Ethiopic Church Manuscript, Art and Music domain area is chosen so as to investigate and analyze issues surrounding the representation of Ethiopic concepts within the context of semantic web. Salient ontologies namely- manuscript, art, music, feast, time, date and number ontologies- are abstracted after careful scrutiny of related ontologies and usage of suitable ontology development methodology. Acquisition and digitization of representative data samples for classes of concepts and provision of a model for storage and retrieval of information related to the domain of interest, is also entertained. Furthermore, an experiment for evaluation of ontologies abstracted and model proposed is conducted by implementing a form-based semantic search engine and comparison of query results with that of keyword-based search engines. Initial results shows that the devised ontologies aloft the searching process in the domain area as well as unveil set aside issues, specific to Ethiopic concepts within the context of semantic web.
Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships, and a XML database can be represented as a graph with XML elements as nodes and containment or ID-IDREF edges as hyperlinks. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity - users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. Since the keyword search query interface is very flexible, queries may not always be precise and can potentially return a large number of query results, especially in large document collections. Consequently, an important requirement for keyword search is to rank the query result. The goal of this book is to present various search techniques.
Restructuring web search results is the best solution for ambiguous queries being entered to the search engine. When ambiguous queries are entered to the search engine gives multiple results for same query, so user don't get specific and accurate information about what they really want, so it becomes difficult for a user to get specific information related to the submitted keyword. For this reason a new criterion is used in which feedback sessions are first generated from user clicked through logs. Using Feedback session a pseudo documents are generated by calculating TF-IDF (Term Frequency Inverse Data Frequency) vectors for each URL in clicked through logs. Then k-means clustering algorithm is applied and these pseudo documents are clustered and user search goals are generated and restructuring is done through user search goal and user gets specific information fast and correctly. Then the performance of each user search goal is calculated by using CAP metric. These metrics shows how correct restructuring is done.
A selection-based search system is a search engine system in which the user invokes a search query using only the mouse. A selection-based search system allows the user to search the internet for more information about any keyword or phrase contained within a document or webpage in any software application on his desktop computer using the mouse. Traditional browser-based search systems require the user to launch a web browser, navigate to a search page, type or paste a query into a search box, review a list of results, and click a hyperlink to view these results.
Information retrieval technology has been central to the success of the web. The goal of information retrieval is to provide users with those documents that will satisfy their information need. With the large volume of data available in the web, retrieving relevant information becomes a difficult task. The common reason for this problem is that currently content-description and query-processing techniques for information retrieval are based on keywords. This involves limitations such as inability to describe semantic relations between search terms. Semantically-enhanced information retrieval overcomes the limitations faced by keyword based search since the focus is on semantics leading to better and accurate results. Even for the use of semantic technology, efficient clustering techniques are needed to improve the relevancy of documents, and also optimization problem occurs, but is rarely considered. The main objective of Document-Clustering is to avoid the recovery of non-relevant documents.
Cloud computing provides a platform to store large amount of data. In conventional method data is stored on local site or local server but it is not feasible for complex and sensitive data. Cloud computing allow data owner to store their data on remote site so it reduce burden on local complex data storing. Data owners outsource their data to public cloud because of safety and economic savings. When data owner outsource their data these data are encrypted before they are transferred to the public cloud.In this project we use Blowfish algorithm for encryption of data. Before storing sensitive or complex data is encrypted and this can overcome plain-text keyword search. Cloud users need different kinds of data from cloud. When cloud user request data multiple keywords are allowed to the user query when searching in cloud this is called Multi keyword ranked
Recently the focus of query independent summary is shifted to query specific document. This book presents a graph based method to find query specific multi-document summarization. Our system is divided into two stages, of- fline and online. We construct document as graph by con- sidering paragraph as nodes in offline stage. Edge scores are represented node similarities. In online stage, query specific weight are calculated and assigned to node. We then perform keyword search on the document graph and search a minimum top spanning tree for finding relevant nodes that satisfy the keyword search. Resultant summary looks coherent due to simultaneous cluster and sentence ranking. Experimental results for multi-document scenarios are encouraging.
The textual information extracted from a digital video could be exploited in semantic indexing and retrieval of digital video libraries. A number of such retrieval systems have been researched previously for the detection of text in Latin alphabets. The detection of Urdu caption however, has not been explored as yet.Current book describes a system that will be able to automatically detect and localize Urdu caption text appearing in video sequence such as in news channels. The system uses edge features for text localization. The candidate text regions are then fed into Artificial Neural Network (ANN) for validation. Finally, the text is extracted from validated candidate text regions. The output of this process could be fed to a (Urdu) Optical Character Recognition (OCR) system to recognize textual content in the video images and employ the extracted (key) words to index the video. Users will then be able to query the indexed video library with a given keyword and find all the videos (and occurrences in a video) containing the keyword provided.
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Q-go is a privately owned international company that specializes in semantic search SaaS, based on Natural Language Processing technology. The technology provides relevant answers to users in response to queries on a company''s internet website or corporate intranet, formulated in natural sentences or keyword input alike. It integrates automatic statistical reporting of user query behavior for businesses that want to monitor what kinds of questions their customers are asking. This is in order to adjust content to provide the appropriate information for customers and to reduce the load on traditional customer service ports of call, such as call centers and answers by email. Q-go was founded in 1999. Its head office is based in Amsterdam, with further offices in Barcelona, Madrid, Bonn, Zurich and New York. It also has partnership presence in other countries.