Abstract

Microblogging services like Twitter enable communication at a massive scale. It has been recently reported1 by Twitter that every month, 248 million users access the microblogging platform, and create around 500 million posts everyday in more than 35 languages. This tremendous growth gives an opportunity to mine useful information that is being shared across social media. However, there exists a 140 character limitation on the posts (also known as “tweets”) that users can create on Twitter. This results in heavy use of emoticons, abbreviations, misspellings and has lead to various linguistic “innovations” that render the traditional text analysis techniques less effective. Another interesting aspect of such tweets is the usage of semantico-syntactical constructs called “hashtags”. Hashtags are “#”-prefixed keywords used by people in order to organise the meaning of their tweets. Also, hashtags enable classification of tweets, since the posts using same or similar hashtags are expected to be semantically related to each other. The challenge posed by hashtags is the fact that most hashtags are not simply “#”-prefixed keywords, but “#” symbol prefixed with concatenation of various words or phrases which are not space delimited. For example, consider the hashtag - “#NSAvsSnowden”. We observe that this hashtag is essentially “NSA vs Snowden”, which is not a single keyword, but a concatenation of various words. In this thesis, we discuss and compare various approaches in order to “segment” the hashtag into meaningful words. Also, our task extends beyond just the segmentation of hashtag - we present a unified framework to also perform “entity-linking” on various constituent entities in a hashtag. Entity Linking is an established IR task, where the goal is to extract latent semantics from plain text by linking the text to a knowledge base (KB) such as Wikipedia. Consider, for example, the following text - “Snowden reveals classified information from NSA”, we first need to identify various entities in this piece of text, followed by “disambiguating” them, and establishing a link between those entities and some knowledge base (KB) so that we have additional contextual information available about the concerned entity. This approach has been found to be instrumental in order to teach the meaning of text to machines, which is otherwise meant for human consumption. Hence, after performing entity linking on the segmented hashtag - “Snowden vs NSA”, we would have enriched the text with additional semantic information by establishing links between “Snowden” and corresponding Wikipedia page - “Edward Snowden”, between “NSA” and “National Security Agency”. “NSA”, in principle, could also refer to “National Sports Academy” or “National Security Act”, and this is exactly where “disambiguation” becomes important for mention-resolving, which employs contextual information to perform this task. Since hashtags are human curated labels associated to tweets, our premise is that segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. Traditionally, most of the IR tasks have treated hashtags as either a single word, or have ignored them for all practical purposes. We demonstrate how extraction of semantics from tweets improved, when additional semantic information was made available by our system by segmenting and entity-linking hashtags. We demonstrate this by performing various experiments on NEEL Challenge Dataset, and a human annotated subset of Stanford Sentiment Analysis Dataset, which has also been made public to ease future research in this area. We have achieved the P@1 score of 0.914 on NEEL Dataset and 0.873 on the manually annotated Stanford Sentiment Analysis Dataset for hashtag segmentation and linking. We also showcase how our approach leads to improvements in the task of “Semantic Microblog retrieval” and “Semantic Hashtag retrieval”. Microblog retrieval refers to retrieval of a ranked list of microposts given a query Q. Hashtag retrieval, on the other hand is a relatively newer IR task. It basically refers to retrieving a ranked list of the top-k hashtags relevant to a user’s query Q. To retrieve information related to a user’s interest, for instance, “Rock concerts”, it’d be very helpful to the user if they can be suggested a list of hashtags which are commonly used in relation to “Rock concerts”. By tracking these hashtags, a user can gain information about rock concerts via the posted tweets. However, it’s not possible for the user to manually figure out all the hashtags that are used across Twitter, relevant to their interest. In this thesis, we also address this problem. In order to solve these two retrieval problems, we propose and discuss a virtual docume

Abstract

One of the DNA lesions formed due to free radical damage of thymine is urea, occurrence of which in DNA blocks DNA polymerases, and has been shown to be lethal. Recently, it has been shown that urea is capable of forming hydrogen bonding and stacking interactions with nucleobases, which are responsible for the unfolding of RNA in aqueous urea. Base pairing and stacking are inherent properties of nucleobases; since urea is able to form both, this study attempts to investigate if urea can mimic nucleobases in the context of the nucleic acid structures by examining the effect of introducing urea lesions complementary to the four different nucleobases on the overall helical integrity of B-DNA duplexes and their thermodynamic stabilities using molecular dynamics (MD) simulations. The MD simulations resulted in stable duplexes without significant changes in the global B-DNA conformation. In agreement with experimental results, the urea lesions occupy intrahelical positions by forming hydrogen bonds with nitrogenous nucleobases. Further, these urea lesions form hydrogen bonding and stacking interactions with other nucleobases of the same and partner strands, which are sometimes similar to the nucleobases in typical B-DNA duplexes. Direct hydrogen bond interactions are observed for the urea-purine pairs within DNA duplexes, whereas two different modes of interactions, namely direct hydrogen bonds and water-mediated hydrogen bonds, are observed for the urea-pyrimidine pairs. The latter explains the complexities involved in interpreting the experimental NMR data reported previously. Binding free energy calculations were further performed to understand the thermodynamic stability of the urea incorporated DNA duplexes with respect to pure duplexes. This study suggests that urea potentially mimics nucleobases by pairing opposite to all the four nucleobases and maintains the overall structure of the B-DNA duplexes.

Abstract

The amount of information available on the web is increasing rapidly. The number of internet users is also increasing every day. A significant section of internet users is monolingual. They want to express themselves in their native language and also seeking information in the same. Hence, multilingual content over the internet is also increasing at a rapid pace. There is a need of systems which empowers such internet users to let them express themselves in the language of their choice and can provide them answers to information requests irrespective of the language barrier. Cross-lingual term associations are very important for many interlingual applications. Machine Translation system is a sub-field of computational linguistics which aims at translating text from one language to other. All MT systems at the core depend on bilingual dictionaries. The bilingual dictionaries are important resources for such NLP applications as statistical machine translation and cross-language information extraction systems. The bilingual dictionary is one such important resource where entries are word translations. They also can serve to enhance existing dictionaries, for second language teaching and learning. Manually created resources are usually more accurate and do not contain noisy information, in contrast, to automatically learned dictionaries. Scientific community seeks for the methods to achieve similar accuracy level and broader terminology scope by automatic means. In this thesis, we try to address the problem of generating bilingual dictionaries automatically. We propose two different approaches to generating bilingual dictionaries for English-Hindi pair. Both the approaches proposed in this thesis are language independent and hence can be used to build dictionaries for other language pairs which are phonetically rich. Hindi is very under-represented on the web because of many technical and socio-cultural reasons. Many languages, like Hindi, suffer from the limited availability of the language resources/ tools. It is crucial to develop a language-independent approach for such languages. In this thesis, though we have shown our experiments for English and Hindi language pair, our approach can be easily extendable to other language pairs as well. We didn’t use any language specific resource in the approaches which we proposed here. We have chosen two of the most reliable document resource for English-Hindi pairs viz. Wikipedia and news streams. We use Wikipedia as comparable corpora and ever growing news corpus as Quasi Comparable corpora. In the first approach proposed, we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. We proved by applying our approach on Comparable corpora containing documents having structural properties. Documents are inherently structurally divided into many sections and subsections. We link these sections to corresponding in the other language using a statistical approach. These mini sections are further divided into comparable sentences. After using some basic NLP techniques, we chose the most co-occurring pairs, which are a candidate for being a dictionary pair. One of the major contributions of this approach is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. The approach proposed here is language independent. In the other automatic method, we proposed exploits continuous quasi-comparable corpora to derive term level associations for the enrichment of dictionaries generated by the first approach. The approaches uses easily accessible (i.e, comparable and quasi-comparable) data sources to derive dictionary entries. The News is one such ever generating quasi-comparable corpora. Using this approach on a news dataset new vocabulary can be added to the dictionary. According to the same taxonomy, we try to link news stories across languages with the same focal event which might serve as comparable corpora. Afterwards, we apply dictionary generation algorithm to extract bilingual dictionary. Our method is completely automatic and does not rely on any labelled information. We compare the dictionary generated by our method to the dictionary generated via the first approach. In this thesis, we show this approach is able to derive interesting term level associations across languages. The dictionary generated from the Wikipedia is of high quality because of the highly structured information like title, section, sub-section etc. and interlingual links. We demonstrate that our method is able to find some associations which are not present in the Wikipedia using this approach.

Abstract

Parsing natural scenes into semantically meaningful entities is one of the open problems in computer vision. Due to complexity present at various levels ranging from scene-object, object-object and objectlayout, the problem becomes challenging. Restricting the problem to indoor scenes makes it slightly easier to track. Holistic understanding of indoor scene involves detection of object, recovery of 3D geometry of the object, estimating spatial layout of the scene and classification of the indoor scene. It helps in executing the high-level task such as navigation, free space estimation, object placement and manipulation. In this thesis, we integrate the information at the various levels in the cluttered indoor environments for efficient semantic segmentation. We use appearance and geometric properties of different entities for estimating free space and localising objects in the given indoor scene. We believe that this work can enable a variety of applications where the semantic understanding of indoor scene is required. For example, mobility assessment, robot navigation, path planning and surveillance, object manipulation, grasping, learning object support order, visual search and 3D reconstruction. In this thesis, we first attempt the problem of learning and estimating free space i.e., floor regions in indoor scenes from a single image. Estimating free space is challenging due to high appearance variability within the floor and non-floor regions. It is even harder to segment floor regions when clutter, specular reflections, shadows and textured floors are present within the scene. We propose a framework which utilises a generic classifier of appearance cues as well as floor density estimates, both trained from a variety of indoor images. The result of the classifier is then adapted to a specific test image where we integrate appearance, position and geometric cues in an iterative framework. A Markov Random Field framework is used to integrate the cues to segment floor regions. The proposed approach is also flexible in situations where scene avoids assumptions like Manhattan world scene or restricting clutter only to wall-floor boundaries. Moving from detecting free space or floor regions, we use the appearance and geometric properties to estimate more general entities in the cluttered scene. These entities or objects are the basic units of any indoor scene, free space being one of them. For example different configurations of the set of objects in scenarios ranging from indoor scenes with cluttered tabletops to indoor scenes of offices, home, corridors, classrooms etc. Due to high variability in indoor scenes itself and complexity due to intraclass variability within objects, we restricted our attention to table top scenarios with known objects. The problem of estimating the layout of table top scenes is challenging due to the presence of clutter, objects of homogeneous appearance with that of the table surface, object-object occlusions and objects having irregular shapes and sizes. We train an ensemble of classifiers over appearance cues from various images of the known objects with different poses. We learn the meta-data (pose, shape) associated with each object and try to estimate its pose and shape in a given cluttered scene. The approach predicts the detailed layout of the objects present and free space on the table top where the objects can be placed. We created two datasets for the above-mentioned work. We first created an RGB based ”CVIT Indoor Scene dataset” of 110 images from various buildings in our campus that included cluttered floor regions. The images contained the wide variety of indoor scenes including classrooms, living rooms, library, corridors, two or three visible walls, etc. It also consisted of images with varied texture within the floor, specular highlights, shadows and scenes with cluttered floors due to furniture or other obstacles, where the clutter is not just confined to image boundaries. We also created a second RGBD dataset ”3DMOS” of 50 objects with different shapes and sizes. Each object had 15 images of different pose and view. It also had 10 cluttered table top scenes from sparsely cluttered to densely cluttered. We have made the dataset publicly available for the research community. The proposed approaches successfully demonstrate the robustness and efficiency on the various mentioned complex situations in indoor scenes. We believe that this work can play a significant role in the true understanding of an indoor scene and its semantics. It could also help in increased interaction of robotic agents or humans with the surrounding environment when this information is given to them. Keywords: Semantic Segmentation, Indoor Scene, Space Estimation, Object Manipulation, Cognitive Vision.

Abstract

Diabetic Retinopathy (DR) is an eye-aliment, which is one of the leading causes of blindness all over the world. With timely diagnosis and proper care, it is possible to manage this condition and control its severity. The sooner this condition is diagnosed, the better are the chances of preventing blindness and causing any further structural damage associated with DR. In this thesis, we address the problem of detecting DR from colour retinal images, by employing some biologically inspired approaches in a machine learning setting. DR detection entails the detection of a variety of lesion, among which we restrict our attention to bright lesions such as hard exudates, cotton wool spots and dark lesions such as haemorrhages. Microaneurysms, which are the smallest of all types of retinal lesions are not covered in this study, since due to their extremely small size, they are generally studied and analysed separately. In this thesis, we approach the problem of DR lesion detection via visual saliency, which is a basic and important biologically motivated concept. We show the relevance of visual saliency in bright lesion detection by demonstrating that visual saliency models, do capture hard exudates. We compare three popular visual saliency models, namely, Itti-Koch, Graph Based Visual Saliency (GBVS) and Spectral Residual (SR) and show that top 90% of the most salient locations provided by these models, are able to correspond with an average of 70% of all true hard exudates present in images. We propose a multi-scale version of the SR model, called as Extended SR (ESR) model and show its relative performance to be about 28.7% better than the previous models in detecting true hard exudates. This ESR model is used as a basis to design a bright lesion detector, which provides at the image level, high sensitivity (0.93) and specificity (0.78), averaged over 6 public datasets. We next propose two systems, one aimed as an assistive system for an annotator and another for detecting both bright and dark lesions. Both are based on a biologically inspired novel, lesion enhancing pipeline. The proposed system has been evaluated over 5 public datasets and is found to outperform existing systems with sensitivity/specificity values of 0.92/0.86 for bright lesions and 0.86/0.84 for dark lesions. We also evaluate the regional level lesion detection on one public dataset and obtain a performance figure of (0.91/0.88) for bright lesions and (0.85/0.83) for dark lesions. These underscore the potential of biologically inspired approaches to computer aided diagnostic algorithm development.

Abstract

Visual Attributes are properties observable in images that have human-designated names ( e.g., smiling, natural) and they are valuable as a new semantic cue in various vision problems like facial verification, object recognition, generating description of unfamiliar objects and to facilitate zero shot transfer learning etc. While most of the work on attributes focuses on binary attributes (indicating the presence or absence of attribute) the notion of relative attributes as introduced by Parikh and Grauman in ICCV 2011 provides an appealing way of comparing two images based on their visual properties than the binary attributes. Relative visual properties are a semantically rich way by which humans describe and compare objects in the world. They are necessary, for instance, to refine an identifying description (the rounder pillow; the same except bluer), or to situate with respect to reference objects (brighter than a candle; dimmer than a flashlight). Furthermore, they have potential to enhance active and interactive learning, for instance, offering a better guide for a visual search (find me similar shoes, but shinier or refine the retrieved images of downtown Chicago to those taken on sunnier days). For learning relative attributes a ranking svm based formulation was proposed that uses globally represented pairs of annotated images. In this thesis, we extend this idea towards learning relative attributes using local parts that are shared across categories. First we propose a part based representation that jointly represents a pair of images. For facial attributes, part corresponds to a block around a landmark point detected using a domain specific method. This representation explicitly encodes correspondences among parts, thus better capturing minute differences in parts that make an attribute more prominent in one image than another as compared to global representation. Next we update this part based representation by additionally learning weights corresponding to each part that denote their contribution towards predicting the strength of a given attribute. We call these weights as significance coefficients of parts. For each attribute the significance coefficients are learned in a discriminative manner simultaneously with a max-margin ranking model. Thus the best parts for predicting relative attribute more smiling will be different from those from predicting more eyes open. We compare the baseline method of Parikh and Grauman with the proposed method under various settings. We have collected a new dataset of 10000 pair wise attribute level annotations using images from labeled faces in the wild ( LFW) dataset particularly focusing on large variety of samples in terms of poses, lightning conditions etc and completely ignoring the category information while collecting attribute annotation . Extensive experiments demonstrate that the new method significantly improves prediction accuracy as compared to the baseline method. Moreover the learned parts also compare favorably with human selected parts, thus indicating the intrinsic capacity of the proposed framework for learning attribute specific semantic parts. Additionally we illustrate the advantage of the proposed method with interactive image search using relative attribute based feedback. In this work, we also propose relational attributes, which provide a more natural way of comparing two images based on some given attribute than relative attributes. Relational attributes consider not only the content of a given pair of images, but also take into account its relationship with other pairs, thus making the comparison more robust.

Abstract

Malayalam is an agglutinative and morphologically rich language as any other Dravidian language. Computational processing of Dravidian languages is not trivial because two or more words can join to form a string of words with a morpho-phonemic change at the point of joining. This process known as “Sandhi”, in turn complicates the individual word identification. The current work is an attempt to break the barrier of word segmentation and to create a shallow parser for Malayalam, which facilitates non-recursive phrase identification given an input. In this work, Shallow Parser has mainly 3 modules namely Sandhi Splitter, POS Tagger and Chunker. Since words are the basic components in the sentence, not identifying the individual words in a sentence will affect the output of a shallow parser. Hence in order to tackle the problem of “Sandhi”, after attempting a few rule-based approaches, we arrived at a hybrid “Sandhi Splitter” which gave an overall accuracy of 87% . This system uses Naive Bayes classifier to identify the split point and hand crafted character-level rules to induce morpho-phonemic changes. A CRF based Parts-Of-Speech tagger has been employed for the identification of grammatical category of words. Various experiments with different templates of features showed that Malayalam has more dependence over word-internal features like prefix and suffix information than word-external features like the position of a word in a sentence. The highest overall accuracy we obtained is 91.25%. The final module “chunker” has been employed to find out the non-recursive phrases based on the Parts-Of-Speech tags of the words. This CRF based chunker gave an overall accuracy of 94.33%. Error propagation is a problem in shallow parsing. Errors created by each module affect the subsequent modules. When each module is put in a pipeline in such a way that the output of the previous module will be the input of the next module, overall accuracy of shallow parser came down to 71% from 92%. This shows the need and importance of a highly accurate sandhi splitter which is the main source of error. It is important to note that the experiments clearly show that morphology is the key factor for processing Malayalam.

Abstract

Expecting a shipment of 1 billion Android devices in 2017, cyber criminals have naturally extended their vicious activities towards Google’s mobile operating system: threat researchers are reporting an alarming increase of detected Android malware from 2014 to 2015. In order to have some control over the estimated 700 new Android applications that are being released every day, there is need for a form of automated analysis to quickly detect and isolate new malware instances. Android is an open source Linux-based mobile operating system distributed by Google. According to the latest statistics, android powers hundreds of thousands mobile devices over 190 countries [26]. Google Play [51] is the official android centralized market place maintained by Google, where any independent application developer can submit his/her android app and make it available to the users. The growing popularity of this android ecosystem also is becoming a worthy target for security and privacy violations. Highly sensitive and confidential information such as text messages, private and business contacts, calendar data, etc may be leaked through an application. Sensors such as GPS present in the phones allow applications to provide context-sensitive user experience, they also create additional privacy concerns it can exploit the data for tracking or monitoring. Apart from these issues, smart phones are also susceptible to various malware threats such as viruses, Trojan horses, worms, etc. [50] Android security model relies highly on permission-based mechanism. There are about 130 per- missions that govern access to different resources. Whenever an user tries to install a new application, he/she is prompted to approve or reject all the permissions requested by the application. The application will be installed only after the user accepts all the necessary permissions requested by it. In this work, we use the permissions and api level information from the apps as the features to detect malicious applications. Further we observe that, android store [51] defines a category for every published application. We have done extensive studies and discovered that, certain categories are highly prone to malicious acts compared to other categories. We explicitly incorporate this information in our model and learn a naive bayes classifier for each category using the features that encode information about permissions and api calls. Given a new test application with a known category, we apply an appropriate classifier to detect if the application is malicious. We created a large data set of android applications and achieve an improvement of 3 4% by incorporating category level information. Secondly, we combine the association rule mining and classification rule mining techniques to build a classifier. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). To select the best features that distinguish between malware from benign apps, we rely on API level information within the bytecode since it conveys substantial semantics about the apps behaviour. More specifically, we focus on critical API calls and their package level information. Rather than simply treating the individual api calls as items, we represent an item as a combination of caller and callee api. We capture one level of control flow and context between caller and callee. Each item in our model is of the form A%B, where A is the caller and B is the callee. We use Androguard [8], a reverse engineering tool to perform API level feature extraction and data flow analysis. In summary, • combining association rule mining and classification rule mining for Android malware detection. • We achieved a detection rate of 85% over the baseline classifier of 0.69%.

Abstract

This thesis discusses sophisticated navigation frameworks for autonomous navigation of quadcopter in the indoor and outdoor environment using the frontal monocular camera as its primary exteroceptive sensor. Indoor and outdoor environment vary in their views, composition and constraints thus requiring different sophisticated approaches for navigation. Therefore, individual autonomous navigation frameworks for the indoor and outdoor environment are developed and presented in the thesis. The navigation frameworks for indoor and outdoor environment consists of three major components – perception, planning and control. Initially, navigation framework for autonomous navigation of quadcopter in GPS denied unknown indoor environment is presented. The perception module of navigation framework estimates dense depth map of the environment in real time from video stream obtained from the frontal monocular camera of the quadcopter using our novel supervised Hierarchical Structured Learning (HSL) approach. The proposed HSL approach discretizes the overall depth range into multiple sets. It structures these sets hierarchically and recursively through partitioning the set of classes into two subsets with subsets representing apportioned depth range of the parent set, forming a binary tree. The binary classification method is applied to each internal node of binary tree separately using Support Vector Machine (SVM). Moreover, the depth estimation of each pixel of the image starts from the root node in the top-down approach, classifying repetitively till it reaches any of the leaf node representing its estimated depth. The generated depth map is provided as an input to planning module based on Convolutional Neural Network (CNN), which generates flight planning commands. Finally, control module employs a convex programming technique to generate collision-free minimum time trajectory which follows these flight planning commands and produces appropriate control inputs for the quadcopter. Thereafter, outdoor autonomous navigation framework of quadcopter along with its application in precision agriculture is presented. The proposed system is able to perform plantation monitoring and yield estimation using supervised learning approach while autonomously navigating through the interrow path of pomegranate plantation. A new ‘pomegranate dataset’ comprising of plantation surveillance video and annotated frames capturing the varied stages of pomegranate growth along with the navigation framework are being delivered as a part of this thesis. The capabilities of the proposed system could be described as a twofold framework:(1) plantation monitoring and yield estimation: life cycle stage grad- ing and yield estimation of pomegranate while autonomously navigating through inter-row paths of the plantation using a monocular camera as its primary sensor. (2) Autonomous outdoor navigation framework: a novel navigation framework, which uses GPS way-points and vanishing point based planner and employs convex optimization for generating minimum time trajectory and control. The framework is implemented over Robot Operating System (ROS) middle-ware and ” first to release in Open Source”. A majority of the work in this thesis is attributed to developing an autonomous navigation framework for generic quadcopter which incorporates various theoretical concepts into practical implementation with Robot Operating System ( ROS ) as underlying middle-ware. Repeatable flights of successful nature, confirm the efficacy of the proposed navigation frameworks. The thesis is concluded by demonstrating the applicability of developed navigation framework on a low-cost commercial quadcopter P arrot T M Bebop.

Abstract

Cinema, being a newest of art forms, has introduced daunting ontological challenge in accounting for the phantasm of ‘cinematic reality’ and a narrative theorist challenge in accounting for cinematic expressivity or telling. Furthermore, cinema has become entirely digital. There is a need for the com- putable ontology of cinema and in particular narrative computing of cinematic story telling. Research in film theory and even future film making will benefit from building of a semantically searchable archive of digital films based on such formal ontology and narrative computing. The thesis depicts any digital cinema to be a hypergraph of various layers of discretist ontology. In particular, we develop automatic determination of Plot Units of the cinema narrative and depict it as hypergraph. We elaborate on the discrete ontology of cinema as hypergraph and finally suggest prototype of a film archive CinemaScope based on HyperGraph ontology of cinema. We give an initial report on the potential of narrative theory driven graph based representations of movies for narrative analysis and summarization. This is done using automatic generation of Plot Units and determination of affect states for characters in movies. The Plot Units are generated from movie scripts which are textual in nature. The movie scripts are parsed into scenes using a simple grammar. We then use some simple heuristics, rules and techniques to programatically generate Plot Units from these parsed movie scripts. We also present results of character analysis, related events detection and summarization from the generated Plot Units for movies. We use these results to argue that a graph data model for the content of movies could better capture its underlying semantics. We then show that the various graph representations for movies can be made to fit into the HyperGraph ontology. We also present a prototype for a digital film archive called CinemaScope based on HyperGraph ontology for films. Digital film archives in addition to preserving cinema should also accommodate and provide provisions for the computation of its cinematic elements by means of the metadata provided to represent the content in them. The provision for the computation of cinema could lead to various applications like semantic search, automatic genre clustering and classification, special visualization tools and robust comparisons of movies. This prototype has been designed to reduce the content and semantics of a movie to a hypergraph of tags. The prototype is very much in its preliminary stage but could serve as a foundation upon which more robust systems for film archives using HyperGraph ontology could be built upon and used for various research and academic analyses.