An Approach for Keyword Searching in Uncertain Graph Data

ABSTRACT: Keyword searching is generally used for retrieving the relevant data from the database. For input query, the related data is retrieved. But it is tedious task to search keyword on uncertain graph. In this paper, the keyword searching technique over uncertain graph is introduced. The Keyword routing method is used to route the keywords to relevant source. In this approach two methods are included. The keyword relationship graph deduces the relationship between keywords and the element mentioning them. The scoring mechanism computes the score of keywords at each level which reduces the ambiguity. The result will include the subtree of the entire graph which includes all keywords of input query having high score and in addition it retrieves the most relevant data . Effective results are derived from employed method

Graph-based Interactive Bibliographic Information Retrieval Systems

In the big data era, we have witnessed the explosion of scholarly literature. This explosion has imposed challenges to the retrieval of bibliographic information. Retrieval of intended bibliographic information has become challenging due to the overwhelming search results returned by bibliographic information retrieval systems for given input queries. At the same time, users’ bibliographic information needs have become more specific such that only information that best matches their needs is seen as relevant. Current bibliographic information retrieval systems such as Web of Science, Scopus, and Google Scholar have become an unalienable component in searching bibliographic data. However, these systems have limited support of complex bibliographic queries. For example, a query- “papers on information retrieval, which were cited by John’s papers that had been presented in SIGIR” is an ordinary information need that researchers may have, but is not appropriately representable in these systems. In addition, these systems only support search for papers and do not support other bibliographic entities such as authors and terms as the final search results. Therefore, in this dissertation, we propose several bibliographic information retrieval systems that can address complex bibliographic queries. We propose form-, natural language-, and visual graph-based systems that allow users to formulate bibliographic queries in a variety of ways. The form-based system allows users to formulate queries by selecting forms and input values in those selected forms. In the natural language-based system, users formulate queries using a natural language. Users formulate queries by drawing nodes and links in the visual graph-based system. These systems are based on a graph model to enhance retrieval efficiency and provides interfaces for users to formulate queries interactively. Through a system-centered evaluation, we find that our graph-based system took less time to process complex queries than a relational-entity-based system (two secs vs. several mins on average). In addition, our visual graph-based system can deal with the representation of advanced queries such as bibliographic coupling, paper co-citation, and author co-citation, while current bibliographic information systems do not support these queries. A user-centered evaluation reveals that participants rated the natural language-based system the most useful, easy to use, and easy to learn. Participants also reported that the form-based system was easier to learn than the visual graph-based system. Based on the results of a usability evaluation, we find that the form-based system is preferred for low-complexity tasks while the visual graph-based system is preferred for high-complexity tasks. The strength of the natural language-based system is that no additional effort is needed to formulate more complex queries. The proposed systems are effective and efficient solutions for addressing complex bibliographic information needs. In addition, we believe the experimental design and results shown in this paper can serve as a useful guideline and benchmark for future studies.Ph.D., Information Studies -- Drexel University, 201

Efficient keyword search on uncertain graph data

As a popular search mechanism, keyword search has been applied to retrieve useful data in documents, texts, graphs, and even relational databases. However, so far, there is no work on keyword search over uncertain graph data even though the uncertain graphs have been widely used in many real applications, such as modeling road networks, influential detection in social networks, and data analysis on PPI networks. Therefore, in this paper, we study the problem of top-k keyword search over uncertain graph data. Following the similar answer definition for keyword search over deterministic graphs, we consider a subtree in the uncertain graph as an answer to a keyword query if 1) it contains all the keywords; 2) it has a high score (defined by users or applications) based on keyword matching; and 3) it has low uncertainty. Keyword search over deterministic graphs is already a hard problem as stated in [1], [2], [3]. Due to the existence of uncertainty, keyword search over uncertain graphs is much harder. Therefore, to improve the search efficiency, we employ a filtering-and-verification strategy based on a probabilistic keyword index, PKIndex. For each keyword, we offline compute path-based top-k probabilities, and attach these values to PKIndex in an optimal, compressed way. In the filtering phase, we perform existence, path-based and tree-based probabilistic pruning phases, which filter out most false subtrees. In the verification, we propose a sampling algorithm to verify the candidates. Extensive experimental results demonstrate the effectiveness of the proposed algorithms. © 1989-2012 IEEE