Abstract

The vast scale information available online is a rich source of collective human knowledge. The major transition from conventional information sources such as books and archives to online sources such as web pages, etc. has introduced the need to categorize, organize and mine the digital corpus since the advent of the internet age. Immense effort has been put into this direction since the last decade or so resulting in a lot of improvement in mining conventional documents. One of the major improvement in better understanding of textual documents has been because of Topic Models such as LDA (Latent Dirichlet Allocation) which are used to learn and highlight abstract predominant topics in the documents. However, more recently, due to advances in technology, new age social media platforms have equipped their users to communicate, network and share knowledge but changed the way information is being generated, propagated and consumed. It has not only affected the means of information sharing but also the characteristics of the documents in terms of their language, style, size and vocabulary. The document size and style pose several challenges to conventional data mining tools to be used to their full potential because they rely on document level word co-occurrences to learn important topics in the corpus. They need larger documents compared to a “typical” social media post. In this thesis, we focus on improving the performance of topic models for contemporary documents and text corpus generated in the form of social media posts. We discuss and propose an improvement to previous efforts in the direction and also introduce a new algorithm, sentence2cluster, which also helps in document categorization and organization. Our first proposition to improve the topic models is an extension of previous efforts, which mainly focuses on prepossessing of the documents in terms of document pooling or aggregation. A popular aggregation scheme is user-based document pooling, where the underlying assumption is that a person has an interest in a limited number of topics, therefore, it is useful to club all his documents into one big document. We extend this approach and propose to aggregate documents not only for a single user but also for users who share similar interests. We use many indicators such as the use of hashtags, communication amongst users, influences, etc. to identify a community of users who can pool their documents to learn a potentially better topic model. This community-based pooling instead of a single user based pooling not only increases the context and document size but also helps highlight niche topics which are popular only among a small group of people who are not as loud as other users. We run our experiments on Flickr datasets where we find communities of users who can pool their documents. We use network information and many user attributes to find such communities. We show that topics learned after community-based document aggregation are more coherent than other previously proposed document aggregation schemes. The second effort in the direction of improving Topic Models is the use of word embeddings. Topic modeling algorithms such as LDA do not use the information hidden in the order of the words in a sentence. In our new approach sentence2cluster, we use this information to learn meaningful word representations, followed by clustering them. The word clusters represent the broader corpus level topics which are assumed to be found in documents with different proportions. The idea to learn corpus level topics first and then infer their presence in every document is inspired from Bi-Term Topic model (BTM), which also focuses on improving topic models for short text but does not utilize information hidden in word ordering in a sentence. We evaluate the utility of the topics learned through our approach by building a navigated corpus browser on real world documents posted online on various social media platforms and forums. We then conduct a survey seeking user experience and to get a feedback on interpretability and coherence of topics and effective utility of our approach. The results indicate the potential in using word level semantics for topic modeling for short textual documents and the positive feedback that supports our hypothesis. Our work presents an elaborate discussion on various aspects of Topic Modeling and how they can be improved. We focus both on pre-processing steps using better aggregation schemes and on leveraging information hidden in sentence structure and word level semantics. In this manner, we hope that it drives fruitful research in various tasks of information categorization and organization in the future by drawing attention towards better Topic Models for contemporary texts.

Abstract

Fast side chain conformational dynamics play a vital role in the biological functions of proteins. High-resolution nuclear magnetic resonance (NMR) studies continue to evolve in important ways to elucidate the structure, dynamics and thermodynamics of proteins in natively folded, unfolded, invisible excited states, and in ligand-bound complexes. NMR spectroscopy uses methyl spin probes of methyl-containing residues to characterize the sitespecific side chain motions and to establish a molecular-level connection between the dynamics and thermodynamics of proteins. Since the side chain motions and thermodynamics are governed by the underlying conformational energy landscape, it is essential to understand the interrelationship between protein energy landscape, protein-mediated biological processes, side chain dynamics and their responses to external perturbations. The major aim of this thesis is to investigate the origin, diversity, functional roles of the fast side chain motions of proteins and their responses to external perturbations (ligand-binding and pressure) using molecular dynamics simulations. The universal features of fast side chain motions in proteins are characterized by examining the conformational energy surfaces of individual residues obtained using enhanced sampling molecular dynamics simulation. The free energy barriers separating side chain rotamer states follows a trimodal distribution. A hierarchical grading of rotamer states based on the conformational free energy barriers, conformational entropy (Scon f), and probability flux revealed three distinct classes of side chains in proteins. A unique nonlinear correlation is established between NMR order parameters (O 2 axis ) and the side chain rotamer populations (ρ). The apparent universality in O 2 axis versus ρ correlation, trimodal barrier distribution, and distinct characteristics of three classes of side chains observed among proteins with different secondary structures and molecular weights indicated a hidden regularity (or commonality) in the dynamical heterogeneity of fast side chain motions in proteins. The influences of ligandbinding, pressure and noncovalent interactions on the fast internal motions of proteins are examined. The derived correlations between O 2 axis, Scon f and strengths of noncovalent interactions reveals that side chain flexibility and conformational entropy decrease with increasing 9 strength of its noncovalent interaction with surrounding evironment in a sigmoidal-like fashion for all residue types. The results also showed that there exists a critical range of noncovalent interaction strength for each residue type determined primarily by the mean side chain conformational barriers, beyond which side chain flexibility and conformational entropy are insensitive to noncovalent interactions. Using fundamental statistical mechanical principles, an exact relationship between noncovalent interaction strengths, the relative stabilities of side chain conformational states and rotamer barriers of protein side chains is derived. Using calmodulin (CaM) and catabolite activator protein (CAP) complexes as model systems, the site-specific ligand-induced changes in thermodynamical properties and side chain dynamics of proteins are investigated. The distribution of rotamer barriers follows a trimodal distribution both in ligand-free and ligand-bound states of CaM and CAP. The distributions of changes in O 2 axis, for all complexes followed a Gaussian-like unimodal distribution and the width of the distribution varied among different complexes. Further, the side chains with O 2 axis ≈0.3 are observed to be least perturbed for all methyl-containing residues. A non-linear correlation between O 2 axis and the side chain rotamer population (ρ) and linear relationship between O 2 axis and conformational entropy (Scon f) are observed even in the presence of significant site-specific perturbations in protein complexes. The investigation on the effect of external pressure on the fast side chain dynamics reveals a heterogenous distribution of side chain dynamics and local compressibility of proteins. Structural characterization of ubiquitin at different pressures (0.001- 2.5 kbar) indicated that even at 2.5 kbar pressure it remains in its native folded state. The present thesis has illustrated the use of advanced enhanced sampling methods and molecular dynamics simulation to gain new molecular-level insights into fast conformational dynamics and to relate side chain dynamics and thermodynamics of proteins to the underlying conformational landscape.

Abstract

We tackle the problem of reconstructing moving vehicles in autonomous driving scenarios using only a monocular camera. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in emph{shape priors}, which are learnt over a small dataset comprising of annotated RGB images of vehicles. Each shape prior comprises of a deformable wireframe model whose vertices are semantically unique emph{keypoints} of that vehicle. The first contribution is an approach for reconstructing vehicles from just a single (RGB) image. To obtain a 3D wireframe representing the shape, we first localize the vertices of the wireframe (keypoints) in 2D using a Convolutional Neural Network (CNN). We then formulate a shape-aware optimization problem that uses the learnt shape priors to emph{lift} the detected 2D keypoints to 3D, thereby recovering the 3D pose and shape of a query object from an image. The shape-aware adjustment robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches. The second contribution is a real-time monocular object localization system that estimates the shape and pose of dynamic objects in real-time, using video frames captured from a moving monocular camera. Here again, by incorporating prior knowledge of the object category, we can obtain more detailed instance-level reconstructions. As opposed to earlier object model specifications, the proposed shape-prior model leads to the formulation of a Bundle Adjustment-like optimization problem for simultaneous shape and pose estimation. We demonstrate how these keypoints can be used to recover 3D object properties, while accounting for any 2D localization errors and self-occlusion. We show significant performance improvements compared to state-of-the-art monocular competitors for 2D keypoint detection, as well as 3D localization and reconstruction of dynamic objects

Abstract

Competent software engineers will produce quality software systems. Lack of such qualified software professionals has far reaching consequences and hence is a major worry for both academia and industry worldwide and especially in India. Educators are trying to create high impact learning environments that can address this specific issue. The general consensus among educators and learners is that there is a gap between theory and practice of Software Engineering (SE), addressable by incorporating more practice oriented instructional approaches in the learning environments, the conceptual embodiments of learning and teaching. The very idea has merit. However, instruction design for SE is not simple due to presence of several impacting factors, situated in the nature of software, Software Engineering, academia and software industry. Sustainability and cultural suitability are other factors to be considered. Since SE learning environments are high impact systems, their design and evaluation should be conscientious exercises, strongly supported by theory and practice, utilizing holistic yet systematic approaches. The primary goal of this work is to develop an effective and sustainable Software Engineering learning environment for Indian conditions. To address this quintessential problem, we followed a Design Based Research (DBR) approach that is suitable for educational research and exhibits many similarities with typical Software Engineering process. We examined the interdisciplinary domain of Software Engineering Education (SEEd) to identify factors impacting SE education in the Indian context. Accordingly, requirements related to learning, and of Differentiating nature were identified. Analysis suggested that the Differentiating requirements such as scalability, administrative usability, practiceorientation, motivation, feedback, pedagogical maturity, resource efficiency etc. exhibit significant interactions and determine the pedagogy design and validation in a learning environment. We could even classify the Differentiating requirements as Systemic and Climatic. We designed a learning model that can effectively and easily bring industrial knowledge and experience to the classroom, thus bridging the industry-academia gap. The proposed Case Oriented learning environment for Software Engineering Education (COSEEd) uses a case based instructional approach, with structured problem solving at its core. Essentially, challenges in well-designed case studies engage students in authentic SE activities for solving authentic, contextualized problems. Students achieve the learning goals through the process of understanding and solving a given SE case study. The assessment activities are in tight integration with the instruction, thus creating an environment conducive for learning. This layered system embeds multiple learning theories and design principles from Educational Technology to support the right set of qualities. To improve the specificity, accuracy and granularity of analysis, we developed a formal validation approach named as Requirement Satisfaction Index (RSI). RSI detects and measures the presence of requisite qualities, including learning at various cognitive levels. RSI is a novel way of thinking towards design and evaluation of learning environments in terms of needed capabilities or requirements of a learning environment. Results exhibit that COSEEd, the case study oriented learning environment is highly suitable for the Indian context. It imparts effective learning, is scalable, mature and flexible to vi accommodate the dynamic nature of SE. Several studies were conducted to examine the effectiveness of COSEEd. Results from these studies indicate the merit of the proposed learning environment on several cognitive measurements of learning, Climatic qualities and Systemic qualities. The model consistently scores high on learning objectives with higher-level cognitive goals. COSEEd’s advantages are visible in the Climatic requirements especially practice-orientation, use of authentic problems, interesting, and motivating. For Systemic requirements, the model was evaluated as scalable, flexible, mature, and administratively usable, though not resource efficient. We developed supporting technology and learning material in form of a set of case studies based on real projects from the Indian software industry. We identified mechanisms to impact learning by manipulating case studies and learning activities. Our approach differs from existing work in many ways. It is India specific, but is generalizable. We take a holistic approach towards the learning environment and consider both learning effectiveness and sustainability. Requirements of the environment are gathered after a thorough examination of Indian academic and industrial scenario as well as nature of the discipline. The design is rooted in several learning theories and design principles from

Abstract

Gold clusters have attracted a lot of interest due to the discovery of their unique catalytic properties and their unique optical properties that give them great potential for medical applications. Small gold clusters serve as models for understanding the molecular processes taking place during catalysis. Along with pristine clusters, role of doped clusters has also gained an intense interest. Strong modulation of the physiochemical properties of metal clusters due to doping has made doped clusters potential candidates in sensing, catalysis and medicine industries. This needs a detailed understanding of the structural and physicochemical properties of the system which are in turn dependent on the electronic properties. In this work, the perturbation of doping atoms, belonging to the alkali and alkaline earth metal groups, on the Au10 cluster has been unraveled by studying the electronic structure and stability of these metal clusters. Our density functional theory based calculations show that a planar to 3D transition can be induced in Au10 by doping it with a dopant of large atomic radius. These dopants lead to electronic modulation of the cluster resulting in better charge transfer potential which is quantified by analyzing the HOMO-LUMO gaps, vertical electron affinity and ionization potential of the pristine and doped cluster. An attempt to locate the energetically minimum structures in case of Aun clusters (n=2- 9) was also carried out using density functional theory and our recently developed genetic algorithm code coupled with GAMESS. PBE0 functional and high quality basis set were employed for carrying out all the calculations related to geometry and electronic modulation of small gold clusters. The main motive of this study was to study the change in properties of gold clusters as size of cluster varies and also cover more sample space and get the minimum energy structures in very less time. So we experimented it with genetic algorithm. This study attempts to provide an insight into how structural and electronic properties vary according to different-sized gold clusters and also portrays that doped clusters can be better potential candidates for use as heterogeneous catalysts, where the dopants serve as tools to tune the catalytic efficiency of the system.

Abstract

Melodic music is a linear succession of musical tones which creates an aesthetic stasis of musical flow in our mental realm. Melody can be analysed with physical and mental concepts like frequency, pitch etc and is perceived as one single unit creating distinctive harmonious ensemble of states in our mind. It can be visualised as a unique concoction of perceptual constants (notes) and combination of notes. The thesis makes an attempt to analyse the consonance of the unique combinations of perceptual constants in the context of Indian Classical Music. In Indian Classical Music, svara-sth ̄ ana-s (notes) are ‘seen’ by musical inner eye as perceptual constants. Perceptual constants in r ̄ aga (melody-class) are analyzed with concepts of v ̄ adi (sonant), samv ̄ adi (con- sonant), anuv ̄ adi (assonant), viv ̄ adi (dissonant), ̄ aroha (ascent sequence), ̄ avaroha (descent sequence), pakad (grip sequence), ch ̄ alan (stylized path sequence), saragama (composition) and more. We have created a database of about 163 r ̄ aga-s with these concepts as given in the book Kramik Pustak Mallika by Bhatkhande. Any melody creates sth ̄ ayi bh ̄ ava which is a perceptually harmonious aesthetic-state collectively created by the sequential cohabitation of svara sth ̄ ana-s or perceptual constants of melody. We build a model which consists of combinatorial aspect of perceptual constants to analyse sth ̄ ayi bh ̄ ava through the concept of ‘consonance’ in Indian Classical Music. We study the consonance of musical tones in Indian Classical Music using the phenomena of ‘Local consonance and dissonance of tones’ by Dr. William Sethares. The idea is to come up with concept of consonance for exploring harmonic possibilities through the implementation and further refinement of the consonance theory. Our research examines how sounds with different kinds of spectral relationships work together to derive a schema of harmonic consonances in musical space to arrive at consonance atlas of musical sounds. To come up with consonance atlas of Indian Classical Music, we perform the analysis by consider- ing various ‘granularities’ of musical tones. We begin with a single interval between tones and then expand it to combination of tones taken together and come up with consonance analysis of musical tones. On the other hand, we take combination of tones and decompose it further into microtones and perform consonance analysis. We arrive at a model to study consonance of musical tones, microtones and ambient sounds. Further, we also arrive at a concept of ‘consonance measure’ which can be used vi vii for objective assessment of musical tones. The outcome of the study we got not only validated the harmony of note combinations in 163 r ̄ agas as outlined by Bhatkhande but also resulted in harmonious v ̄ adi (sonant), samv ̄ adi (consonant), anuv ̄ adi (assonant) and viv ̄ adi (dissonant) structures which are not prevalent in contemporary practices and the- ory. We have performed the analysis by taking two tones and expanded to combinations of tones taken together. Our analysis has resulted in 69 consonant two note combinations out of which only 26 pairs have been traditionally believed to exist. By expanding it to combinations of tones, we arrive at thou- sands of combinations of tones which are consonant, thereby creating a new space for composition. We also perform consonance analysis of microtones. We study the phenomenon of microtones ( ` sruti) using the concept of critical bandwidth and ‘conso- nance measure’. The consonance analysis of microtones is compared with the traditional model of ` sruti in Indian Classical Music. We arrive at a ` sruti model with 24 consonant microtonal notes using Just Intonated and Equi Tempered scales and make several inferences. Using the framework of consonance measure, we also make an attempt to study the relation between r ̄ agas and environmental sounds. Every r ̄ aga has an interesting relationship with various ambient prop- erties like time of the day, season etc. By our analysis, we make an attempt to validate the traditionally existing practice of r ̄ agas and their time/season in which they are played and also predict the association of r ̄ agas whose relation with ambient sounds is not mentioned traditionally. The model produced an accuracy of 78% for about 128 r ̄ agas and 1000 ambient sounds taken together. Our model is one of its kind and opens up the interesting possibility of sound art involving ambient sounds and musical sounds

Abstract

Trust is a complex human attitude that forms a vital, yet common component of nearly every aspect of human social behaviour. As a concept, it has been studied in various disci- plines of humanities in a myriad of different ways, while its use in computer science domains has met with limited degrees of success. Over the years, it has been observed however, that trust-aware models offer relevance, transparency and usability in real-world applications. With computational systems becoming increasingly intelligent, and web-based social networks hav- ing become ubiquitous in today’s global society, contemporary new dimensions of trust have emerged. The transformation of the internet into a social web has made it important to under- stand what trust means in the online world. In this thesis, we aim to extend a formal theory of trust in the context of social media (in an attempt to bridge the gap between its understanding in the humanities and computer science) and explore its viability in designing better socio- technical systems. We first detail the intricate nature of trust based on theories in philosophical literature. We discuss how trustworthiness of the trustee, risks, expectations of the trustor, motives, incentives and skills factor into the attitude that is trust. We discuss ontological and epistemological per- spectives such as where trust exists, how do we know what we trust and where its value comes from. We also study its relation to the utilitarian moral theory to see how we can maximize benefits through good actions. Grounded in these ideas, we argue for the important role played by it in the effective working of large-scale social networks. We outline a novel multi-class taxonomical framework comprising of personal, social and functional elements of trust. We identify how individual traits of users, collective behaviours of communities and the structural capabilities of the technologies, all contribute to the creation of trust. We expound on the in- tricate relationships between these three elements and show how institutions, digital semiotics and value-aligned technologies form strong theoretical and philosophical underpinnings in the design of these systems. vii viii Having built the framework, we then depict its feasibility and relevance to real-world sys- tems through a case study of Wikipedia. We investigate how the different elements of trust manifest themselves in Wikipedia. We illustrate themes such as purpose, content, systems de- sign, tasks, etc. and demonstrate the usefulness of the taxonomy in understanding where and how trust originates, and why readers and editors alike have faith in Wikipedia. And lastly, we use elements of the framework along with machine learning and tensor factorization meth- ods to propose a novel, hybrid, trust-based recommendation model to suggest a personalized list of items to users. We present experimental evaluations of our model on a dataset of TED videos and show that it outperforms standard existing collaborative filtering and trust-based approaches. Moreover, subjectively speaking, it scores high on user satisfaction based on pop- ularity, diversity and freshness

Abstract

Geographical Information Systems(GIS) is a powerful, ubiquitous tool that can be seen in various domains of today's fast-moving and highly technological landscape. With the rising needs and uses of GIS data visualization system, such as smart city planning, environment and climate change monitoring, weather simulation, transportation and so forth, the Geospatial vec- tor visualization research, application and technology has become prevalent. Large amounts of data can be visualized on an interactive platform which is one of the most appealing and useful abilities of GIS. High powered computers can visualize large data sets, change element of the display easily, give any visual effects for better view and also make suitable interpretations. Recent Geospatial data visualization systems have been predominantly through applications that are installed and run in a desktop environment. The drawbacks with the desktop application model is that it is tightly coupled to the desktop environment attributes like operating system and hardware of the system. Desktop applica- tion has the limitations to be used on multiple devices like smartphones, tablets and personal computers simultaneously. Also, it lacks the scope to collaborate with the other users. To solve the above problems a client-server model has been adopted where data on a single sys- tem can be accessed, manipulated and visualised on different clients without any environment dependencies. Over the last decade, this server-client model for data handling, data rendering and visualization has been the most prevalent approach in WebGIS. But the above paradigm of WebGIS gives limited capabilities and functionalities to the client to handle the Geospatial data making it dumb. Although the client-side technologies have become functionally more powerful over the recent years, the above model has largely ignored it and is still in a mode of server-dominant computing paradigm. Given the current scenario of the GIS visualization systems, an attempt has been made to develop and demonstrate LSIViewer - a simple, easy and robust online Geospatial data visuali- sation system for the user's own data that harness the client's capabilities for data rendering and user-interactive styling, with a reduced load on the server. The technologies chosen to build this system makes the application extensible and scalable. The architecture of LSIViewer is based on client-server model where role of the server is to provide the instance of the client ap- plication, act as an adapter and convert the input vector data into a GeoJSON format, compress the GeoJSON encoded data and send it as a response to the client. GDAL/OGR, a popular open source format translator GIS library, is used in conversion of vector format to GeoJSON. The transmission of GeoJSON data between client and server is optimized by reducing the information size using efficient compression method that is supported in all the web-browsers. The client decompresses the encoded response and render the map on HTML5 canvas via JavaScript. The performance analysis on the developed system included various tests that were run on a range of vector datasets. These tests showed that the time taken to render the vector data using LSIViewer is comparable to a desktop GIS application, QGIS, over an identical system con- figuration. All the web browsers that support HTML5 give the capability of rendering vector data to the client, making the developed application a platform-independent and installation- free toolkit. This indicates that any device with a web browser has the capability to visualize vector data without the need of a full-scale GIS desktop application. Eventually, the developed system can be extended to build an enterprise level collaborative GIS platform for users and developers to share and analyze their data. This online visualization tool is just an inception, opening the gates to a whole new horizon of things that can be possible through WebGIS.

Abstract

The subset sum problem, also referred to as SSP, is an NP-Hard computational problem. SSP has its applications in broad domains like cryptography, number theory, operations research and complexity theory. The most famous algorithm for solving SSP is Backtracking Algorithm which has exponential time complexity. Therefore, our goal is to design and develop better alternate enumeration techniques for faster generation of SSP solutions. Given the set of first n natural numbers which is denoted by X n and a target sum S, we propose various alternate enumeration techniques which find all the subsets of X n that add up to sum S. In this thesis, we present the mathematics behind this exponential problem. We analyze the distribution of power set of X n and present formulas which show definite patterns and relations among these subsets. We introduce three major distributions for power set of X n : Sum Distribution, Length-Sum Distribution and Element Distribution. These distributions are prepossessing procedures for various alternate enumeration techniques for solving SSP. We propose novel algorithms: Subset Generation using Sum Distribution, Subset Generation using Length-Sum Distribution, Basic Bucket Algorithm, Maximum and Minimum Frequency Driven Bucket Algorithms and Local Search using Maximal and Minimal Subsets for enumerating SSP. We compare the performance of these approaches against the traditional backtracking algorithm. The efficiency and effectiveness of these algorithms are presented with the help of these experimental results. Furthermore, we studied the extra subsets generated by various algorithms to get the complete solution for subset sum problem. Finally, we present a conjecture about upper bound on the number of subsets that has to be enumerated to get all solutions for Subset Sum Problem.

Abstract

A Question Answering system constructs its answers by querying a data resource. In this thesis, we develop an architecture for a multi-lingual dialog based QA system using PurposeNet based Ontology as the data resource. We have handled two unrelated domains, one of which is an open domain (recipe) while the other is a closed domain (MMTS) and the QA system is reusable with few domain based changes. In this cross-lingual setup we have considered Hindi as the user language (question-input and answer-output are in Hindi) while the Ontology is written in English language. We discuss the creation of Ontological resource in both the domains. The Ontological structures for both domains was highly influenced by PurposeNet architecture. Data for both the domains exhibit a different characteristic. While a major chunk of the MMTS domain data could be easily extracted by automated means, extracting recipe domain data was a layered approach. The action and artifact Ontology of the MMTS domain are created separately, artifact Ontology is created using automated means while action Ontology is manually populated. For recipe domain, we populated the resource using recipe blog data and then executed a post-processing step, to bring a more unified structure to the Ontology. Question analysis is a very important aspect of a QA system. We have tried to semantically and structurally classify questions in Hindi. Structural classification helps us in parsing the questions correctly and hence identify the question arguments correctly. Semantic classification helps us in mapping the question arguments to the Ontology and hence extract the answers from the data resource. We also study polysemous question types (kya and kaise) in Hindi. These questions are partly tackled by the dialog system which is integrated in the QA system. We give an architecture of a QA system using PurposeNet based Ontology created in OWL, with Pellet reasoner (for data inference). The design of the QA system ensures a multilingual architecture and domain independence. After understanding the question type and its requirements, we create Sparql queries, answers of which are extracted from the knowledgebase. We have handled all kinds of question types giving a major emphasis on non-factoid questions including why and how. The structure of PurposeNet helps in identifying the answers to these questions effectively. We provide a graph based algorithm for answering these question types efficiently. As said earlier we also support the QA system with a dialog system which can interact with the user before, during or after a question answering cycle. If a QA system finds an ambiguity, incompleteness or incorrectness in a question, it asks for user interference using the dialog module. We maintain the state of the question and start processing from this state on user’s input to efficiently process questions faster. To test the premise of domain-independence of the QA system, we first create it for MMTS domain and then extended to recipe domain. Testing against gold data gives us a decent accuracy of 76.2% and 63.75% for MMTS and recipe domains respectively. The answers we receive from the QA system is cross-verified by human evaluators on correctness and readability. The results are extremely promising with scores of 2.32/3.00 and 2.25/3.00 respectively for both the domains.