Theses and Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/1

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    Knowledge compilation in multimodal logic
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2016) Kokane, Tushar V.; Raut, Manoj Kumar
    Knowledge representation and knowledge retrieval are the integral parts of artificialintelligence. However, both the representation of knowledge using a logicalformalism and the retrieval of information from the knowledge base, arehighly demanding from computational point of view. The three main approachesproposed to deal with the computational intractability of query answering problemsare restriction on the representation language, approximation of the knowledgebase and knowledge compilation. However, the first approach leads to reducedexpressibility and the second one lacks the equivalence-preserving property.Knowledge compilation divides the task of query answering into two phasesnamely, off-line and on-line phase. In off-line phase, the knowledge base is compiledand its output is then used to answer the actual queries in the on-line phase.In this thesis, we are mainly concerned with the logical compilations in multimodalknowledge bases. We consider computation of theory prime implicates asour off-line phase of knowledge compilation. The algorithm to compute theoryprime implicates in modal logic has been proposed in [10]. In this thesis, we haveextended that algorithm to compute theory prime implicates of a knowledge baseX with respect to another knowledge base ^ts=1 sY in multimodal logic Ks andproved its correctness. We have also extended the query answering algorithmfrom [10] and given the complexity for the same.
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    Service integration on social network
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Patel, Mehul; Chaudhary, Sanjay; Bise, Minal
    Microblogging services are part of social network platforms, which allow people to exchange short messages. Social networks provide people to play an active role in collecting, analyzing and reporting news and information. People can use social network platform for marketing, buying and selling of their products. A sellers can tweet regarding product information including links of related photos, videos etc. A buyer can show interest in the product by means of tweets. Social network can be used as a mechanism to bring sellers and buyers closer. It provides a common platform for buyers and sellers to sell and buy their products. Microblogs can be parsed and analyzed to generate useful suggestions, e.g. sellers can be informed about potential buyers to get higher profit. Such information can be used to generate classified information to help users to take decision, e.g. minimum expected price of a crop that sellers expect in a given region. Microblogs can be written in different regional languages. Agro-produce marketing information can be processed and then stored in RDF/RDF(S) and OWL data store. SPARQL and conjunctive queries with pellet like reasoner or SPARQL-DL can be used to generate classified summarized information from RDF/RDF(S) and OWL data store.
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    SPARQL query optimization
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Singh, Rohit Kumar; Chaudhary, Sanjay
    Query Optimization is the process of selecting the most efficient query evaluation plan among the many strategies possible for processing a given query, especially if the query is complex. The users are not expected to write their queries in such a way so that they can be processed efficiently; rather it is expected from system to construct a query evaluation plan that minimizes the cost of query evaluation. In any query optimization, the goal is to find the execution plan which is expected to return the result set without actually executing the query or subparts with optimal cost. Query engines for ontological data mostly execute user queries without considering any optimization. Especially for large ontologies,optimization techniques are required to ensure that query results are delivered within reasonable time. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. So, Query optimization may speed up SPARQL query answering by knowledge intensive reformulation. In our research work, we have proposed learning approach to solve this problem. In our approach, the learning is triggered by user queries. Then the system uses an inductive learning algorithm to generate semantic rules. This inductive learning algorithm can automatically select useful join paths and properties to construct rules from a ontology with many concepts. The learned semantic rules are effective for optimization of SPARQL query because they match query patterns and reflect data regularities.
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    Context aware semantic service discovery
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2009) Patel, Pankesh; Chaudhary, Sanjay
    This report is a part of an effort to develop a novel context based mechanism to facilitate semantic service discovery. Context- awareness is considered to be a key problem in designing more adaptive applications. Context modeling and reasoning are important research area of context-awareness computing. At the same time huge amount of data leading to inefficient systems raises a demand for filtering techniques. A study of existing discovery mechanisms has been done.
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    Web content outlier detection using latent semantic indexing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2007) Paluri, Santosh Kumar; Jotwani, Naresh D.
    Outliers are data elements different from the other elements in the category from which they are mined. Finding outliers in web data is considered as web outlier mining. This thesis explores web content outlier mining which finds applications in electronic commerce, finding novelty in text, etc. Web content outliers are text documents having varying contents from the rest of the documents taken from the same domain. Existing approaches for this problem uses lexical match techniques such as n-grams which are prone to problems like synonymy (expressing the same word in different ways), which leads to poor recall (an important measure for evaluating a search strategy). In this thesis we use Latent Semantic Indexing (LSI) to represent the documents and terms as vectors in a reduced dimensional space and thereby separating the outlying documents from the rest of the corpus. Experimental results using embedded outliers in chapter four indicate the proposed idea is successful and also better than the existing approaches to mine web content outliers.