INTELLIGENT-MULTIDIMENSIONAL-DATABASE-INTERFACE

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 5
ISSN 2229-5518
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INTELLIGENT MULTIDIMENSIONAL DATABASE
INTERFACE
Mona Gharib Mohamed Reda Zahraa E. Mohamed
Faculty of Science, Mathematics Department, Zagazig University
Zagazig, Egypt
ABSTRACT
In the present computing world, most new-generation database applications need for intelligent interface to enhance efficient interactions between database
and the users. Database query language SQL could be difficult to the non-expert users and learning these formal queries takes a lot of time. In this paper,
we discussed mapping of natural language queries to SQL rather than building normal query and a user simply poses the question in everyday verbiage
language. Furthermore, we introduce a dynamic approach to determine the tables and attributes involved in the query by using database metadata schema.
Our approach will minimize the time that used to build the queries thus minimizing the code size and effort for building query. We also propose a dynamic
component that helps in building any NLIDB this component is linked to all the NLIDB system's components and helps to build the dictionaries and database
schema graph.
KEYWORD
Intelligent Multidimensional Database Interface (IMDI), Natural Language Interface to Databases (NLIDB), Database Management System (DBMS),
Structured Query Language (SQL).
——————————  ——————————
INTRODUCTION
Natural Language Interface to Database (NLIDB) systems
have provided an easy access to database system without the
need for the user to use formal query languages [1], such as
SQL. Database query languages can be difficult to the non-
expert users and learning these formal queries takes a lot of
time. In NLIDB, users can type a question or a sentence in
their natural language. Then it will be converted through a
special natural language interface interrupter into formal
database query. A major problem that faces the NLIDB
designer is the identification of the tables that contain the
required information and the desired attributes in query. In
[2], previous work used static built-in templates of possible
production rules for the possibly introduced queries. In
which tables' names are embedded in the template. However,
this requires large code that considers all possible query
templates. The standard approach to database NLP systems
relies on creating a ‘semantic grammar’ for each database,
and uses it to parse the NL questions [2]. The semantic
grammar creates a representation of the semantics of a
sentence. After some analysis of the semantic representation
a database query can be generated to SQL. In the existing
systems, they are some problems such as: the requirement of
using additional knowledge to extract meaningful
information, the input can have many choices and it is not
easy to choose the correct choice among target
representations (one-to-many mappings), the complexity of
mapping in NL sentences if you change a single word, the
entire structure can be changed, which is called the quantifier
scoping problem. Words such as “the,” “each,” or “what”
can have several meanings in different situations. Another
problem is the identification of tables required to build the
query. In this work, we will solve some these problems by
using semantic analysis, lexical dictionary, interactive query
formulation and syntax-based system.
In this work, we provide user friendly query interface for
non expert users and this interface will have a graphical user
interface; user should have knowledge about data source
structure and contents which are essential to the construction
of intuitive interface. The transformation of natural language
interface to database divide into two steps: Query
interpretation and Query translation.
Query interpretation: a natural language query enters by the
user is parsed into a logical representation.
Query translation: the logical representation of a query is
mapped to a database querying language.
It is evident that query interpretation process requires both
extensive linguistic resources for understanding query,
whilst the query translation step requires semantic resources
for mapping query terms to database entities.
BACKGROUND AND RELATED WORK
NLIDBs permits users to formulate queries in natural
language, providing access to information without requiring
knowledge of programming or database query languages. A
general information management system that is capable of
managing several kinds of data, stored in the database is
known as Database Management System (DBMS).In the real
world we obtain information by asking questions in a natural
language, such as English. Supporting arbitrary natural
language queries is regarded by many as the ultimate goal
for a database query interface when it comes to using a
multidimensional database for internal business purposes;
the main advantage is the ease of obtaining data quickly and
succinctly. Any question that contains a request for
information that is not found within the database will not
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result in a direct response. DATABASE systems are being
used in almost all aspects of our lives, in banks, universities,
hospitals. Etc. To manage databases; users have to learn a
formal query language. Formal query languages are difficult
to learn and Master; at least by non-computer specialists.
Casual users need an application interface through which
they can manage the database. There are several major
reasons why Understanding is a difficult problem [1]. These
problems include: the requirement of using additional
knowledge to extract meaningful information, the input can
have many choices and it is not easy to choose the correct
choice
Among target representations (one-to-many mappings), the
complexity of mapping because in NL sentences if you
Change a single word, the entire structure can be changed,
which is called the quantifier scoping problem. Words
Such as “the,” “each,” or “what” can have several meanings
in different situations [1, 12, 13]. Another problem is the
Identification of tables required to build the query. Previous
work used static built-in templates of possible production
Rules for the possibly introduced queries. This will require
the careful analysis of the user requests and huge code for
Transformation of these requests into queries. There are
many applications that can take advantages of NLIDB [2]. In
PDA and cell phone environments, the display screen is not
as wide as a computer or a laptop. Filling a form that has
many fields can be tedious: one may have to navigate
through the screen, to scroll, to look up the scroll box values,
etc. Instead, with NLIDB, the only work that needs to be
done is to type the question similar to the SMS (Short
Messaging System).The NLIDB is actually a branch of more
Comprehensive method called Natural Language Processing
(NLP). In general, the main objective of NLP research is to
create an easy a friendly environment to interact with users
in the sense that computer does not require any
programming language skills to access the data; only natural
language is required. The research of Natural Language
interface to databases (NLIDB) has recently received
attention from the research communities. The area of NLIDB
research is still very experimental and systems so far have
been limited to small domains, where only certain types of
statements can be used. When the systems are scaled up to
cover larger domains, it becomes difficult due to vast amount
of information that needs to be incorporated in order to parse
statements. Although the earliest research has started since
the late sixties, NLIDB remains an open search problem.
Intelligent system overview
To translate a natural language query into SQL Query
expression, we first need to identify words/phrases in user
question which will be mapped into the corresponding SQL
query. Unclassified terms can’t be mapped in our system. We
will use query interpretation to fill both semantic dictionary
from user question and lexical dictionary from database
metadata. We will define query tables and attributes by using
semantic and lexical dictionaries. Then, we will use query
generator to map data from semantic dictionary with data
from lexical dictionary then test if we have required data to
build SQL query component (Tables, Attributes, Conditions)
then execute query on database and retrieve data to user.
Fig (1): Intelligent database interface.
Intelligent System Components
Our system consists of four main components: User Friendly
Interface (UFI) to provide user with friendly graphical user
interface, Query Interpretation (QI) used to fill semantic
dictionary from user question and fill lexical dictionary from
database using Metadata, Query Generator (QG) used to
map semantic with lexical dictionary and generate SQL
statement and system outputs (RV) used to view returned
result from database.
User Friendly Interface
We will provide user graphical friendly interface to facilitate
user query by writing question in natural language with
some user guides to ensure that we have enough data to
generate database query
Query Interpretation
After receiving user question we have ability to check if user
poses this question before to get SQL query that are saved in
our system to increase performance by providing a method
to save question if user poses new question we will go
further to generate corresponding SQL query by using our
approach.
Semantic analysis
Once the words are extracted from the user request using the
semantic analysis, they are mapped to the database
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attributes. This implies that the meaning of each word needs
to be defined. Each word in user question may have many
Synonyms words that could be used in user query each word
in semantic dictionary may have many Synonyms words. We
will semantic dictionary words for mapping with lexical
dictionary words. This process transforms user question
from normal language SQL query. Both the lexical analysis
and the semantic analysis are domain dependent modules.
Many NLIDB systems are based on syntactic grammar or
semantic grammar like in. This approach creates a ‘semantic
grammar’ for each database. The semantic grammar creates a
representation of the semantics of a sentence. For example
“name” word may have many synonyms (names, nam and
Names).
Lexical analysis
This dictionary is used to store the most common words
expected to be used in the user request for this specific
database system which makes this dictionary domain
specific. These words consist of all the elements of the
database (tables, attributes, relations, and valued attributes),
which we called root words, and their possible synonyms
along with some misspelling words. This step uses a lexical
dictionary which holds the definition of all the words that
may occur in the user request. We will fill lexical dictionary
data from database using Metadata schema that are
generated from our system database, each word in semantic
dictionary should have one word in lexical dictionary, then
we define word type in lexical dictionary, every word should
have one of these types ( Tables Names, Attributes,
Condition ). Keywords are defined to be words or phrases
that have particular meaning within the domain of work.
These words consist of all the elements (relations, attributes
and values) of the database and their synonyms. For
example, if we have a database that has the attributes name
and age of students, the lexical dictionary will contain the
keywords found in the database like: “name: name, names,
named, student: students, pupils, pupil, age: age, ages, old,
more: more, larger, much, and numbers like 20, 22, 21, . . .”
where the bold words are the keywords substituted instead
of its following words. A request such as: “find the names of
students aged more than 21” when lexically analyzed will be
compared to the lexical dictionary and will be converted to:
“name, student, age, more, 21”.The idea behind adding the
misspelling words to the dictionary is to minimize the
rejection rate of the user request. The user request can be
rejected if it does not contain meaningful parts that could be
used to build the query. If the system can determine every
keyword and their corresponding meaning from the user
request, then the sentence can then be converted easily to an
appropriate query.
Query Generator (QG)
Our approach to generate SQL query will use templates:
Select Attributes from Table Name or Select Attributes from
Table Name Where Condition or Select Attributes from Table
Name Where Conditions. First template will define
Attributes and Tables Name, Second Template will define
Attributes, Tables Name and one condition, Third will define
Attributes and Tables Name and more than one condition.
Query Formulation
The mapping process from natural language to SQL Query
requires our system to be able to map words from semantic
dictionary with related words from lexical dictionary. Each
word in semantic dictionary should have one word in lexical
dictionary then define word type in lexical dictionary
(Tables, Attributes, Conditions) Due to the limited
vocabulary understood by the system; certain terms cannot
be properly classified. Clever Natural language
understanding systems attempt to apply reasoning to
interpret these terms, with partial success. To ensure that this
process proceeds smoothly for the user, we provide the user
with specific feedback on how to rephrase.
Syntax-Based Systems
In syntax-based systems the users question is parsed (i.e.
analyzed syntactically) and the resulting parse tree is directly
mapped to an expression in some database query language.
Syntax-based systems use a grammar that describes the
possible syntactic structures of the users’ questions. Syntax
based NLIDBs usually interface to application-specific
database systems that provide database query languages
carefully designed to facilitate the mapping from the parse
tree to the database query.
System Process
The two dictionaries are built empty of data, and are
filled/updated through the database management
component. The database metadata schema graph is also
built through the database management component during
the building of the database tables. Extract the database
schema graph from it, and filling/updating the two
dictionaries used. Finally, the result from this preprocessing
step are a set of tokens of words after the removal of the un-
wanted characters (like,*, &, .etc.). For using more than on
condition we will use Query logical and numeric operators
the role of this function is to return a string that contains the
condition part in the query. The valued attributes found in
the request represent the condition. This function takes the
processed request, identifies the valued attributes, matches
each with the proper attribute then returns a string that
contains the condition statement used in the query. This
function can deal with both simple and multiple conditions
on different attributes. Logical operators (And, Or, Not)
numeric operators (Greater or more than, Less than, Equal)
Greater or more than converted to (>=).
Less than converted to (<=).
Equal converted to (=).
Not Equal converted to (=).
Query Attributes built in SQL Functions
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We will check if user question has SQL built in functions by
using semantic dictionary data if there exists we will get
corresponding function name to determine which function
category belong to in our system we will cover some
categories such Mathematical and statistics function Such
(Count, Sum, Max, Min, Avg, Mean, etc), we will search for
some words in user question should be mapped into
semantic dictionary with corresponding word in lexical
dictionary for example how many in semantic dictionary will
be converted into Count in lexical dictionary, we have
already predefined data in semantic and lexical dictionaries.
Fig (2): Intelligent system - Conceptual Model
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Fig (3): Intelligent system Database Diagram
Intelligent system design
We will have some system artifacts to describe our system
approach in different forms such
Conceptual data model
This is the highest level ER model in that it contains the least
granular detail but establishes the overall scope of what is to
be included within the model set. The conceptual ER model
normally defines master reference data entities that are
commonly used by the organization. Developing an
enterprise-wide conceptual ER model is useful to support
documenting the data architecture for an organization.
Our conceptual model will be used to describe our approach
for building SQL query first user poses a question, we will
save user question and corresponding query to return query
if user ask for this question again to increase system
performance by calling corresponding query. Extract words
from user question and check for synonym words then fill
semantic dictionary, check if question have condition and
define which template should be used then fill lexical
dictionary from database and define word types in lexical
dictionary.
Fig (4): Intelligent system activity diagram
Physical model
Our system physical model will be used to describe database
tables, attributes and relationships user question will have
many to many relationship with synonym words, each word
in semantic dictionary have many words in synonym word
one too many relationship, each word in lexical dictionary
have one corresponding word in semantic dictionary one to
one relationship and user question could have one or many
condition one too many relationship.
Activity Diagram
Our system will provide user friendly interface for user
question then using query interpretation to fill semantic
dictionary from user request and lexical dictionary from
database schema them mapping two dictionaries to generate
SQL query by using query generator after create query we
will check if we have enough data if not return feedback to
user if we have correct query execute on database.
Screen Shots for Intelligent system interface
Intelligent system interface is Graphical user friendly
interface help user to enter question, compile question to
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Fig (5): Intelligent Interface – User Question.
SQL query and execute query in our system database using
DBMS then return result from database to view to user.
Intelligent system interface for previous questions are used to
view all previous question users have entered before and we
have saved complied query in our system to increase
performance and return result in less time from go on steps
to generate new SQL query, User have ability to choose
question then press Execute Question to return data and
view to user.
Fig (6): Intelligent Interface – Previous Questions.
CONCLUSION
In this paper, we introduced a new approach for generating
SQL query using multidimensional database interface for
non expert user by posing a question then converting
question to SQL query. The main advantage of our system is
using natural language for writing question, ability to save
questions and mapped query for each question in database to
increase system performance and stability. We used dynamic
template to generate database query using query
interpretation, semantic and lexical analysis to fill semantic
and lexical dictionaries. Then get required data to build SQL
query depending on our templates. We have used a dynamic
approach to generate query whatever database we used by
using metadata to fill lexical dictionary and have ability to
communicate with different database providers by
depending on standard SQL query language. And this
approach will minimize the time and effort that used to build
the queries.
REFERENCES
[1] Amany Sarhan. "A proposed architecture for dynamically built
NLIDB systems". Knowledge-based and Intelligent Engineering Systems
Journal, 13 (2), IOS Press Amsterdam, Netherlands, 2009.
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[2] Zongmin Ma, "Intelligent Databases: Technologies and Applications",
IGI publishing, 320 pages, 2007.
[3] Burlesan, Donald K., Joe Celko, John Paul Cook, and Peter Gulutzan.
2003. Advanced SQL Database Programmer Handbook.BMC Software
and DBAZine.
[4] I. Androutsopoulos, G. Ritchie and P.Thanisch. “Time, tense and
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[5] C. Hallett, Generic Querying of Relational Databases using Natural
Language Generation Techniques, Proceedings of the Fourth
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[6] Dietmar Wolfram, "Applications of SQL for Informetric Data
Processing", Proceedings of the 33rd conference of the Canadian
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[7] Androutsopoulos, I., Ritchie, G. and Thanisch, P. "Natural Language
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[8] Donald P. McKay and Timothy W. Finin, "The Intelligent Database
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1990 National Conference on Artificial Intelligence: 677-684, 1990.
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