QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGE

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.8, No.4, July 2017
DOI : 10.5121/ijaia.2017.8405 53
QUESTION ANSWERING SYSTEM USING ONTOLOGY
IN MARATHI LANGUAGE
Sharvari S. Govilkar1
and J. W. Bakal2
1
Department of Computer Engineering, PCE, Mumbai, India
2
Department of Computer Engineering, SJCOE, Mumbai, India
ABSTRACT
Humans are always in a quest to extract information related to some topic or entity. Question answering
system helps user to find the precise answer of the question articulated in natural language. Question
answering system provides explicit, concise and accurate answer to user questions rather than providing
set of relevant documents or web pages as answers as most of the information retrieval system does. The
paper proposes question answering system for Marathi natural language by using concept of ontology as a
formal representation of knowledge base for extracting answers. Ontology is used to express domain
specific knowledge about semantic relations and restrictions in the given domains. The ontologies are
developed with the help of domain experts and the query is analyzed both syntactically and semantically.
The results obtained here are accurate enough to satisfy the query raised by the user. The level of accuracy
is enhanced since the query is analyzed semantically.
KEYWORDS
Question answering system (QAS), Ontology, Marathi Natural language QA system (NLQA), Natural
language processing (NLP)
1. INTRODUCTION
With the rapid growth of the amount of online and electronic documents in Indian regional
language, the keyword based approaches lack many important elements to enable QA driven
process. So a system is required which can provide user with accurate answers for their queries
.Question answering system provides user with functionality where they can ask questions in
natural language and the system returns answer which is most accurate and precise of all the
possible answers for the given input question. Question answering supports user with providing
option to ask natural language query rather than traditional structured queries. A question
answering system provides more accurate result when ontology is used for representation of
knowledge. Ontology is a form of conceptual representation of information where relation
existing between different entity and details about a particular entity is provided.
Any question answering system basically consists of three parts as question processing, answer
retrieval and answer generation. In question processing users natural language question are parsed
to formulate question in machine readable form using different approaches. Then in answer
retrieval candidate answers are extracted based on intermediate representation of question. Finally
in answer generation phase user understandable precise and accurate answer is generated and
provided to user.
QA systems are classified into two main types as close domain QAs and open domain QAs. In
close domain QAs, scope of user question is limited to a particular domain like sports, medicine,
entertainment, history and others. An open domain QAs mostly works like search engines where
scope for question is global.

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Question in any question answering system can be of varying types. Question can be factoid
question for which answers are simple fact about the entity in question. Some question can be of
descriptive type where one needs to full detail about person, place or any event. There can be
simple yes/no type of question where answers are as yes or no. A question can also be an
instruction based question where answers are provided as an instruction to accomplish any task.
Question in QAs can be of many other forms which provide precise answer in the same format as
that of question provided.
There is very less work has been reported for creating QA system for natural languages like
Hindi, Marathi etc and specifically there are no such systems available where ontology itself is
represented in Marathi. Most of the QA system converts the Indian regional language data to
English and the answers are extracted which many times lead to loss of morphological rich
contents of Hindi or Marathi. In recent past, information extraction was based on keyword
matching, but it has main drawback of semantic matching. To achieve semantic matching,
ontology’s with it’s onto triples appeared to be efficient method. Ontology’s can be general or
domain specific and can be created automatically or manually.
As ontology has become trending topic now days, there are sufficient tools and information
available to build a question answering system using ontology in English but hardly any ontology
is created where data itself is represented in Hindi or Marathi.
The aim of this paper is to design, implement and experiment a new Marathi language QA
framework based on ontologies where answers to the user’s questions are provided by using
predefined domain specific ontology. The overall objective is to provide user with semantically
correct and accurate answer for their queries in Marathi language.
In section 2, related work and motivation is discussed in detail. Proposed system is described in
section 3.Working of system is mentioned in detail in section 4. Section 5 explores performance
analysis of QAs system. Finally, paper is concluded in section 6.
2. RELATED WORKS
QA systems are designed to address the problems of traditional search engines and meet the
growing requirements of users searching the large amounts of information available on the web.
In fact, these systems are faced with a double challenge: first processing and understanding a
question in natural language and second identifying and extracting the correct answer from a set
of documents also in natural language.
Sahu, Shriya, N. Vashnik, and Devshri [1] Roy have presented an approach to extract answers
from Hindi text for a given question where the text is expressed in the form of query logic
language and then relevant answer is extracted for the given question. The focus of the system has
been basically on four kind of questions type such as: What, Where, How many, and what time.
The type of question and keywords where extracted by using shallow parser, but no semantic
relations are consider while extracting information. There approach uses the traditional methods
i.e. to take words as independent words during matching and just check the existence of the query
keywords in the stored data and no relations constraints between words in a phrase or
neighborhood are extracted which leads to less accuracy.
Hindi Question Answering System is created by Stalin, Shalini, Rajeev Pandey, and Raju Barskar
[2]. The system is based on searching in context by using similarity heuristic and utilizes
syntactic and partial semantic information. Domain-specific and question specific entities are
found out after removing the stop words and also longest phrase are extracted while processing
query. Here database is used to send candidate answers collection, based on keyword present in

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the question, to next answer extraction module which extract candidate answers from the
retrieved documents. Building of limited words synonyms lexicon reduces the accuracy of system
due to mismatch of unavailable entities.
Using locality based similarity heuristics Kumar, Praveen, et al.[3] have created Hindi search
engine. It provides facility to extract correlated contents from set of e-learning contents. The
architecture consists of an entity generator which generates specific domain entities. Such
generated entities where corresponding to the questions of which users wanted to retrieve
answers. Questions provided by the users where then classified for selection of appropriate
answers. From the query stop words are removed and relevant keywords where extracted. Query
was enriched with synonyms of keywords. Finally the query is passed to retrieval engine, which
on basis of locality returns top passages after ranking.
To process question provided in Hindi language and retrieve answers for those question, Sharma,
Lokesh Kumar, and Namita Mittal[4] have used Named Entity based n-gram approach for their
question answering system. For retrieval of answers first question classified and analyzed to
generate a proper query. Question classification helps to identify relevant type of answers. Then
by using similarity metric relevant document is retrieved which probably contains the answer and
at last by using the bigram and NER relevant answers are retrieved for the given question. Overall
higher accuracy was obtained by using the bigram approach but accuracy dropped in scenario
where synonyms present in document where not matched due to the use of syntactical approach.
A dialogue based question answering system which provides answers related to railway domain
in Telugu language is proposed by R. Reddy, N. Reddy and S. Bandyopadhyay [5]. Question
answering process is based on keyword approach where input query are tokenized and keyword
are extracted using knowledge base related to railways. Tokens generally consist of train names,
station names whereas keywords specify when, in, out, go and others present in the query text.
Query frame is extracted by matching it with predefined procedures to generate relevant SQL
query. Dialog manager task is to interact with users if more information is needed to execute SQL
query to fetch relevant answer to user question.
Question answering system to produce answer of question in Punjabi and English is proposed by
V. Gupta [6]. The system accept query in English or Punjabi language of which stop word is
eliminated initially. Then from the query string key terms like noun, adjective, verbs or adverb
are extracted. Using dictionary of Punjabi and English language synonyms of key terms is
extracted. Finally query is reformulated using the extracted keywords and its synonyms. By using
reformulated query various matching web pages are retrieved using a search engine. Extracted
documents are summarized based on proximity of key term found in documents and finally
candidate answer is provided as per its rank.
An algorithm for Punjabi question answering system is proposed by P. Gupta and V. Gupta [7].
The system provides a better approach for finding patterns and matching to extract accurate
precise answers from set of possible answers. The proposed algorithm works for ਕੀ (what), ਕਦᶦ
(when), ਕਿਕੱਥੇ (where), ਕੋਣ (who) and ਕਕਉ (why) form of questions where first question word is
extracted from question then as per different procedure create for each question type
corresponding question keywords are extracted and through final answers are retrieved. The
overall accuracy of system is 73 % where 4850 question where asked for over 50 documents of
Punjabi language
Keyword based question answering system is developed by J. Cherapanamjeri, L. Lingareddy,
Himabindu. K, [8] which provides answer to question related crop statistics in Telugu. All the key
words in the user query are mapped with database and if the keyword matches then appropriate
SQL queries are generated which fetch answer from the database. First the input query is

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converted into WX notation and the tokenized. All the tokens are searched in knowledge base and
if token is found in KB then corresponding key value pair is stored in memory which aids in
development of natural language query to be provided to user. If user acknowledges the query
then its corresponding SQL query is generated using the query frame and fired on database to
fetch answer which is finally converted to natural language text by using predefined templates.
Chaware, S., and S. Rao [9] has discussed a system where Semantic matching is performed using
ontology for Hindi and Marathi languages to infer the information from knowledge base.
Knowledge is represented using ontology. The data and ontology are maintained in English for
easy building and traversing, the query terms from a query matches with ontology terms
semantically by using synsets for each language. Finally, ontology terms are extracted to
represent knowledge as an answer for the query. The approach converts local language to English
using bilingual dictionary where there is more chance of translating mismatch and loosing of
morphological rich words and phrases of Hindi and Marathi language, which may lead to
mismatched query keywords.
Tahri, Adel, and Okba Tibermacine [10] have proposed a new architecture to develop a factoid
question answering system based on the DBPedia ontology and the DBPedia extraction
framework. There system SELNI is a sentence level question answering system that integrates
natural language processing, ontologies, machine learning and information retrieval techniques.
Three steps are followed to build this system as the comprehension of the question, detection of
its answer type, Question Processing, resources and keywords extraction to build SPARQL query
and execute it by interrogating the DBPedia ontology. The result of the query is the answer of the
given question. SELNI system offers encouraging results while comparing to other question
answering systems.
Wang, Chong, et al [11] has created a Portable natural language interface to Ontologies, name as
PANTO which accepts generic natural language queries and outputs SPARQL queries. Based on
a special consideration on nominal phrases, it adopts a triple-based data model to interpret the
parse trees output through parser. They have used Stanford Parser and multiple existing
techniques and tools are integrated to interpret parse trees of natural language queries into
SPARQL. To understand sense of the words in the NL queries and WordNet and string metrics
algorithms are also integrated.
A prototype system is developed by Lopez, Vanessa, Michele Pasin, and Enrico Motta named
AquaLog [12] which is a portable question-answering system which takes queries expressed in
natural language and ontology as input and returns answers drawn from the available semantic
markup. AquaLog uses GATE NLP platform, string metrics algorithms, WordNet and novel
ontology-based similarity services for relations and classes to make sense of user queries with
respect to the target knowledge base.
Architecture for ontology based natural language question is proposed by Raj, P. C. [13] where
concept of semantics and ontology is used to facilitate better query construction and extraction of
answer. Architecture consists of question processing, document extraction and processing and
finally answers processing. Here in the question processing module the question is analyzed using
NLP techniques like POS tagger, Parser, NER. In second module relevant documents are
retrieved from repository based on conceptual indexing and processed to extract candidate answer
set. In answer processing module candidate answers are filtered and finally answer are generated.
The literature review shows that most of the existing QA systems are available for English
language and some researchers have worked on Hindi, Telgu and Punjabi as Indian regional
languages. Most of these algorithms have used Cross Lingual based approach to extract the
information. The QA system for Telgu is based on dialogue manger which uses SQL query

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generator to fetch answer. Most of the existing system mostly provide answers for “what, where,
when and who” type of questions only.
Various approaches like DBPedia framework, Ontology, synonym matching, SQL query
generator, Bigram, NER had been used in past to extract answer for given questions. But most of
them worked well with English language only. Literature review also shows that similar work of
QAs for Marathi language has been recently started. Author has used concept of Ontology but the
actual ontology is created and traversed in English language so Cross Lingual based approach is
used to extract the information.
3. PROPOSED SYSTEM
The proposed system provides most relevant and precise answer to the user’s natural language
questions through semantic matching by using ontology. The input to the system is users question
in Marathi language and output will be precise answer of the question.
Fig.1 presents proposed framework of Marathi QA system. User specifies the query in Marathi
natural language in textual form. Input to the system is natural language Query in Marathi
language. Input query is first tokenized to generate individual tokens and then these tokens
undergo word grouping where two or three corresponding word are merged together if they are
related with each other by using the available word grouped list. Part of speech (POS) tagging is
performed on word grouped tokenized query text to extract relevant part of speech associated
with the query text. POS tagged query text then passes through chunking process where noun and
verb grouped present in the query text are extracted. Based on the extracted chunked groups
initially query triples are extracted using Subject, Object and Verb (SOV). Then next process is to
generate onto triples by fetching relevant onto words from ontology. Finally ontology is traversed
to fetch relevant answer based on generate onto triples, if onto triple matches with any onto set in
ontology then corresponding answer is fetched and passed to answer generation process to present
the answer as natural as possible mostly in the form of natural language text.
Sample input and output for Marathi query:
Input Question: मुंबईची मुख भाषा कोणती आहे?
Answer: मुंबईची मुख भाषा मराठ आहे.
Figure 1. Proposed Framework
4. WORKING OF THE SYSTEM
Proposed question answering system is a text based question answering system where ontology is
created for different domains for semantic representation of Marathi content.

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Due to unavailability of ontology creation tool for Indian regional language like Hindi and
Marathi, we have created a simple representation for creation of ontology in Marathi by taking
into account the generalized approach used for creation of ontology in other languages like
English.
After specifying the domain of ontology, stemming is performed on the document for which
ontology is to be created. As Hindi and Marathi are morphologically rich languages, root word
need to be extracted from the given document. After stemming is performed important terms in
the document are extracted manually. These extracted terms are mainly nouns, adjective and other
modifiers surrounding noun, verbs and its supporting auxiliary verbs. Form the extracted terms
the nouns and verbs are the candidates to be the entity in the ontology and the modifiers
associated with nouns and verbs become property or attributes of those entities. Then finally
relation between entities’ is extracted and stored in the ontology. The root word is useful for
traversing ontology.
Figure 2. Sample Ontology for Mumbai City
After query is provided by user, users question text is passed onto Marathi QA module which
contains tokenization, word grouping, POS tagging, Chunking, Query Triple extraction, Onto
Triple Extraction, Onto matching and fetching of answer.
5. EXPERIMENTAL EVALUATION
In order to show that our proposal can have a great interest and that it can contribute to improve
the performance of the Marathi QA task, we conducted various case studies and developed a
prototype to show that the proposed framework can improve the performance of Marathi question
answering system.
5.1. CASE STUDY
Let us consider the following scenario where user asks the question as:
Marathi Question: मुंबईतील व वध वमानतळांची नाव काय आहेत?
Input question is tokenized to generate tokens from the question, while tokenizing filtering of text
is also performed to remove non Marathi tokens using UTF8 codes.
Tokenized Query:
Token 0 : मुंबईतील

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Token 1: व वध
Token 2: वमानतळांची
Token 3: नाव
Token 4: काय
Token 5: आहेत
After tokenization, related tokens are combined together which on grouping constitute as single
entity.
Word Grouped Tokenized Query:
Token 0: मुंबईतील
Token 1: व वध
Token 2: वमानतळांची
Token 3: नाव
Token 4: काय
Token 5: आहेत
Part of speech is generated for all the extracted tokens as Noun, Quantifier, Intensifier, Verb,
Adjective, etc. are assigned to them.
POS tagged query:
मुंबईतील:: NNP व वध:: JJ वमानतळांची:: NN नाव:: NN काय:: WQ आहेत:: VAUX
Further the process of chunking is carried out to extract noun and verb group from the POS
tagged question. Chunked groups can be in the form of proper noun, common noun and verb
groups.
Chunked query:
[मुंबईतील NNP]:: NNPG [ व वध JJ वमानतळांची NN]:: NNG [नाव NN]:: NNG [काय]:: WQ
[आहेत]:: VAUX
Noun Group1: मुंबईतील NNP
Noun Group2: व वध JJ वमानतळांची NN
Noun Group3: नाव NN
Every question of Marathi language may at least content a subject in it or it can contain
combination of subject object and predicate. Subject object and predicated thus contribute for
generation of query triples in the question.
Query Triple:
काय(मुंबईतील, व वध_ वमानतळ,नाव)
User provided question will not always contain same terms as stored in the ontology for such
scenario semantic mapping of user terms to corresponding onto term is needed. Query triple thus
generated are transformed to onto triple.

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Onto Triple:
काय(मुंबई, व वध_ वमानतळ,नाव)
And finally matching of onto terms of question with those stored in ontology is done which leads
to retrieval of accurate answer for the given question.
Answer extracted from ontology for the given question is:
मुंबईतील व वध वमानतळांची नाव छ पती "शवाजी आंतररा%&'य वमानतळ,जुहू वमानतळ
आहेत.
5.2. EXPERIMENTAL RESULTS
In QA systems it is important to retrieve exact answer or part of the answer that will satisfy the
user question. There are number of evaluation measures that can be used to compare the
performance of the various retrieval techniques. Precision and Recall are the most commonly
used indicators to measure Information extraction quality.
Accuracy, precision and recall are used as performance metrics which can be defined as True
Positive (TP), True Negative (TN), False Positive (FP) and False negative (FN):
Recall = TP / (TP+FN)
Precision = TP / (TP+FP)
Accuracy = (TP+TN)/ (TP+FP+TN+FN)
Marathi QA system accepts questions in simple sentences, analyses them, and returns answers in
a single word, phrase, or sentence Here in terms of Marathi QA system, TP is number of question
correctly answered, FP is number of question wrongly answered, TN is answer present in system
which have no importance to context and FN specify number of answers to question present in
the system but are not retrieved.
The system is evaluated to check whether the answer to the user question is relevant or not.
Mostly QAS either provides relevant answer for the user question or it simply returns null if no
answer is found. It is more like a Hit or Miss System i.e. either we will get answer for a question
or we won’t get answer.
We experimentally evaluated the performance of the proposed framework by testing it with
various Marathi documents of different domains like history, festival, sports, city, politics…etc.
Table 1 shows Contingency table for history domain, where number of questions asked was 55
out of which 51 questions where correctly answered and 3 questions where either not answered or
incorrectly answered.
Table 1. Contingency table for history domain
Total answers Relevant
answers
Non
relevant
answers
Answer Retrieved 51 3
Answers not
retrieved
1 0
Here TP =51, FP =3, TN = 0, FN = 1.
Precision in % = 94.44% Recall in % = 98.07%

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Accuracy in % = 92.72%
Marathi QA system (MQAS) was evaluated for different domains like history, sports, city,
entertainment, politics and festival using the metrics such as precision, recall, accuracy and F-
Measure. Table 2 shows the test results of Marathi QA system for a particular run.
Table 2. Experimental analysis of Ontology based Semantic information extraction system
Sr. No. Input Domain Precision in % Recall in % Accuracy in % F- Measure
1 History 94.44 98.08 92.73 1
2 Sports 93.33 93.33 87.50 1
3 City 100.00 100.00 100.00 1
4 Entertainment 93.33 93.33 87.50 1
5 Political 91.67 91.67 84.62 1
6 Festival 90.91 90.91 83.33 1
Average 93.95 94.55 89.28
The proposed framework’s efficiency is compared with publicly available search engines like
Google and Bing. Table 3 shows domain based accuracy comparison for MQAS with Google and
Bing.
Here we are calculating accuracy of system by taking percentage of answer retrieved for set of
question.
Figure 3 shows average accuracy comparison between MQAS, Google and Bing for various
Marathi language documents.
Figure 3. Overall accuracy Comparison between MQAS, Google and Bing
Figure 4. Domain based accuracy comparison of MQAS, Google and Bing

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Performance of MQAS was evaluated by measuring its ability to retrieve all and only relevant
information. MQAS performance is strongly dependant on POS Tagging and correct processing
of the queries. The system achieved an overall precision of 93.95%, recall of 94.55% and
accuracy of 89.28% and F-Measure as 1.Table 3 describes the performance of MQAS based on
question type. The designed system is tested with 20 different types of question types in Marathi
language. Average Precision of 100.00% shows that all the answers retrieved are correct answers.
Percentage of recall is 97.11%. Factoid and certain non-factoid questions were only considered in
this work. Yes/No questions are not considered in the design of MQAS and hence still it remains
as a research topic.
Table 3. Performance Analysis of MQAS according to Marathi Question Type
Question
Type
Precision
in %
Recall
in %
F
measure
कोण 100.00 80.00 88.89
कु ठे 100.00 100.00 100.00
काय 100.00 100.00 100.00
कोण)या 100.00 90.00 94.74
कधी 100.00 88.89 94.12
कोणी 100.00 83.33 90.91
कोणाचे 100.00 100.00 100.00
कशाचे 100.00 100.00 100.00
के *हा 100.00 100.00 100.00
कोणाबरोबर 100.00 100.00 100.00
कोणती 100.00 100.00 100.00
कशाने 100.00 100.00 100.00
कसा 100.00 100.00 100.00
कु ठला 100.00 100.00 100.00
कशी 100.00 100.00 100.00
,कती 100.00 100.00 100.00
कोणकोणती 100.00 100.00 100.00
कोणकोणते 100.00 100.00 100.00
कोणता 100.00 100.00 100.00
कोणते 100.00 100.00 100.00
Handling of ‘कसं’ and ‘का’ type questions are the most difficult because they mostly require
answers spreading over more than one sentence or paragraph. These questions sometimes require
deep semantic processing of the sentences and identification of more keywords to detect the
presence of explanations, intentions, justifications etc.
6. CONCLUSIONS
The system is tested with Marathi documents of various domains like History, sports, festival,
politics.etc and shows an overall precision of 93.95%, recall of 94.55% and accuracy of 89.28%.
The designed system is also tested with 20 different types of question types in Marathi language
for ex: “कोण, कु ठे, काय, कोण)या, कधी, कोणी, कशाचे, के *हा, कसा, कु ठला, कशी, ,कती “and system
shows precision of 100.00%, recall is 97.11%. Factoid and certain non-factoid questions were
only considered in this work.

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The proposed system is compared with publicly available search engines like Google and Bing.
The system shows average accuracy as 93.66%, 44.61 and 29.82% for designed MQAS, Google
and Bing respectively.
7. FUTURE SCOPE
At present, domain specific ontology construction is a manual task. No tool is available till date
for automatic ontology construction for Marathi language. The future enhancement to the current
methodology is to build the ontology automatically by using a tool. Automated tool can be
developed to minimize the manual intervention in QA process.
In spite of significant contributions made by proposed system, there are number of research
avenues which can be taken up in future. The dataset considered under study was very small in
size and also for very few domains of Marathi language. In future, system can be tested with large
dataset. Factoid and certain non-factoid questions were only considered in this work. Yes/No
questions are not considered in the design. The research can be further extended for handling of
‘कसं’ and ‘का’ type questions which are most difficult type questions as these questions require
deep semantic processing of the sentences to extract answer.
The systems can be scaled to cover much more domains and support of more complex natural
language queries in the future.
ACKNOWLEDGEMENTS
With deep sense of gratitude I express my sincere thanks to my esteemed and worthy supervisor
Dr. J.W.Bakal for his valuable guidance in carrying out this work under his effective supervision.
My greatest thanks are to all who wishes me success and supports me to complete this.
REFERENCES
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[2] Stalin, Shalini, Rajeev Pandey, and Raju Barskar. "Web based Application for Hindi Question
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[3] Kumar, Praveen, et al. "A query answering system for E-learning Hindi documents." South Asian
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AUTHORS
Sharvari Govilkar is professor in Computer Engineering Department, at PIIT, New Panvel
and was research scholar at TSEC, Bandra , University of Mumbai, India. She has received
her M.E in Computer Engineering from University of Mumbai. And completed her Ph.D. in
Information Technology from University of Mumbai. She is having twenty years of
experience in teaching. She has publications in various national and international journals &
conferences. Her areas of interest are Text recognition, Natural language processing, Information Retrieval,
domain specific ontology construction etc.
J. W. Bakal received M.Tech in Electronics Design & Technology, from Dr. BAMU
University. Later, He has completed his Ph.D. in the field of Computer Engineering from
Bharati University, Pune. He is a PhD supervisor in CSE at University of Mumbai. He is
presently working as principal at the S.S. Jondhale College of Engineering, Thane, India. He
was a chairman of board of studies in Information Technology in University of Mumbai. His
research interests are Telecomm Networking, Mobile Computing and Information Security. He has
publications in journals, conference proceedings, and books in his credits. During his academics tenure, he
has attended, organized and conducted training programs in Computer and Electronics branches. He is life
member of professional societies such as IETE, ISTE INDIA. He is also a member of IEEE. He has
prominently worked for IETE as a chairman, Mumbai section.

QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGE

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QUESTION ANSWERING SYSTEM USING ONTOLOGY IN MARATHI LANGUAGE