J. Software Engineering & Applications, 2010, 3, 1047-1053
doi:10.4236/jsea.2010.311123 Published Online November 2010 (http://www.SciRP.org/journal/jsea)
Semantic Analysis of Natural Language Queries
for an Object Oriented Database
Bentamar Hemerelain1, Hafida Belbachir2
1Oran University, Wilaya of Oran, Algeria; 2University of Science and Technologie of Oran (USTO), Wilaya of Oran, Algeria.
Received September 3rd, 2010; revised October 15th, 2010; accepted October 19th, 2010.
ABSTRACT
This paper presents the semantic analysis of queries written in natural language (French) and dedicated to the object
oriented data bases. The studied queries include one or two nominal groups (NG) articulating around a verb. A NG
consists of one or several keywords (application dependent noun or value). Simple seman tic filters are defined for iden-
tifying these keyword s which can be of semantic value: class, simple a ttribute, composed a ttribute, key value or not key
value. Coherence rules and coherence constraints are introduced, to check the validity of the co-occurrence of two
consecutive nouns in complex NG. If a query is constituted of a single NG, no fu rther analysis is required. Otherwise, if
a query covers two valid NG, it is a subject of studying the semantic coherence of the verb and both NG which are at-
tached to it.
Keywords: Query, Nominal Group, Natural Language, Object Oriented Data Ba se, Semantic Validation
1. Introduction
On the earliest and most widely studied areas of natural
language processing is the development of a Natural
language interfaces to databases (NLIDB) [1-7]... A
NLIDB allows users to input a query in a natural lan-
guage such rather than in a formal query language and in
conceptual terms particular to their understanding of the
database application domain. In most works, the underly-
ing database is assu med to be relational. In our work, we
are interested in building a NLI for an object-oriented
database (OODB). Unfortunately, the relational model
and the object model are fundamentally different. Rela-
tional database (RDB) systems are based on two-
dimensional tables in which each item appears as a row.
Relationships among the data are expressed by compar-
ing the values stored in theses tables. The object model is
based on the tight integration of code and data, flexible
data types, hierarchical relationships among data types,
and references [8].
This paper is concerned with the semantic validation
of natural language query (NLQ) for an OODB. The
natural language co nsidered here is French.
A considerable amount of work has been published on
the semantics of NLQ. Works, usually carried out by
Computer Scientists, can be classified into two catego-
ries: 1) work based on established linguistic theories
which are modified and/or extended such as [9-12]; 2)
new ad hoc but efficient semantics that are not based on
established linguistic theories. Examples are quoted by
[2]. Some recent works are [13-15].
Our approach falls into category 2) and is designed on
the principle of [13] who introduce the idea of semanti-
cally tractable (ST) questions. As their database is in the
form of RDB, they define the ST questions as questions
where the words correspond to relations (tables), attrib-
utes and values. The nature of the OODB introduces
some new challenges, beyond these addresses for RDB.
In this paper, we deal with the concept of class to model
complex data, simple attributes, complex (composite)
attributes with classes as their domains, and two special
kinds of associations between classes: 1) inheritance
which is the most powerful con cept of object oriented. It
is a mechanism of reusability: the news classes, known
as sub-classes inherit attributes of the pre-existing classes,
which are referred to as super classes; an inheritance
relationship is created between the sub-class and the
super-class. 2) reference: since a class C may have a
complex attribute with domain C’, a reference relation-
ship can be established between C and C’.
Our center of interest is the analysis of NLQ consist-
ing of one or two NGs articulating around a verb. At this

Semantic Analysis of Natural Language Queries for an Object Oriented Database 1048
step of analysis, we consider the NG constituted of one
or several keywords and we ignore the DB independent
words (article, relative pronoun,...). A keyword can be an
application dependent noun or a value. The former
matches a class name, a simple attribute name or a com-
posite attribute name in an OODB.
In a NLQ, the verb is created by the DB administrator.
It expresses the link which exists between a class and
another class or between a class and one of its attributes.
The application dependent nouns and the verbs with
their semantic features are stored in a lexicon. This last
includes inflection forms of nouns and conjugated forms
of verbs. It is also augmented with relevant synonyms.
This addition makes it possible to the user to write a
query in various natural ways.
Heritage and reference relationships are added to lexi-
con allowing the safeguarding of the heritage and refer-
ence link of the OODB. These Heritage and reference
relationships with the lexicon form the natural language
database (NLDB).
The semantic analysis of a query first deals with iden-
tification of individual keywords in the NGs. It uses
some simple semantic filters to determine their semantic
value. If the NG is atomic and the single keyword identi-
fied, no further analysis is required. On the co ntrast, it is
necessary to verify if the complex NG respects the con-
ceptual constraints of the domain DB. The conceptual
constraints mainly refer to inheritance and reference
relationships and are function of the semantic value of
the keywords and their combination in the NG. If the
NLQ is constituted of a single NG, no supplementary
treatment is necessary. If the NLQ covered two valid
NGs, it is a subject of studying the semantic coherence
of the verb and both NGs which are attached to him.
The paper is organized as follows: The Section 2 de-
scribes the lexicon and defines the inheritance and refer-
ence relationships in a NLDB. The Section 3 is divided
into three parts. In the part 1, semantic filters are defined
to identify keywords. With the introduction of the se-
mantic coherence constraints of keywords, the part 2
discusses the process of NG’s validation. Part 3 concerns
the semantic coherence of the verb with its subject and
its object. In Section 4, an example of illustration is
treated to simulate the tools developed in preceding
section. To understand this example and those that are
quoted in the text; we give an extract of the database
prototype which is used in our experimentation. The
results of the experimentation are reported in Section 5.
A conclusion is given in Section 6.
2. Natural Language Database
The natural language database (NLDB) is a translation of
the object oriented database (OODB). The NLDB con-
tains a lexicon and inheritance and reference relation-
ships.
2.1. Lexicon
A natural language query consists of two parts: an inter-
rogative particle and a phrase. A phrase is a succession
of nominal and verbal groups. A nominal group (NG) in
the natural language query (NLQ) consists of one or
several meaningful words. The latter can be an applica-
tion depende nt noun, a verb or a value .
2.1.1. Defi ni ti ons
Application dependent noun
The former matches a class name, a simple attribute or a
composite attribute name in an OODB.
Each application dependent noun is represented in the
OODB as a variable whose name might be meaningful
according to the naming conventions but not necessarily
the same word in natural language (NL) (e.g. ENS, X).
Thus, we generate, for each variable declared in an
OODB, a word in NL (NW) (e.g. NW (ENS)  enseig-
nant [teacher]; NW(X)  personne [person]).
Due to this, to each variable declared in the database,
corresponds, a word family. It includes all of the inflec-
tion forms of the word (masculine singular (ms), mascu-
line plural (mp), feminine singular (fs),…) (e.g. enseig-
nant [teacher] {enseignant (ms) [teacher], enseignants
(mp) [teachers], enseignante (fs) [teacher], enseignantes
(fp) [teachers]} and the set of its synonyms.
(e.g. enseignant  {maitre,…} [teacher  {school-
master,…}]).
Verb
The verb of the query expresses the link between two
classes or a class and one of its attribute. Then, the verb
is transitive. To each created verb , correspond s co njug ate
forms (3rd person of the singular (s), 3rd person of the
plural (p)) (e.g. habiter [to live]  {habite (s) [lives],
habitant (p) [live]} and the set of its synonyms (e.g.
(habiter)  {résider, demeurer,...} [to live  {to reside,
to dwell,…}]).
All the nouns and verbs usually depend heavily on the
application. They constitute the lexicon.
The initial set of synonyms is created with electronic
dictionaries such that Wolf and Crisco and then proposed
to expert individual who can modify it to reflect the
specific senses of the DB application domain.
The sibling relation that exists between the words of
the same family permits the database requester to use
any of them without affecting the meaning of the query.
Thus, a family of words is represented by a single term
called the stem which can be the first generated word in
natural language.
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Semantic Analysis of Natural Language Queries for an Object Oriented Database 1049
Value
On the other hand , a value in the query is an instance of a
simple attribute. It is identified via a dialogue with the
database requester. If an attribute is designated by the
DB administrator as key then its value is said to be key
and non-key otherwise. A key value can specify a class.
For example, in the query “what is the salary of Linda”,
Linda specifies the class “Employee” then it is a key
value. However, in the query “what is the name of the
module which has the coefficient 4”, the value ‘4’ is not
key value because it specifies no class.
2.1.2. Descri ption
Application dependent noun
Depending on the relation of the word to the database
being handled, an application dependent noun is de-
scribed, in the lexicon, by its stem and its semantic valu e
which can be ‘C’ if it is about a class, ‘SA’ of a simple
attribute or ‘CA’ of a composite attribute.
In the description of a simple attribute, we add the
stem of the class containing it and in the case of a com-
posite attribute, the stem of the referenced class by the
composite attribute. For ex ample, the composite attribute
‘matières’ [matters] is described by its stem ‘matière’
[matter], its semantic value ‘AC’ and the stem of its
referenced class ‘module’ [module].
Verb
A verb is described by its subject which is the class that
contains it and its object which is either a declared at-
tribute in the subject class or a referenced class by a
composite attribute of the subject class. It is formally
described by its stem, the stem of the subject entity and
the stem of the object entity. For example, the verb
‘habiter’ [to live] is described by its stem ‘habiter’ [to
live], the stem of the subject entity ‘personne’ [person]
and the stem of the object entity ‘ville’ [town].
2.2. Inheritance Relationship
The inheritance relationship in the NLDB is an extens ion
of the traditional inheritance relationship ‘HERITE’ in
an OODB and is given by the relation Herite*. It allows
handling the NLQ on a given class using the properties
of the class itself or those of its super-classes and sub-
classes.
The extended inheritance relationship allows moving
in both directions up and down the hierarchy of the
classes.
The direct inheritance relationship ‘herite’ is defined
as follows:
Let CLASSE be classes stems of a NLDB.
herite: CLASSExCLASSE  0,1}
(x,y)  herite (x,y)

herite (x, y): = 1 if NW-1 (x) HERITE NW-1 (y)/*
NW(v) = x/‘v’ is a variable in the OODB and ‘x’ is the
word corresponding in natural language */
herite (x, y):= 0 otherwise;
}
The relation Herite* is defined as fo llows:
Herite*: CLASSExCLASSE  0,1}
(x,y)  Herite* (x,y)

Herite*: = 1
if (x = y) or (x  y and (herite (x, y)
or herite (y, x))
or (x  y and  C1, C2,... Cn  CLASSE/
herite (x, C1)
and (i  [1, n–1] herite (Ci, Ci+1))
and herite (Cn, y)
or (x  y and  C1, C2,... et Cn  CLASSE/
herite (y, Cn)
and (i  [1, n–1] herite (Ci+1, Ci)
and herite (C1, x))
Herite*: = 0 otherwise;
}
2.3. Reference Relationship
Let M1 be a class, M2 its composite attribute, M3 the
referenced class and R1,2 a relation connecting M1 and
M2.
The relation R1,2 is also applicab le b etween the class of
definition M1 and the referenced class M3. It represents
a direct reference link between these two classes. This
relation is called the reference relationship in a NLDB. It
is an extension of the tradition al reference relationship in
an OODB. It allows ge nerat i n g the ve r bs of the NL DB .
We notice that the existence of a reference relationship,
does not base itself on a criterion accord ing to th ese link s,
but that it is random, thus the solutions in that case relay-
ing on a linguistic analysis which indicates for all the
roads of (direct) reference, the existence or the non-
existence of a verb of relation.
They translate direct reference links between the
classes. In that case, the existence of the verb can create
a indirect reference link (e.g. travailler (chercheur, projet)
[to work (searcher, project)] and appartenir (projet, labo-
ratoire) [to belong (project, Laboratory)]  travailler
(chercheur, laboratoire) [to work (searcher, Laboratory)]).
NOTE. - The deducted relations can be calculated by
combining the reference links and the links of inheri-
tance (e.g. assurer (enseignant, module) [to ensure
(teacher, module)] and Herite* (enseignant, personne)
[Herite* (teacher, person)]  assurer (personne, mod-
ule)).
Let Ref be an extension of the defined reference rela-
tionship previously. This relation is defined as follows:

Semantic Analysis of Natural Language Queries for an Object Oriented Database 1050
Let ‘ac’ be a composite attribute.
Ref: CLASSExCLASSE  0,1}
(x, y)  Ref (x, y)

Ref (x, y): = 1 if/* exists ‘ac’/‘x’ is the class
where ‘ac’ is defined or its sub or super-class and the
class ‘y’ is the domain of ‘ac’ or a indirect reference link
between th e classes ‘x’ and ‘y’ */
Ref (x, y): = 0 else;
}
3. Query Semantic Validation
A NLQ consists of one or two NGs articulating around a
verb. We consider a NG constituted by one or several
keywords. A keyword is an application dependent noun
or a value. So, the process of the validation of the query
consists in:
 keyword’s identification in the NG; In the case of a
NG formed by an identified single keyword, no sup-
plementary treatment is necessary since only one
component is used: the NG is valid. If the NG is con-
stituted by more than a keyword and that one of them
is not identified, the NG is considered as erroneous.
Should the opposite occur, it is necessary to carry out
the NG’s semantic validation.
 If the query is constituted by a valid NG, no furthe r-
analysis is required. If the query covered two valid
NGs with an identified verb, it is a su bject of studying
the semantic coherence of the verb with its arguments.
3.1. Keyword’s Identification
The first step of the analysis is to determine if a word of
the query is a keyword. For this, we define a simple
semantic filter denoted SF<w> as the set of triplets {(*, *,
)} associated with the semantic value . The first star
in the triplet represents the lexical information while the
second designates the syntactic information associated
with the word.
For an application dependent noun, a semantic filter is
defined by:
SFN = {(*, Noun, )/  {C (class), SA (simple
attribute), CA (composite attribute)}} where the star
represents the w o r d’s stem .
For instance, to represent the word ‘students’ that
matches the class ‘Student’; we would have the semantic
filter SFN<C> including the triplet (student, Noun, C).
Likewise, the semantic filter, for a value, has the same
structure except that the category  takes values from the
set:
 = {VK (Value_as_Key), VNK (Value_None_Key)}
and the tagging info rmation is the value in the first case,
hence SFV<> = {(*, value, )}. For The number ‘4’ in
the NG “coefficient 4”, we have the triplet (4, value,
VNK).
From the query “what is the salary of the teacher
Linda”, salary  SFNSA, teacher  SFNC and
Linda  SFVVK then all words are semantically cor-
rect.
The process of identification of a keyword ‘mi’ con-
sists mainly in determining the corresponding simple
semantic filter. It is defined by the following function:
Function Ident (mi): boolean

ident: = 1 if\exist\{C, SA, CA, VK, VNK}/
(mi, *, *)  SFN  SFV
ident:= 0 otherwise;
}
3.2. NG’s Semantic Validation
In the case of a NG formed by several identified key-
words, it is necessary to verify if the complex NG re-
spects the conceptual constraints (semantic coherence
constraints) of the domain database which amounts to
associate semantic relations between every two consecu-
tive keywords in the NG. These relations mainly refer to
inheritance or reference relationships. They are based on
the semantic values of the keywords and their combina-
tion in the NG.
For example, the NG ‘salaire de l’enseignant’ [salary
of the teacher] is valid because the class ’enseignant’
uses the attribute ‘salaire’ of its super class ‘employé’
[employee]. Thus, at the combination ‘simple attribute-
class’ (SA-C), we must have an inheritance relationship
between the class that contains the attribute (i.e. ‘em-
ployé’) and the specified class in the NG (i.e. ‘enseig-
nant’).
Before giving the coherence constraints, we need to
introduce some notations and notions:
 A part of a NG constituted by the succession of two
keywords x an d y is noted x – y.
 if (m, *, *)  FSX (w)/w  {SA, C, CA, VK, VNK}
and x  {N, V}, the keyword ‘ m’ is resp ectiv ely no ted
SA’, C’, CA’, VK’, VNK’.
 A class can be manipulated through its name, the
name of the composite attribute which references it or
a key value which is the v alue of its key attribute (e.g.
‘module’, ‘matter’ and ‘Compilation’ specify the same
class ‘module’). For that, 'class’ will indicate these
three information.
 In what follows we take:
Xi: = Classe (mi) if mi = C’
Xi: = Class-ref (mi) if mi = CA’
Xi: = Class (Attrib (mi)) and Yi: = Attrib (mi)
if mi  {SA’, VK ’, VNK’}
where Classe is a function which returns, for a class,
thes tem of th is one and for a simp le at tribute that of the
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Semantic Analysis of Natural Language Queries for an Object Oriented Database 1051
class where it is defined. Attrib gets, for a simple at-
tribute or for a value, the stem of this attribute and
Class-ref, for a composite attribute, the stem of its
referenced class.
3.2.1. Semantic Coherence Constraints
The combination (COMB) of two keywords according to
their semantic value is governed by a set of rules called
coherence rules (CR). Based on the patterns of combina-
tion, we have formulated coherence constraints (CC) that
express the semantic relation between these words.
CR1: a class can be specified by either its super class or
subclass. A class indicated by a key value cannot be
specified by a class indicated by another key value. A key
value can specify at most one class. The semantic rela-
tion is an inheritan ce relationship.
COMB1: mi  {C’, CA’, VK’} and mi+1  {C’, CA’, VK’}
/if mi = VK’ then mi+1  VK’
CC1 (mi – mi+1): Herite * (Xi, Xi+1)
CR2: a simple attribute can be specified by its value. The
attribute corresponding to the value must be identical to
the specified attribute in the query and their class re-
spective too.
COMB2: mi  {SA’} and mi+1  { VK’, VNK’} or mi 
{VK’, VNK’} and mi+1  {SA’},
CC2 (mi – mi+1): Yi = Yi+1; Xi = Xi+1
CR3: a simple attribute of a class can be used by its class,
its super-classes or sub-classes. The semantic relation is
an inheritance relationship.
COMB3: mi {C’, CA’, VK’} and mi+1  {SA’} or mi 
{SA’} and mi+1  {C’, CA’, VK’},
CC3 (mi – mi+1): Herite* (Xi, Xi+1)
CR4: a class is associated with another class if there
exits a reference link between these classes. The seman-
tic relation is a reference relationship.
COMB4: mi  {C’, CA’, VK’} and mi+1  {C’, CA’,
VK’},
CC4 (mi – mi+1): Ref (Xi, Xi+1)
3.2.2. Algorit hm of NG Validati on
The operation of validation of a NG constituted by sev-
eral identified keywords m1-m2-...-mn consists in finding
a pattern of the combination for every two consecutive
words mi and mi+1 for i  1, n–1 and verifying the
correspondent semantic relation CCj (mi – mi+1).
The algorithm of validation is defined as follows:
{
i: = 1;
if i = n then
/ * NG  1 keyword */
if Ident (mi) then ‘valid NG’ else ‘Erroneous NG’
else
/* NG  several keywords */
while (i  n  Ident (mi)  Ident (mi+1)  j  [1,4]/
CCj (mi – mi+1)) do
i: = i + 1; end;
if i = n then ‘valid NG’ else ‘Erroneous NG’
}
3.3. Semantic Coherence of the Verb with Its
Arguments
In the lexicon, a verb is described with its real subject
(RS) and its real object (RO). In a query, the subject of a
verb is one of keywords of the subject NG which really
carries out the action. It is noted (QS). The object is
generally the first keyword of object NG which under-
goes the action. It is noted (QO).
A verb is compatible with its subject NG (QS) and its
complement (QO) if:
 QS is identical to RS or it is its super or sub class
because the subject is always a class;
 1) The object is a class, then QO must be identical to
QS or be its super or sub class;
2) The object is an attribute (or a value of an attribu te)
then the attribute QO must be identical to QS.
4. Example of Query’s Validation
To understand the following example and the examples
of illustration quoted in the text, we give an extract of a
NLDB on the University of Oran, in French and its
equivalent in English. The adopted syntax is the follow-
ing one:
<class> (<mother class>}, <attribute1>, <attribute2>,...
<attributen> , verb1, verb2,...)
Personne (}, nom , ville, habiter)
Employé (Personne}, salaire)
Enseignant (Employé}, matière: Module)
Chercheur (Enseigna nt }, pr ojet : Projet)
Module (}, nom, coefficient)
Projet (}, laboratoire: laboratoire)
Laboratoire (} …)
Person (}, name, town, to live)
Employee (Person}, salary)
Teacher (Employee}, matter: Module)
Searcher (Teacher}, project: Project)
Module (}, name, coefficient)
Project (}, laboratory: laboratory)
Laboratory(}…)
Example: ‘Donner le salaire des enseignants de Compila-
tion qui ha bi t e nt Oran’
[Give the salary of teachers of Compilation who live in

Semantic Analysis of Natural Language Queries for an Object Oriented Database 1052
Oran]
Verb = habiter;
NG1 = m1 – m2 – m 3 = noms – enseignant s – Compilati on;
m1  SFNSA, m2  SFNC, m3  SFVVK,
m1 – m2 = COMB3;
CC3 = Herite* (Classe (Attrib (nom)), Classe (enseignants))
= Hérite* (employée, enseignant): = true;
m2 – m3 = COMB4 = Ref (Classe (enseignants), Classe
(Attrib (Compilation))) = Ref (enseignant, module): = true;
NG2 = Oran; m  SFVVNK,
In the lexicon, we have: habiter (personne, ville) and in
the query habiter (enseignant, Oran);
Herite* (personne, enseignant): = true, Attrib (Oran) =
ville.
The keywords are identified, the CCj between them
validated and the verb is coherent with its arguments,
and then the query is valid.
5. Experimental Results
To evaluate the performance of the system, we have
tested our system on 120 significant queries collected
from the students of computer department of university
Oran. 7 queries were disregarded because they did not
cover the BD application domain. Of the remaining qu e-
ries, 102 queries were correctly analyzed (i.e. success
rate 90, 26). The errors were mainly due to unregis-
tered keywords in the lexicon. Our lexicon contains
nouns, verbs and their various synonyms that typically
are used in a particular domain. Their semi-automatic
generation involves inevitably false results. A better
solution for the interface is the integration of a learning
system to enrich the lexicon.
6. Conclusions
Our work proposes a method of query’s validation for
OODB. Define a query key information to be analyzed
semantically its structure from one part and its sense
from another part, express the link which connects them
and the method of handling them were the strong prob-
lems which we tried to solve.
There is not dependency between our system and the
DB application domain. When changing one DB to an-
other, the only chan ge occurs at the lexicon.
Nevertheless, the study present limits relating to se-
lected requests model (a request includes only one sub-
ject, only one complement and only one verb). These
limits carried out knowingly, into a qualitative aim, make
that, the developed tools remain incomplete but can be
wide to more complex queries.
Also note that, the presented information is necessary
in NLDB, to treat the second aspect of the semantics
which is the translation of the NLQ to formal language
query such as OQL [16].
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