EXECUTION OF IMPERATIVE NATURAL LANGUAGE
REQUISITIONS BASED ON UNL INTERLINGUA AND
SOFTWARE COMPONENTS
Flávia Linhalis
Institute of Mathematics and Science Computing. São Paulo University, Av. Trabalhador São-Carlense, São Carlos, Brazil
Araraquara University Center (Uniara), Rua Voluntários da Pátria, Araraquara, Brazil
Dilvan de Abreu Moreira
Institute of Mathematics and Science Computing. São Paulo University, Av. Trabalhador São-Carlense, São Carlos, Brazil
Keywords: Natural language, Universal Networking Language (UNL), Ontology, Software Component.
Abstract: This paper describes the use of an Interlingua as a new approach to the execution of imperative natural
language (NL) requisitions. Our goal is to embed a natural language interface into applications to allow the
execution of users requisitions, described in natural language, through the activation of specific software
components. The advantage of our approach is that natural language requisitions are first converted to an
interlingua, UNL (Universal Networking Language), before the suitable components, methods and
arguments are retrieved to execute each requisition. The interlingua allows the use of different human
languages in the requisition (other systems are restricted to English). The NL-UNL conversion is
preformed by the HERMETO system. In this paper, we also describe SeMaComp (Semantic Mapping
between UNL relations and Components), a module that extracts semantic relevant information from UNL
sentences and uses this information to retrieve the appropriated software components.
1 INTRODUCTION
The idea of a restricted natural language interface is
very appealing because natural language is the way
which humans communicate with each other. That is
why several systems, developed throughout the last
twenty-five years, have pursued the goal of
describing user intentions in restricted natural
language and have them executed by computers
(Ballard & Bierman, 1979; Price et al., 2000; Cheyer
& Martin, 2001; Tsai et al., 2003).
Despite the intuitive appeal of a natural language
interface, it has been argued that a language like
English has too many ambiguities to be useful for
communicating with computers. The UNL
(Universal Networking Language) project aims to
represent, in the cyber world, the functions of
natural languages used in human communication.
But, different from natural languages, UNL
expressions are unambiguous. UNL is an interlingua
that enables computers to process information and
knowledge across language barriers. UNL enables
people to express knowledge conveyed in natural
languages (English, French, Spanish, Portuguese,
and so on). It also enables computers to
intercommunicate, thus providing people with a
linguistic infrastructure for distributing, receiving
and understanding multilingual information (UNL
Center, 2003).
Our goal is to be able to execute user requisitions
described in several restricted natural languages,
such as English, Portuguese, French, and so on. In
order to do this, user requisitions are first converted
into UNL. The UNL representation is used to extract
relevant semantic information from the input
sentences that will be necessary to retrieve and
execute software components. The user requisitions
will be executed through the activation of specific
component methods.
In this paper, requisitions refer to user intentions
described in a high level semantic abstraction and
related to a specific domain. For example,
considering the domain of web course management,
valid requisitions could be:
254
Linhalis F. and de Abreu Moreira D. (2005).
EXECUTION OF IMPERATIVE NATURAL LANGUAGE REQUISITIONS BASED ON UNL INTERLINGUA AND SOFTWARE COMPONENTS.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 254-259
DOI: 10.5220/0002528902540259
Copyright
c
SciTePress
(a) “Add student John Smith to the Hypermedia
course.”
(b) “Send an e-mail to the students of the
Operating Systems course saying that the test
will be on December 14
th
.”
The main advantage and innovation of our
approach is the use of UNL as an interlingua. In this
way, natural language requisitions, expressed in
different human languages, can be translated into the
same UNL representation before being executed. To
convert natural language into UNL, the HERMETO
(Martins et al., 2004) system was used.
To achieve our goal, a new system, the
SeMaComp (Semantic Mapping between UNL
relations and Components) system is being
developed. It uses ontologies to identify what
components, methods and arguments will be
necessary to execute requisitions expressed in UNL.
This paper is organized in the following way:
Section 2 discusses related works about interfaces
for the execution of natural language requisitions.
Section 3 describes the UNL project and the
HERMETO system. Section 4 presents an
imperative natural language requisition system using
SeMaComp. Section 5 describes an application in
the web course management domain. Section 6
concludes the paper with some remarks on future
work.
2 RELATED WORKS
The first efforts to execute user requisitions
expressed in natural language began in the later 70s.
The NLC (Natural Language Processing) system
(Ballard & Bierman, 1979) was designed to process
data stored in matrices or tables. It enables a
computer user to type English commands into a
display terminal and watch them executed on the
screen. A more recent example of the same idea is
NaturalJava system (Price et al., 2000). Its interface
accepts English sentences as input and generates the
Java source code to execute the sentences. Both
systems are very limited because input must be in a
restricted algorithmic fashion. Higher semantic level
sentences are not allowed.
Some approaches, such as OAA (Open Agent
Architecture) (Cheyer & Martin, 2001) and SOTA
(Tsai et al., 2003), have worked with software
components and agents to get a higher level of
abstraction. OAA is a framework for constructing
agent-based systems that makes it possible for
software services to be provided through the
cooperative efforts of distributed collections of
agents. OAA provides an interface that accepts
English sentences as input that are converted to ICL
(Interagent Communication Language), a Prolog-
based language. ICL is used, by the agents, to
communicate with each other and to register their
capabilities with a facilitator agent. The facilitator is
responsible for matching ICL requests to choose the
most suitable agents to execute these requests.
SOTA is an office task automation framework
that uses web services, ontology, and software
agents to create an integrated service platform that
provides user-centric support for automating intranet
office tasks. SOTA can take plain English text
sentences as input and serve users with a single and
integrate user-interface form to access web services,
thus avoiding the need to access each distributed
service manually. SOTA performs its tasks in three
phases: first it parses user input sentences to identify
possible web services using an ontology, next it
prepares most of the input data fields the services
requires, and, finally, it combines related services to
define a single task flow.
Such as OAA and SOTA, our work aims to use a
restricted natural language interface to describe user
requisitions and software components to execute
these requisitions. One of the major differentials of
our approach is that the natural language requisitions
are first converted to an interlingua (UNL (Ushida &
Zhu, 2001)), and then the requisitions are analyzed
and the appropriated component methods are called
(to process the requisitions). References to systems
that convert user requisitions into an interlingua and
use that interlingua semantic information to choose
the appropriated software components have not been
found in the literature.
3 THE UNL PROJECT
The UNL project started in 1996 and currently
embraces several universities and research
institutions in the world. The project proposed an
interlingua, entitled Universal Networking Language
(UNL), which has sufficient expressive power to
represent relevant information conveyed by natural
languages. For each natural language, two systems
should be developed: a "Deconverter" capable of
translating texts from UNL to this natural language,
and an "Enconverter" which has to convert natural
language texts into UNL.
UNL represents sentences using three elements
(UNL Center, 2003):
• Universal Words (UWs): Each UW relates to a
concept and is represented as an English word
that can be optionally supplied with semantic
specifications to restrict its meaning. The
following are examples of UWs: book,
book(icl>publication), book(icl>reserve). In the
EXECUTION OF IMPERATIVE NATURAL LANGUAGE REQUISITIONS BASED ON UNL INTERLINGUA AND
SOFTWARE COMPONENTS
255
two last examples, the meaning of book is
restricted by other UWs (“publication” and
“reserve”). The restrictions allow representing
UWs as disambiguated English words.
• Relation Labels (RLs): RLs express semantic
relations between UWs. There are today 44 RLs
defined. The RLs are represented as a pair
relation_label(UW1, UW2). For example:
o obj (move, table): This relation defines a
thing in focus that is directly affected by an
event or state. In our example, it means the
“table moved”.
• Atribute Labels (ALs): ALs express additional
information about UWs, such as verb tense,
intention, emphasis, etc. ALs are represented as
UW.@atrib
1
.@atrib
2
...@atrib
n
. For example:
obj(eat.@past, apple.@pl). The AL “@past”
indicates past and “@pl” indicates plural.
We do not intend to describe the UNL language
here in details. A full specification of UNL can be
found at http://www.undl.org.
3.1 HERMETO
HERMETO is a standalone environment for fully
automatic syntactic and semantic natural language
analysis (Martins et al., 2004). It can be used to
convert any natural language into the Universal
Networking Language (UNL). It receives as input a
dictionary and a grammar that should be
parameterized for each language, in a way very
similar to the one required by the UNL Center
Enconverter program (UNL Center, 2003). However,
HERMETO brings together three special distinctive
features: 1) it takes rather high-level syntactic and
semantic grammars; 2) its dictionaries support
attribute-value pair assignments; and 3) its user-
friendly interface comprises debug, compiling and
editing facilities. In this sense, it provides a better
environment for the automatic production of UNL
expressions.
Figure 1 shows examples of HERMETO
dictionary entries and Figure 2 presents examples of
its grammar rules. We do not intend to describe the
dictionary syntax and the rules formalism in this
paper. This information can be found in Martins et al
(2004).
Figure 1: Examples of dictionary entries
[a.m.] {} a.m. "a.m.(icl>ante meridiem)" (pos:abr)
<EN,1,1>;
[AM] {} a.m. "a.m.(icl>ante meridiem)" (pos:abr)
<EN,1,1>;
[a] {} a "a" (pos:art,typ:ndf) <EN,1,1>;
[access] {} access "access" (pos:ver) <EN,1,1>;
[add] {} add "add(icl>do)" (pos:ver) <EN,1,1>;
[admin] {} administrator "administrator" (pos:nou)
<EN,1,1>; [after] {} after "after(icl>how)"
(pos:adv,typ:tme) <EN,1,1>;
[after] {} after "after(icl>time)" (pos:pre) <EN,1,1>;
Figure 2: Grammar rules examples
; 2. PHRASE LEVEL
; 2.1. IMPERATIVE VERB PHRASE (IVP)
IVP[1] := VER.@entry + NOU + NOU -> nam(:02,:03),
obj(:01,:03)
IVP[2] := VER.@entry + NOU + NOU + PRE + ART +
NOU + NOU -> nam(:02,:03), nam(:06,:07), bj(:01,:02),
gol(:01,:07)
IVP[2] := VER.@entry + NOU + NOU + PRE + NOU +
NOU -> nam(:02,:03), nam(:05,:06), obj(:01,:02),
gol(:01,:06)
; 3. WORD LEVEL
; 3.1. VERB
VER[1] := ver.@entry
; 3.2. NOUN
NOU[1] := nou.@entry
NOU[2] := ppn.@entry
4 THE PROPOSED SYSTEM
As stated in section 1, our goal it to execute user
requisitions described in restricted natural language.
Figure 3 illustrates our approach.
Currently, the input requisitions must be
imperative sentences. The input requisitions also
have to obey grammar rules and words defined in a
dictionary. Both, grammar and dictionary have to be
defined according to a specific application domain.
HERMETO will use them to convert natural
language sentences into UNL.
The UNL sentence is the input for the
SeMaComp (Semantic Mapping between UNL
relations and Components) module. The SeMaComp
goal is to identify what components, methods and
arguments will be required to execute the UNL
requisition. To achieve this goal, the SeMaComp
module uses the Component Ontology (described in
session 4.1). Some concepts of this ontology are
shared with a Domain Ontology.
ICEIS 2005 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS
256
Figure 4: Component Ontology
Figure 3: The Proposed System
The application domain software components
have to be already installed in the system and ready
to be used. These components can make simple
queries and modify the Domain Ontology (and its
instances) according to user requisitions. They also
can perform external actions to the system, such as
send e-mail. The Domain Ontology is currently
developed using the Protégé tool (Noy et al., 2001),
hence the components access it through the Protégé
API. This API can be used directly by external
applications to access Protégé knowledge bases
without running the Protégé tool.
Natural Language Requisition
HERMETO
Dictionary Grammar
UNL representation
_______
_______
_______
SeMaComp
4.1 The Component Ontology
Ontology of
Components
Figure 4 presents the Component Ontology classes
and relationships. This ontology has to be
instantiated in accordance with the characteristics of
the application domain software components.
The instances of the class OntoDomainConcepts
correspond to concepts of the application domain
that are present in the Domain Ontology. The
instances of the class Components correspond to the
components of the application domain that can be
related to one or more concepts of the
OntoDomainConcepts class. The instances of class
Method correspond to the methods of each software
component of the application domain. The instances
of class Params correspond to the arguments of
each method. Finally, the instances of class Actions
correspond to imperative verbs. Each verb (action) is
Components,
methods an
argum
d
ents.
omponents Domain C
Methods Call
Domain
Ontology
Can query and
modify the domain
Protégé API
EXECUTION OF IMPERATIVE NATURAL LANGUAGE REQUISITIONS BASED ON UNL INTERLINGUA AND
SOFTWARE COMPONENTS
257
related to one or more methods, and each method is
related to one verb.
The class UNLRelations relates UNL relations to
information about components. The aim of this class
is to indicate the mapping between a particular
relation_label(UW1, UW2) and the components,
methods, arguments and actions in the Component
Ontology. This class contains instances representing
all the UNL relations currently being used in the
imperative sentences related to the application
domain.
Before defining the UNLRelation instances, it is
necessary to observe what semantic information can
be extracted from the UNL relations that are relevant
to the UWs-Components mapping.
5 WEB COURSE EXAMPLE
We can demonstrate our approach with a scenario
that involves a web course management domain.
The software components must be set and ready to
use. For this particular domain, we defined a set of
components; each one related to a specific concept
of the domain. For example, we have a
“TeacherComponent” that is responsible for the
execution of actions related to the “Teacher” concept.
This component has methods to create, delete and
list teachers, to assign a specific teacher to a specific
course, to update information about a particular
teacher, and so on. In a similar way, we have
components related to the concepts “User”,
“Student”, “Candidate”, “Course”, “Class”,
“Monitor” and “Administrator”. After the
development of these components, we defined a
Domain Ontology, with relationships between the
concepts of the underlined domain.
Figure 5 shows some examples of natural
language imperative sentences (requisitions) that can
serve as input to the system.
The requisitions must obey the grammar rules
and dictionary entries created for the domain.
Figures 1 and 2 showed a small part of the
dictionary and rules created for this domain. The
requisition will be converted into UNL (using
HERMETO). For example, consider the following
requisition:
“Delete administrator Mary from course Java.” (c)
HERMETO will generate the UNL representation
showed on Figure 6.
Figure 5: Imperative Sentences Examples
- Create course Operating System.
- Delete course Java.
- Add student John Smith to the class xxx.
- List classes of teacher Susan.
- Delete administrator Mary from the course Java.
- Update course Java candidate name from Mary Smith
to Maria Smith.
Figure 6: UNL representation generated by HERMETO
for the sentence on (c)
obj(delete,administrator)
gol(delete,course)
nam(administrator,Mary)
nam(course,Java)
The UNL relations, shown in Figure 6, will serve
as input to the SeMaComp module. It will use the
Component Ontology to detect which components
methods and arguments should be used to execute
the requisition. The Component Ontology should
have been previously instantiated according to the
characteristics of the domain software components.
Special attention should be given to the class
UNLRelations. Before defining this class, in the
Component Ontology, it is necessary to observe
what semantic information can be extracted from the
UNL relations, present on the application domain,
that is relevant to the UWs-Components mapping.
Figure 7 illustrates the UNL relations present in the
imperative sentences of the domain as instances of
the class UNLRelations. This class instances and
their relationships indicate which information can be
extracted from the UNL representation of the
sentence to help finding the most suitable
components, methods and arguments to execute the
requisition. This class should state if a UW, related
to a particular relation label, corresponds to a
component, an action, an argument type, an
argument value or a return value.
As shown in Figure 7, UW1 of obj relation is
Params
Actions
Components
UNLRelations
obj
UW1
UW2
gol
UW1
UW2
nam
UW1
UW2
mod
UW1
UW2
Instances
Figure 7: UNL-Components Mapping
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258
always related to an Action (it means that the value
of UW1 can be any instance of class Actions in the
Component Ontology). Similarly, UW2 is related to
a Concept – that should have one or more software
components related to it and have to be present at
the Domain Ontology.
SeMaComp separates the tokens of the UNL
sentence and classify them using the Component
Ontology. For the UNL representation of the
requisition in Figure 6, SeMaComp will identify the
relevant information shown in Figure 8.
Figure 8: Relevant information extracted from the UNL
sentence of Figure 6
With this information, SeMaComp searches the
Component Ontology to discover which methods are
related to the action “delete” and belong to one of
the components associated to the “administrator”
concept. This search returns the methods
deleteAdmin and deleteAdminCourse. Still
in the Component Ontology, SeMaComp retrieves
data about the number of arguments, argument types
and return type of each identified method. These
information are used to analyze the available
methods and to conclude which one is the most
suitable to execute the requisition.
6 CONCLUSIONS AND FUTURE
WORK
This paper described a new approach to the
execution of natural language requisitions. This
approach proposes a semantic mapping between
UNL relations and software components. UNL is an
interlingua, the advantage of using an interlingua to
describe user requisitions is that it can represent
requests derived from different languages (English,
Spanish, Portuguese, French, etc.).
In our system, natural language requests can be
translated into UNL and the SeMaComp module can
perform the semantic mapping between those UNL
requests and software components and activate
methods in these components to fulfill the requests.
The semantic mapping can be used in different
application domains; it is just necessary to write the
appropriate software components, define the
dictionary and grammar rules (that will be used by
HERMETO), create instances of the Component
Ontology and define the Domain Ontology.
The semantic mapping between UNL relations
and software components currently performed is
limited to the information given by the user in the
natural language requisition. As future work, we
intend to extend the Component Ontology to support
context information. Another future work is to
perform the semantic mapping between UNL
relations and software components using not only
imperative sentence structures, but also interrogative
and conditional sentence structures.
Action = delete
Main Concept: administrator
Other Concept: course
Argument: Mary
Argument type: administrator
Argument: Java
Argument type: course
Number of arguments: 2
Return type: none
Our ultimate goal is to provide a restricted natural
language interface that could be used by other
systems to allow computer-based actions to be
described in several natural languages.
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