FROM LINGUISTICS TO ONTOLOGIES

The Role of Named Entities in the Conceptualisation Process

Nouha Omrane, Adeline Nazarenko and Sylvie Szulman

Laboratoire d’Informatique de Paris Nord, Universit

e Paris 13 & CNRS (UMR 7030)

99 av. J.-B. Clment, 93430 Villetnaeuse, France

Keywords:

Ontology design, Named entity, Conceptualisation.

Abstract:

Ontologies that have been built from texts can be associated with lexical information that is crucial for the

semantic annotation of texts and all semantic search tasks. However, the entire pocess of building ontologies

from texts cannot be fully automated and it is important to guide the knowledge engineer during the building

process. This paper presents an enriched version of TERMINAE, which is a text-based methodology for ontol-

ogy design. It combines a fact-based approach of modeling with the more traditional concept-centric one. We

show that named entities can be used to enrich an existing ontology and to bootstrap the acquisition process.

In other words, named entities are used for the conceptualisation of ontologies and not only for their popula-

tion. This approach is illustrated on two use-cases based on policy documents and evaluated by measuring the

Precision and Recall of the resulting ontologies with respect to pre-existing ontologies independently built by

domain experts.

1 INTRODUCTION

As deﬁned by (Studer et al., 1998), an ontology is a

formal, explicit speciﬁcation of a shared conceptual-

isation. Specialized texts are rich sources of infor-

mation and they are more widely available and ex-

ploitable than domain experts who often do not have

much time for interviews and are hardly conscious of

their own knowledge. Another advantage of building

ontologies from texts concerns the future exploitation

of the ontology. If the ontology is to be used to anno-

tate documents, for information retrieval or document

indexing, it is important to link documents to ontolo-

gies and this can be done by recycling the information

captured in text-based acquisition.

This paper shows how two types of domain spe-

ciﬁc textual units – terms and named entities, ex-

tracted from texts using natural language process-

ing (NLP) tools – can help the design of ontologies

and more speciﬁcally their conceptualisation. Be-

side methods relying on the terminological analysis

of texts for the building of ontological models (Termi-

nological Box, or T-Box, in Description Logic), many

works have tried to exploit named entity recognition

for the population of these ontologies (Assertional

Box, or A-Box) (Maynard et al., 2008). However, we

argue that named entities are also worth consider-

ing for conceptualisation and that taking these textual

entities into account yields to improve T-Boxes (here-

after ontologies).

The TERMINAE methodology (Aussenac-Gilles

et al., 2008) is enhanced by taking named entities into

account in addition to terms

. In this paper we fo-

cus on the creation of concepts and instances. Many

of our conclusions can be extended to the creation of

properties and instance properties, but this point is not

developed here.

The resulting methodology is illustrated in the

context of business rules and policy modeling, where

knowledge engineers have to design ontologies,

which are then used as conceptual vocabularies for

the annotation of source documents and the design of

business rules.

Section 2 presents the current approaches in text-

based ontology building. Although named entities

and terms are traditionally given distinct roles, Sec-

tion 3 explains that terms and named entities can be

exploited in a uniﬁed way and shows how the TER-

MINAE methodology has been enriched with the out-

put of named entity recognition tools. Section 4 illus-

trates the approach on two use-cases. The last section

The corresponding TERMINAE tool has been im-

proved accordingly (http://www-lipn.univ-paris13.fr/ szul-

man/logi) but we focus on the methodological aspects here.

249

Omrane N., Nazarenko A. and Szulman S..

FROM LINGUISTICS TO ONTOLOGIES - The Role of Named Entities in the Conceptualisation Process.

DOI: 10.5220/0003659602490254

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 249-254

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

evaluates our approach.

2 TEXT-BASED APPROACHES

TO ONTOLOGY BUILDING

Text-based ontology building methods differ on the

degree of automaticity they claim to achieve and in

the kind of textual elements they rely on (words, terms

and named entities

Towards Semi-automatic Methods. Distributional

approaches rely on the hypothesis that clustering

words on the basis of their contextual distribution

yields to semantic classes that can then be interpreted

as concept. This approach has proved to be more

reliable on specialized acquisition corpora and is of-

ten referred as ”ontology learning”, but it can hardly

be considered as automatic. In the OntoLp plug-in

of the Protege ontology editor, human intervention is

required for ﬁltering out words and words semantic

clusters (Lopes and Vieira, 2009). Text2Onto (Cimi-

ano and V

olker, 2005) exploits various NLP tools to

build a draft ontology but the resulting ontology needs

to be manually edited by the knowledge engineer af-

terwards (Wang et al., 2006). Systems like ASIUM

(Faure and N

edellec, 1999) or Doodle-OWL (Morita

et al., 2008) directly implement ontology design as an

incremental or interactive process. Even if word sim-

ilarities seem to be helpful in the modeling process,

word classes are seldom directly exploitable as onto-

logical concepts.

Terminological Approach. Among human-

centered approaches, the importance of domain

terminology for the building of domain speciﬁc

knowledge is acknowledged for years (Meyer et al.,

1992; Aussenac-Gilles et al., 1995) and TERMINAE

methodology relies on a terminological analysis.

Terminology is often deﬁned as the body of words or

terms relating to a particular subject, ﬁeld of activity

or branch of knowledge

. If we consider that each

domain of knowledge has its speciﬁc sublanguage(s),

the terms form the vocabulary associated to such a

sublanguage. A term is often a multi-lexical unit

rather than a simple word since compounds usually

have a more precise, more specialized and less

ambiguous meaning than words. Another important

property of terms is their relative stability. Even if a

term may have variant forms, this variability is lower

Much work has also been done for relation extraction,

but, as mentioned above, we do not focus on properties here.

Cf. Collins Dictionary.

than for plain words which can often substitute for

other words or phrases.

These domain speciﬁc and stable textual units are

useful for ontology building but termhood cannot be

deﬁned on purely linguistic or statistical ground. Lin-

guistic and surface clues help to restrict the set of can-

didate terms but the ﬁnal selection requires human ex-

pertise and a general understanding of the domain.

The Role of Named Entities. Named entities are

another type of domain speciﬁc textual units. They

have a referential meaning and refer to well identiﬁed

domain entities.

Although various types of mentions refer to de-

ﬁned entities (proper names as ”American Airlines”,

pronouns like ”it” and deﬁnite descriptions such as

”this company”) (LDC, 2004), we focus on proper

names that are easier to identify in documents.

Named entity recognition (NER) tools have been de-

signed to locate proper names in texts (persons, loca-

tions and organisations but also temporal expressions

or mesure units) and to associate a semantic type to

them (e.g. PERSON, LOCATION) (Nadeau and Sekine,

2007).

Named entities are traditionally exploited in ontol-

ogy engineering but for populating the instance level

of ontologies rather than for their conceptual struc-

turing. (Magnini et al., 2006; Giuliano and Gliozzo,

2008) try to (semi-)automatically enrich the knowl-

edge base (A-Box) associated to a given ontology by

discovering new concept instances. Recognising a

named entity of type T yields to the creation of an

instance of the concept T .

3 A COMBINED METHOD FOR

BUILDING ONTOLOGIES

FROM TEXTS

Assuming that texts do not contain all useful informa-

tion and that ontology design depends on the applica-

tion for which the ontology is to be used, TERMINAE

proposes an an interactive approach where the knowl-

edge engineer relies on domain speciﬁc textual units

to model an ontology (Section 3.1). Whereas terms

are traditionally used in conceptual design and named

entities for ontology population, we argue that both

types of textual units can be exploited for the concep-

tualisation (Section 3.2) and its bootstrapping (Sec-

tion 3.3). This approach is embodied in a new version

of TERMINAE tool (developed as an eclipse applica-

tion and available as a NeonToolkit plugin).

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

250

3.1 TERMINAE Layered Approach

The TERMINAE method decomposes the building pro-

cess into three main steps – the extraction, normali-

sation and formalisation (or ontological) steps. The

overall process is represented on Figure 1

. At each

level, the knowledge engineer has to select the rele-

vant items and organise them. This process is helped

by the previous terminological analysis of the text

which is automatic (step 1), and by the guidelines of

the method embodied in the interfaces of TERMINAE

(steps 2 and 3).

Acquisition

corpus

Terminological

relations

Termino-concepts

Termino-conceptual

relations

Conceptual

relations

Terminological

level

Termino-conceptuel

level

Conceptuel level

(ontological)

Concepts

Instances

Terms

Entity names

2 3

EXTRACTION

NORMALISATION

FORMALISATION

Figure 1: Abstracting a conceptual model out of text: the

TERMIANE layered approach.

At the terminological level, the knowledge engi-

neer identiﬁes the textual units of the acquisition cor-

pus that seem to be relevant for the domain and use-

case to model. This step relies on NLP tools known as

term extractors and named entity recognition tools

but the knowledge engineer has to rework manually

the results to rule out ill-formed units, ﬁlter out the ir-

relevant ones and cluster variants or synonyms under

a single canonical representative. Only a rough anal-

ysis is usually done at that level but the selection and

clustering go on during the next building step.

At the termino-conceptual level, the list of rel-

evant textual units is normalised into a network of

termino-concepts structured by the BT/NT (broader

than/ narrower than) links. Each group of synony-

mous terms must be clustered and associated to a

unique termino-concept. Ambiguous terms must be

disambiguated in such a way that each relevant mean-

ing is represented as a distinct termino-concept.

The third building step formalises the network of

termino-concepts into an ontology. Several important

questions regarding the concept/instance/property

We focus hereon the upper level of the ﬁgure.

In the reported experiments, YaTeA term ex-

tractor (http://search.cpan.org/ thhamon/Lingua-

YaTeA-0.5) and ANNIE NER tool

(http://gate.ac.uk/sale/tao/splitch6.html#chap:annie) have

been used.

distinction and the inheritance hierarchy must be tack-

led at the conceptual level.

In TERMINAE, the correspondence between the

levels is established through the creation of termi-

nological and termino-conceptual forms (see Sec-

tion 3.2). The termino-conceptual network can be ex-

ported as a SKOS ﬁle

). The conceptual level is re-

alized within Neon Toolkit Ontology editor

which is

plugged in our tool.

3.2 Named Entities Conceptualisation

The core task of ontology building is the conceptu-

alisation step that consists in choosing, structuring

and deﬁning the conceptual elements of the domain

model. The introduction of named entities at the ter-

minological level raises the question of their role in

the conceptualisation process, which mainly relies on

the knowledge engineer’s understanding of the do-

main and aimed application.

Our approach differs from the works on ontol-

ogy population which tend to automatically derive the

ontological types of conceptual elements (instances

vs. concepts) out of their linguistic type (named en-

tities vs. concepts). TERMINAE allows to link any

type of conceptual elements to any type of textual

unit through the termino-concepts (terms and named

entities to termino-concepts and termino-concepts to

concepts, instances or conceptual relations), as shown

on Figure 1. The distinction between the T-Box

and the A-Box levels does not exists at the termino-

conceptual level. The relevant linguistic units have to

be linked to termino-concepts or termino-conceptual

relations at the termino-conceptual level. The concep-

tualisation choices come after, in the transition from

the termino-conceptual level to the ontological one.

The ﬁrst conceptualisation step is handled in TER-

MINAE through the creation of terminological forms

for any textual unit that is considered as relevant for

the domain to model. A terminological form de-

scribes the properties of a term or named entity and

associates it to one or several termino-concepts, one

for each relevant meaning. Figure 2 presents the form

of Sapphire, the textual unit that had been selected

in the list on the left side

. The lexical information

indicates whether it has been extracted as a term, a

named entity or both. By browsing the list of its oc-

currences, the knowledge engineer decides whether

this unit is an important one or not (selection) and

how many of its meanings are relevant for the use-

http://www.w3.org/2004/02/skos/

http://neon-toolkit.org/wiki/Main Page

Sapphire is a speciﬁc category of travellers in the Air-

line use-case (see Section 4).

FROM LINGUISTICS TO ONTOLOGIES - The Role of Named Entities in the Conceptualisation Process

251

Figure 2: Terminological form of the named entity Sap-

phire.

case (ambiguity analysis). For each relevant mean-

ing, a termino-concept is created, the knowledge en-

gineer can give a natural language deﬁnition, asso-

ciate synonyms (clustering) and select the most repre-

sentative occurrences for the newly created termino-

concept. Figure 3 presents the termino-concept Sap-

phire. Sapphire and AAdvantage platinum are consid-

ered as synonyms.

Figure 3: Termino-concept Sapphire.

The case of named entities is speciﬁc c they are

associated to a semantic type by NER tools (PERSON

in the case of Sapphire). This semantic type may also

be relevant for the domain to model. As they have no

linguistic counterpart, semantic types are represented

directly at the termino-conceptual level with no oc-

currence attached to them even if they remain associ-

ated to their named entities as the fathers of the cor-

responding termino-concepts (see the hierarchical list

of termino-concepts on the left side of Figure 3). The

terminological form of the named entity Sapphire is

thus associated with the termino-concepts Sapphire

and Person, the latter being built from the semantic

type PERSON given by the NER tool.

3.3 Bootstraping Conceptualisation

Mining long lists of terms, named entities, termino-

concepts or concepts is difﬁcult. The broad-ﬁrst

search is not practicable as the terminological anal-

ysis cannot be completed without a view of the in-

tended ontology. Conceptualisation rather relies in a

deep-ﬁrst search strategy but the problem is to choose

the textual units and concepts to start with for boot-

strapping the conceptualisation process.

We propose here a fact-based approach. Starting

with the named entities that are expected to refer to

core domain speciﬁc entities leads to createx the con-

cepts the named entity belong to, the relations they

hold and more generally to model what is said about

them (in the same sentences or textual areas).

4 EXPERIMENTS

We consider two use-cases dealing with regulations

(loyalty program, EU directives). The resulting on-

tologies are to be used for the modeling and formal-

ization of the rules that are expressed in written poli-

cies. The ﬁnal rule bases and underlying ontologies

are intended to be used in Business Rule Management

Systems in the FP7 ONTORULE project. These ex-

periments show that the named entities are important

to take into account even in texts that do not contain a

lot of them .

The ontology building scenario differs in the two

experiments reported below. In the ﬁrst one, the

named entities are exploited to enrich an ontology that

we had previously built on the basis of terms only.

The second experiment shows how useful named en-

tities are for bootstrapping the conceptualisation.

4.1 Airline Use-case

In the ﬁrst experiment, the ontology is built out of a

document explaining the mileage policy of American

Airlines (AA) to customers (5 744 words).

Having built an initial ontology from of a list of

973 terms, we considered the 67 named entities iden-

tiﬁed in the corpus and some of them appeared as

highly relevant for the domain.

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

252

For instance, Central America refers to a spe-

ciﬁc type of airports that plays a speciﬁc role in

the attribution of miles. The underlying notion had

not showed up in our initial term analysis but the

named entity list has brought it to light and we ﬁ-

nally modelled it as a concept in the ontology (with

various airports as instances). The named entities re-

ferring to airline compagnies (e.g. Japan airways)

were modelled as instances of the concept AAdvan-

tage Airline Participant that we had already created

from the term AAdvantage participant (airline com-

pagnies participating in the “AAdvantage” program).

The semantic type ORGANIZATION lead us to cre-

ate a concept Organization as a father concept of

AAdvantage Airline Participant. The named entity

analysis also revealed the importance of Sapphire and

Ruby which had initially been considered as noisy

terms but actually refer to customer categories.

Detecting these named entities allowed us to better

understand the airline policy underlying domain and

lead us to revise the initial ontology. The ﬁnal one

contains 137 domain concepts.

4.2 Regulation Use-case

The Regulation use-case is based on EU directives

describing policies on seat belt control procedures

(3 704 words).

We started modeling the domain by considering

the named entities having PERSON, DATE, PERCENT

and UNKNOWN as semantic types

and by exploring

their contexts

as shown on Figure 4.

...

1st iteration

2nd iteration 3rd iteration

Figure 4: Exploration strategy. Starting with a given named

entity (NE

), the knowledge engineer analyses the surround-

ing terms (T

to T

) during a 1st iteration before further

exploring the list of terms during additional iterations.

Bootstrapping the conceptualisation starting with

the selected named entities (1st iteration) helped iden-

tifying relevant domain terms during the ﬁrst regula-

tion browsing. For instance, the names of the tests

A quick analysis showed that the named entities of

other semantic types (ORGANISATION and LOCATION)

were not relevant for the domain to model and the aimed

application.

The occurrence and the two surrounding sentences.

(e.g. Calibration Test) used to check the conformance

of seat belts are emphasised. Named entities of PER-

CENT type help the knowledge engineer to detect im-

portant properties of domain concepts (mainly test-

ing conditions). The UNKOWN named entities often

correspond to speciﬁc elements of the vocabulary of

safety belt procedures such as categories of vehicles

(M1, N1 ) or speciﬁc belt positions (Point A, Point C ).

In all, we created 53 domain concepts during the

bootstrapping step.

5 EVALUATION

We compare the ontologies built for each use-case

with those that were previously built by an expert

who did not start from texts (hereafter ”expert on-

tologies”). The comparison is based on the labels of

concepts or termino-concepts. We use Precision and

Recall

measures to evaluate the resulting ontologies

with respect to the pre-existing ones.

For each use-case, we created a draft ontology,

through a single iteration of the whole building pro-

cess. The goal is to evaluate the role of named enti-

ties either for enriching a term-based ontology (Air-

line use-case) or in the bootstrapping strategy (Regu-

lation use-case).

In the Airline use-case, we compare a ﬁrst ontol-

ogy built without taking named entities into account

and a revised version enriched with named entities

with respect to the expert one. There is no major dif-

ference between the ﬁrst and enriched versions of the

ontology (from 0.828 to 0.830 for Precision and from

0.675 to 0.720 for Recall ), due to the small number

of named entities. However, taking the named entities

into account has lead to re-structure the ﬁrst ontology

(11.5% of the existing concepts have been redeﬁned),

enrich it (the revised version is 40% larger) and pop-

ulate it (45 instances have been added). In all, 60% of

the named entities have been introduced in the ontol-

ogy in a way or another.

The Regulation use-case presents a high Precision

(0.742) but a low Recall (0.393): the ontology con-

tains mainly relevant domain concepts but only par-

tially covers the domain described by the expert. The

concepts that are missing in our ontology belong ei-

ther to the core level of the ontology or to speciﬁc

sub-types of existing concepts (e.g. categories of ve-

hicles). The former ones are concepts that are not di-

rectly expressed in texts. Detecting the latter would

P =

number of relevant termino-concepts RTC

number of termino-concepts TC

R =

number of relevant termino-concepts RTC

number of concepts in the expert ontology C

FROM LINGUISTICS TO ONTOLOGIES - The Role of Named Entities in the Conceptualisation Process

253

require a ﬁner-grained analysis and additional itera-

tions of the conceptualisation process, but most of the

related terms occur in the contexts of the mentions of

existing concepts.

Both use-cases show that named entities help the

detection of relevant domain concepts. Since the list

of named entities extracted is much smaller than the

list of terms (more than 10 times smaller in our use-

cases), it is interesting to rely on named entities as

a starting step, even if the list of textual units must

then be further explored to enrich the draft ontologies

based on named entities.

6 CONCLUSIONS

This paper shows how text-based ontology building

methods can be enriched by taking speciﬁc textual

units into account – named entities as well as terms

– and explains how named entities can be used in the

conceptualisation task. We show that they can be used

either to enrich an ontology (building new concepts,

their properties, re-structuring and populating exist-

ing concepts) or for bootstrapping the conceptualisa-

tion step and identifying relevant domain terms.

This combined approach, which is implemented

in the new TERMINAE tool, is illustrated on two use-

cases. Even if named entities are not as numerous in

policies as in press articles for instance, they are im-

portant to take into account in the conceptualisation

process because they point out critical domain ele-

ments that are important to integrate in a conceptual

model.

ACKNOWLEDGEMENTS

This work was realised as part of the FP7 231875 ON-

TORULE project (http://ontorule-project.eu). We are

grateful to American Airline who is the owner of one

of our working corpora.

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