shown that performances in terms

of accuracy, MRR and the number of answered

questions have been improved by 6.04%, 1,61 and

8,22% respectively.

In this paper, we conduct experiments in order to

evaluate the impact of enriching the NE content of

AWN using all available data of the YAGO

ontology. Therefore, instead of considering only

YAGO's entities and facts related to the non

processed questions, we translate, firstly, all the

content of this ontology and perform, secondly, a

mapping between YAGO entities and AWN synsets.

The rest of the paper is structured as follows:

Section 2 describes the features of the AWN

ontology, its use in the context of Arabic NLP and

previous works aiming its extension; Section 3 is

devoted to the description of our technique using the

YAGO ontology. The results of the experiments that

we have conducted are presented and discussed in

Section 4 and 5 respectively. Finally, in Section 6,

we draw the main conclusions of the current work.

2 FEATURES AND AUTOMATIC

ENRICHMENT OF AWN

Recent years have seen two significant facts: first we

Yet Another Great Ontology, available at http://www.mpi-

inf.mpg.de/yago-naga/yago/downloads.html

note an extension of information content particularly

those produced by blogs and wikis. Moreover, with

respect to linguistic resources that can be used in

NLP, there is huge need of electronic dictionaries,

lexical databases, ontologies, etc. This need is due to

the fact that manually built resources with a high

coverage are not freely available.

For languages such as English, there are many

interesting open source ontologies which belong

either to specific and open domain categories:

OpenCyc (Matuszek et al., 2006), Know-ItAll

(Etzioni et al., 2004), HowNet

, SNOMED

GeneOntology

, etc. To our knowledge, AWN

ontology is the only freely available ontology in the

context of the Arabic language. This lexical

ontology is composed of 23,000 Arabic words and

10,000 thousands of synsets (sets of synonyms).

The interest devoted to AWN is due mainly to its

compliancy and mapping with WordNets of other

languages such as English (Fellbaum, 2000). The

development of these WordNets is the subject of a

periodical conference

. The AWN ontology is also

connected to the Suggested Upper Model Ontology

(SUMO) (Niles & Pease, 2003). AWN has been

used in many Arabic NLP works such as Q/A

systems (Brini et al., 2009), Arabic Named Entity

Recognition (Benajiba et al., 2009), QE processes

(El Amine, 2009), etc. These works showed that the

techniques based on AWN are promising in the

considered fields but the coverage of this ontology

has to be improved in order to get higher

performances.

A way to tackle this kind of problem is to use

existing information content in order to improve the

AWN coverage. Authors of (Al Khalifa and

Rodriguez, 2009) have shown that using the Arabic

Wikipedia

it is possible to enrich NEs in AWN. The

evaluation done in that work shows that 93.3% of

the NE synsets which was automatically recovered

are correct. However, due to the small size of the

Arabic Wikipedia, only 3,854 Arabic NEs have been

recovered.

In (Abouenour et al., 2010a), we have proposed

another technique aiming to enrich the NEs content

in AWN. This technique relies on the YAGO

ontology. Indeed, many advantages are presented by

using YAGO such as its high coverage of NEs (3

millions), its accuracy of around 95%, its mapping

www.keenage.com/html/e_index.html

www.snomed.org

www.geneontology.org

The last edition of the Global WordNet conference was held in

February 2010 in Mumbai, India.

www.wikipedia.org/

ON THE EXTENSION OF ARABIC WORDNET NAMED ENTITIES AND ITS IMPACT ON QUESTION /

ANSWERING

425

with WordNet, its connection with the SUMO

ontology, its presentation in many standard formats

such as RDF, etc (Suchanek et al., 2007). Let us

recall briefly that the YAGO ontology is divided

into two types of information: entities and facts. The

former are NEs while the latter are relations between

these NEs. This ontology has been recently used in

many NLP works such as IR systems (Pound et al.,

2009), automatic categories generation (Anjian et

al., 2009), knowledge base building (Wang et al.,

2010), etc.

In the next section, we present the technique

used for automatically extending the NE content in

YAGO.

3 EXTENTION TECHNIQUE

USING YAGO

Identifying NEs within the Arabic Wikipedia

content is one of the challenges reported in the work

of (Al Khalifa and Rodriguez, 2009). The use of

YAGO is, therefore, helpful in that NEs are already

identified and checked in this ontology.

Figure 1 illustrates the different steps performed

in order to enrich AWN entries based on YAGO

entities.

As shown in Figure 1, the first step concerns the

translation of all YAGO entities from English into

the Arabic language. This translation is performed

automatically using the Google Translation API

(GTA). Generally, machine translation is more

accurate in the case of NEs as they are formed of

very few words.

The translated YAGO entities have been added

in AWN according to two types of mappings as

follows:

(i) The WordNet synsets corresponding to the

YAGO entity are identified using the “TYPE”

relation in the YAGO facts. After that, the AWN

synsets corresponding to the identified WordNet

synsets are connected with the given entity;

(ii) A mapping is performed between YAGO

relations and AWN synsets. Table 1 shows some

examples

. Based on this table, we extract the facts

related to the mentioned YAGO relations. The

second argument of the fact is candidate to be an

instance of the corresponding AWN synset. In the

We use the Buckwalter transliteration schema to show

romanized Arabic. This schema is widely used by the NLP

community.

Figure 1: AWN ontology enrichment steps using YAGO.

case of the “isMarriedTo” both the first and the

second arguments are considered. For the

“inTimeZone” and “hasUnemployment” relations,

we consider the first argument.

In order to validate the final connections

between YAGO entities (translated into Arabic) and

the AWN synsets, we have eliminated some

particular entities. Indeed, for each candidate entity,

we query the Web using the exact expression formed

from the synset and the candidate entity. If no

document matches this query, we add the entity to a

list for manual validation. For example, the YAGO

entity “Wendy_Hall” appears in the following fact:

id: 401543569

relation: isLeaderOf

argument 1: Wendy_Hall

argument 2: Association_for_Computing_Machinery

According to Table 1, the “Association for

Computing Machinery” (  ) is

candidate to be added to the AWN hierarchy under

the “country” node as an instance. Using the Yahoo

API, we search the content which matches the exact

expression “     ” (Association for

Computing Machinery country).

Since there is no such content, we add this

YAGO entity to the list of validation instead of

adding it directly in the AWN ontology. This

technique allows also eliminating some YAGO

entities that have not been correctly translated. Table

2 shows the statistics after applying this technique.

YAGO

Entities

Arabic

YAGO Entities

Goo

le Translation API

Direct mapping

through WordNet

YAGO relation

mapping

Enriched

AWN

KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development

426

Table 1: Mapping between YAGO relation and AWN

synsets.

YAGO relation AWN synset AWN synset id

actedIn



(creation : AibodaAE)

madiynap_n1AR

bornIn  (city : mdynp) madiynap_n1AR

diedIn  (city : mdynp) madiynap_n1AR

hasCapital  (city : mdynp) madiynap_n1AR

hasCurrency  (country : balad) balad_n1AR

hasNumberOfPeople  (country : balad) balad_n1AR

hasPopulation  (country : balad) balad_n1AR

hasPopulationDensity  (country : balad) balad_n1AR

hasUnemployment  (country : balad) balad_n1AR

inTimeZone

-

(region : mnTqp)

minoTaqap_n1AR

isCitizenOf  (city : mdynp) madiynap_n1AR

isLeaderOf  (country : balad) balad_n1AR

isMarriedTo

 -

(married : zwj)

zawoj_n1AR

livesIn  (city : mdynp) madiynap_n1AR

locatedIn  (city : mdynp) madiynap_n1AR

originatesFrom



(region : mnTqp)

minoTaqap_n1AR

oliticianOf  (country : balad) balad_n1AR

worksAt



















(institution/establishm

ent : mu&as~asap)

mu&as~asap_n1A

wrote



(writer/author : kAtb)

kaAtib_n1AR

Table 2: YAGO and AWN evident mapping statistics.

YAGO relation # entities Eliminated entities

actedIn 28,836 35.09%

bornIn 36,189 20.59%

diedIn 13,618 12.92%

hasCapital 1,368 6.78%

hasCurrency 367 0.00%

hasNumberOfPeople 6,171 0.00%

hasPopulation 77,928 9.78%

hasPopulationDensity 44,628 0.00%

hasUnemployment 41 0.00%

inTimeZone 2 0.00%

isCitizenOf 4,865 0.00%

isLeaderOf 2,886 0.00%

isMarriedTo 8,416 0.00%

livesIn 14,710 11.11%

locatedIn 60,261 14.03%

originatesFrom 11,497 26.67%

politicianOf 6,198 0.00%

worksAt 1,401 3.45%

wrote 12,469 27.27%

As we can see, the three YAGO relations

“wrote”, “originatesFrom” and “bornIn” are the

most concerned by the elimination of the entities to

be added in AWN. In the case of the first relation,

this is due to translation errors. For the second and

third relations, the eliminated entities belong to

cities or countries when we expect a region and to

countries when we expect cities.

The number of entities that can be added to the

AWN ontology using automatic mappings is

288,204. This is a very high number compared to the

number of NEs in the current release of AWN (546).

Note that we have not considered other YAGO

entities due to several reasons (redundant entities,

incorrect translation, etc.). As previously shown

(Abouenour et al., 2010a), we expect to further

improve performances with this large number of

NEs. In the next section, we present the

experimental results related to the evaluation that we

have done in order to investigate this assumption.

4 EXPERIMENTAL RESULTS

In order to measure the impact of the automatic

enrichment presented in the previous section, we

have considered all the 2,264 TREC and CLEF

questions translated into Arabic.

These experiments have been conducted in the

same way as in (Abouenour et al., 2010a). Indeed,

the semantic QE using AWN and the structure-based

PR using the Java Information Retrieval System

(JIRS) (Gomez et al., 2007) are applied together on

this set of questions. Accuracy, Mean Reciprocal

Rank (MRR) and the number of answered questions

have been used in order to measure the performance

(Abouenour et al., 2010b). Table 3 shows the

obtained results before and after using the enriched

release of AWN.

Table 3: Results before and after AWN enrichment.

Measures before YAGO using YAGO

Accuracy 17.49% 25.22%

MRR 7.98 14.78

umber of answered

questions

23.15% 35.05%

As we can see thanks to the enrichment of AWN

using the YAGO ontology, performances have been

significantly improved in terms of Accuracy, MRR

and the number of answered questions by 7.73%,

6.80 and 11.90% respectively.

5 DISCUSSION

The enriched content of the AWN ontology allowed

http://sourceforge.net/projects/jirs

ON THE EXTENSION OF ARABIC WORDNET NAMED ENTITIES AND ITS IMPACT ON QUESTION /

ANSWERING

427

us to apply the keyword-based and the structure-

based approach on the TREC and CLEF questions

which could not be processed previously. Moreover,

performances have been enhanced even more

according to the considered measures.

Improving the NE coverage of the AWN

ontology had a positive impact on the effectiveness

of the considered approach. These results confirm

those obtained in preliminary experiments

(Abouenour et al., 2010a) where only a restricted

part of YAGO has been used. In that work, the

accuracy has been improved from 17.49% to 23.53%

(now it is 25.22%), the MRR from 7.98 to 9.59 (now

it is 14.78) and the number of answered questions

from 23.15% to 31.37% (now it is 35.05%).

These performances are due to the high coverage

of YAGO in terms of names of Locations, Time and

Persons existing in the considered questions

(respectively 43%, 55% and 32.56%).

6 CONCLUSIONS

In this paper, we have shown through experiments

that extending the coverage of NE in the AWN

ontology using the YAGO ontology has a positive

impact in the context of the Arabic Q/A systems.

Indeed, applying a semantic QE approach based on

the enriched content of AWN on a set of 2,264

TREC and CLEF questions allowed to reach better

performances in terms of accuracy, MRR and the

number of answered questions.

As future work, we plan to investigate the impact

of enriching other main facets of AWN such as

nouns and verbs using specialized ontologies and/or

lexicons.

ACKNOWLEDGEMENTS

This research work is the result of the collaboration

in the framework of the bilateral Spain-Morocco

AECID-PCI C/026728/09 research project. The third

author thanks also the MICINN research project

TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03

(Plan I+D+i).

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