•
Millorar el procés de desenvolupament del sistema
•
Millorar la funcionalitat
– Utilizació en aplicacions més complexes
– Expansió de la cobertura lingüística
– Millora del controlador de diàleg
• Utilització del model del diàleg
• Utilització del model de tasques del sistema
– Integració amb altres modes: multimodalitat

Evolució
•
Transportables a dominis diferents
• Sistemes i eines per desenvolupar mòduls comunicatius
– INKA: Interfícies per construir Sistemes Experts
• Utilitza un Llenguate Structurat d’Interfícies
–
NL-MENU: Interfícies per consultar bases de dades
– NAT: Interfícies per diferents llenguatges i aplicacions

Evolució
Interfícies en LN per sistemes basats en el coneixement
•
El coneixement conceptual implicat és més complexe
•
Es necessiten noves functionalitats
– Preguntes sobre l’aplicació
• El coneixement lingüístic necessari és més gran
Incorporació de la representació del domini

Evolució
Eficiència Cobertura Reusabilitat
Basats en templetes orientats a la tasca
Bona Pobre Difícil
Recursos
Bona Rica Fàcill a diferents aplicacions
Recursos generals Pobre Rica Fàcil

• La integració de speech permet una comunicació més amistosa i noves aplicacions
– VOYAGER (MIT), Office Manager (CMU), MASK
(Multimodal Multimedia Automated Service Kiosk), ATIS (MIT,
CMU), Railtel, Sundial, Verbmobil
• La integració amb menus, gràfics i gest millora la communicació en moltes aplicacions
– MMI2 (Multimodal Interface for Man Machine Interaction)
–
MATIS (Multimodal Airline Travel Information System)
– COMET (Coordinated Multimedia Explanation Testbed),
ALFresco, CUBRICON

The functionality of GISE
• It supports NL communication with
KBSs
• It automatically adapts
– General linguistic knowledge
• Represented in a Linguistic Ontology
– To application communication tasks
• Represented in a Conceptual Ontology

Aim of the study
GISE, a system for improving NL Interaction with Knowledge Based Systems
• Reducing the run-time requirements for processing user interventions
• Guiding the user about the system capabilities
• Reducing the cost of developing the grammar and lexicon
• The GISE NLI uses:
- An application-restricted grammar and lexicon
- A menu-system
• GISE automatically adapts
- General linguistic knowledge to the application knowledge represented in a Conceptual Ontology

GISE
• Conceptual knowledge: Conceptual Ontology
– Application knowledge appearing in communication
– Communication tasks: general and specific
• Linguistic knowledge: Linguistic Ontology
– Linguistic structures expressing the communication tasks
• Control knowledge: Control Rules
– Controlling the process of relating general linguistic knowledge to application knowledge

GISE
Step 1.
Providing the application domain-specific knowledge
Step 2.
Adapting the general communication tasks to cover application knowledge
Step 3.
Adapting general linguistic knowledge to express the application communication tasks

The functionality of GISE
Data Description
Conceptual Ontology
General knowledge
Application knowledge
Control Description
Control rules
Dialogue system
Application grammar
Application lexicon
Linguistic Ontology
General knowledge
Application lexicon

The architecture of GISE
• There are 3 basic entities represented in 3 separated taxonomies
– Concepts
– Attributes
• Describing the concepts
• They are classified according to a syntacicosemantic taxonomy
– Operations
• The communication tasks consist of the expression of allowed operations over the CO concepts

Conceptual Ontology
• Generalization of the relations between
– Application knowledge in the Conceptual
Ontology
– Linguistic knowledge in the Linguistic
Ontology
• Each class is related to the linguistic structures expressing the consulting and filling of the attributes in the class

Conceptual Ontology
• participants : who_does what_object who_object
• being: is
• possession: has
• descriptions and relationships between two or more objects : of
• related processes: does

Conceptual Ontology
TOP
CONCEPT ATTRIBUTE OPERATION
TRANSPORT lex: (transporte) departure arrival departuretime arrivaltime price
TRAIN BUS

Conceptual Ontology
OF_TIME
ATTRIBUTE
OF
OF_QUANTITY
OF_COST
ARRIVALTIME lex: (llegar,...) unit: h/m
DEPARTURETIME lex: (hora_salida, salir,..) unit: h/m
PRICE lex: (precio,..) unit: Euro

Conceptual Ontology
TOP
CONCEPT ATTRIBUTE OPERATION
TRAIN lex: (tren) departure arrival departuretime arrivaltime price
MINIMUM_ATTRIBUTE
OF_TIME OF_COST
_VALUE_O concept attribute
Which <concept_name>
<attribute_verb> first?
Which is the cheapest
<concept_name> ?
Which train departures first?
Which train arrives first?
Which is the cheapest train?

Conceptual Ontology
• Operations are represented as CO objects
– The attributes describing these objects represent their parameters and their preconditions (the conditions that must hold for an operation to be executed)
• They are classified as
Simple or complex
Constructive
Creating a conceptual instance, filling attributes
Consultative
Consulting the value of an instance attribute

The architecture of GISE
• It is organized following the basic principles of the
Nigel grammar
A large systemic functional grammar of English
It is based on Hallidays’s work
It has been used with GUM to generate NL
• It covers the Spanish communication with KBSs
• It is represented as an ontology

•
Their size is not large -> Simple parsing
–
They cover only the domain communication tasks
–
They incorporate dynamic categories
•
They incorporate information from the
Conceptual Ontology -> Simple semantic interpretation
–
In the lexical entries
–
In the features augmenting the categories
–
In the preconditions associated with the rules

Linguistic Ontology
•
Linguistic knowledge is organized in two dimensions:
–
Rank: The scale of the grammatical structures represented
•
Clause
•
Group
•
Word
–
Metafunction: The type of meaning
•
Interpersonal: The type of interaction
•
Ideational: The propositional meaning and content
•
Textual: The information organization

The architecture of GISE
• They control the process of adapting the general linguistic knowledge to applications
• They establish general relations between
:
Concepts and operations in the CO
CO and LO objects
• Their form is: conditions ----> actions
• They are implemented in PRE (Production
Rules Environment)

The control rules
for each CONCEPT in ONTOLOGY do generate_CO_operations_ instance_modifying_concept ( CONCEPT ) generate_CO_operations_ instance_consulting_concept ( CONCEPT ) endfor

The control rules
for each OPERATION_INSTANCE in ONTOLOGY do generate_CLAUSE_instances ( OPERATION_INSTANCE ) for each ARGUMENT in OPERATION_INSTANCE do generate_GROUP/WORD_instances ( OPERATION_INSTANCE , ARGUMENT ) endfor endfor

The control rules
• It controls the generation of grammars and lexicons for each application
• It contains 48 rules organized in 8 rulesets
• It covers different types of interfaces
Interfaces supporting descriptions
Interfaces supporting consults
Interfaces supporting consults and descriptions
• It can be enlarged easily

The control rules
(rule cio ruleset creating_instance priority 1 control forever
(object ^con ?con ^pcc ?pcc)
--->
(?crinno := (create-name ‘criwno ?con)
(?concrinno := (create-object ?crinno ‘crinno))
(?oparg := (add-slots ?crinno ‘((con ?con)(pcc ?pcc))))
...
)

Dialogue sytem
Menu system
Parser Grammar
Lexicon
Dialogue
Controller
Communication Manager
User
Conceptual
Ontology
Application

• They are obtained from the LO objects
• They are represented in the definiteclause grammar (DCG) formalism because:
– Definite-clause grammars are more expressive than conventional context-free grammars
– They can be efficiently parsed
– They are automatically generated

The lexicon
A lexical entry representing the verb ser
String Category Interpretation es verbser (syn(num(s),tense(p))) (((l,X),(l,Y)), (X,Y) ) syn tactic num ber s ingular tense p resent

The lexicon
A lexical entry representing the concept
ARCHITECT
• String un_arquitecto
• Category indefngcon (syn(gen(m),num(s)), sem(con(architect))) syn tactic gen der m asculine num ber s ingular
• Semantic Interpretation sem antic con cept architect architect

The lexicon
Representing instances of concepts
Category function pngi (sem(con(person))) instance_of(person)
Representing values of attributes requested to the user during communication
Category function defngattrof (sem(con(person), attr(name))) name
Representing all possible values of an attribute defngvalofcause (sem(con(requirementobuild),attr(reasonotbuilt))) menu(reasonotbuilt)

The lexicon
• The number of lexical entries to be considered is reduced
• They allow the introduction of new values during communication
• They guide the user to introduce specialized terms

• It is based on the Ross version of the
Left-corner algorithm
• It assures there is always a correct choice to continue from a correct prefix
(prefix correctness)
• It can parse
– A word and predicts the set of all possible next words

•
The DC completes and disambiguates the semantic interpretation of the user request
–
The result is a complete specification of an operation over the Conceptual Ontology
•
The DC controls the execution of the operation
•
The DC passes the resulting information to the interface

The Dialogue Controller
•
The DC completes and disambiguates the semantic interpretation of the user request using:
–
History of dialogue
•
Concept and parameters of the previous operations
–
The Conceptual Ontology
•
The definition of the operation: mandatory arguments, default values,...
•
This process is simple when users build the requests using the NL options shown in the screen
– Mistakes and misunderstandings are avoided

Applications of GISE
• Previously its communicative tasks
– were fully integrated with functional tasks
– were based on a set of menus
• Applying GISE improves the communication:
– Complex concepts can be expressed in one sentence
– User-initiative dialogues are allowed
– The size of the linguistic resources is not big:
26 grammar rules and 112 lexical entries

• Main contribution:
– Proposing an organization of the knowledge involved in communication that improves the obtaining of the linguistic resources most appropriate for each application

Conclusions
• The Conceptual Ontology
– It provides a general framework for representing application communication tasks
– It includes a syntactic-semantic taxonomy of attributes
• Capturing the relations between application communication tasks and their linguistic realization

Conclusions
• The Linguistic Ontology
– It is an adaptation of NIGEL grammar for communication with KBSs in Spanish
• The Control Rules
– They control the process of adapting linguistic knowledge to each application
– A basic set of rules controls this process for different types of applications

Conclusions
Using grammars and lexicon restricted to the application communication tasks
• Their size is not large: The parsing is simple
– Dynamic categories are used
– A menu-system is integrated in the NLI
• They incorporate information from the
Conceptual Ontology: The interpretation is simple
• In the lexical entries
• In the features augmenting the categories
• In the preconditions associated with the rules

Conclusions
• Using an easy and clear language
– Guiding the user about application specific information
• Using a menu-system to introduce NL
– The user is guided about the system requirements
– The user can avoid typing sentences
• Tools helping the user are incorporated into the interface

GIWEB
Interface
Parser
Grammar
Lexicon
Dialogue
Controller
Wrapper
1
Wrapper
2
Conceptual
Ontology
Wrapper n
Internet
Source
1
Source
2
Source n
User