Interpretation Of Query Research Articles

CLOUD BASED MULTI-LANGUAGE INDEXING USING CROSS LINGUAL INFORMATION RETRIEVAL APPROACHES

The exponential growth of data sizes created by digital media (video/audio/images), physicalsimulations, scientific instruments and web authoring joins the new growth of interest in cloud computing. The options for distribution and parallelization of information in clouds make the retrieval and storage processes very complicated, especially when faced with real-time data management. The quantity of Web Users getting access to data over Internet is expanding step by step. An enormous measure of data on Internet is accessible in various languages which could be accessed by anyone whenever. The Information Retrieval (IR) manages finding valuable data from a huge assortment of unorganized, organized and semi-organized information. In the present situation, the variety of data and language boundaries are the difficult challenges for communication and social trade over the world. To tackle such obstructions, CLIR, the cross-language information retrieval frameworks, are these days in solid interest. The Query Expansion (Q.E.) is the way toward adding related and important terms to original inquiry to upgrade its indexing ability to improve the significance of recovered files in CLIR. In this exploration work, Q.E. has been investigated for a Hindi-English and Kannada-English CLIR in that Hindi and Kannada queries are utilized to look through English docs. After the interpretation of query, recovered outcomes are positioned making use of OkapiBM25 to organize the most important doc at the top for expanding the significance of recovered docs using QE. We proposed architecture for Hindi-English and Kannada-English CLIR making use of QE. to improve the importance of recovered reports. In the primary investigation, QE. is performed with and without OkapiBM25 ranking. The outcomes show that the pertinence of recovered archives is higher with OKapiBM25 as contrast with the one without positioning. The work docs plainly demonstrate that the presentation of Hindi-English and Kannada-English CLIR framework can be improved altogether with query development using fitting terms located at suitable place and the recovered Snippets can incredibly fill in as the continuous test collection.

Implicit relevance feedback from electroencephalography and eye tracking in image search

Objective. Methods from brain–computer interfacing (BCI) open a direct access to the mental processes of computer users, which offers particular benefits in comparison to standard methods for inferring user-related information. The signals can be recorded unobtrusively in the background, which circumvents the time-consuming and distracting need for the users to give explicit feedback to questions concerning the individual interest. The obtained implicit information makes it possible to create dynamic user interest profiles in real-time, that can be taken into account by novel types of adaptive, personalised software. In the present study, the potential of implicit relevance feedback from electroencephalography (EEG) and eye tracking was explored with a demonstrator application that simulated an image search engine. Approach. The participants of the study queried for ambiguous search terms, having in mind one of the two possible interpretations of the respective term. Subsequently, they viewed different images arranged in a grid that were related to the query. The ambiguity of the underspecified search term was resolved with implicit information present in the recorded signals. For this purpose, feature vectors were extracted from the signals and used by multivariate classifiers that estimated the intended interpretation of the ambiguous query. Main result. The intended interpretation was inferred correctly from a combination of EEG and eye tracking signals in 86% of the cases on average. Information provided by the two measurement modalities turned out to be complementary. Significance. It was demonstrated that BCI methods can extract implicit user-related information in a setting of human-computer interaction. Novelties of the study are the implicit online feedback from EEG and eye tracking, the approximation to a realistic use case in a simulation, and the presentation of a large set of photographies that had to be interpreted with respect to the content.

Retrieving web search results using Max–Max soft clustering for Hindi query

Information retrieval (IR) is the process of finding relevant information from the millions of unstructured documents on the web. Despite of all the success in IR, it faces many problems such as lexical ambiguity, compound word formation and language morphology etc. To address the ambiguity problem, in this paper the authors proposed a graph based soft clustering method which improves the performance of IR system. Initially text snippet words are taken for constructing a co-occurrence graph corresponding to the Hindi query given by a user. Then other words (relevant to the query terms) present in the text corpus are added on the basis of the dice coefficient. For each interpretation of the user query, we retrieve results in the form of a web cluster. Sometimes more than one interpretation of the query are closely related, therefore many results returned from IR corresponding to these interpretations are common. This type of issue can be better dealt by using soft clustering method, so we use Max–Max soft clustering approach. We use various similarity measures like word overlap, degree overlap, token overlap and average similarity respectively for ranking the results within each cluster. This is the first attempt to fuzzy IR for a query in Hindi language, experimental evaluations shows promising results.

Advance Frameworks for Hidden Web Retrieval Using Innovative Vision-Based Page Segmentation

The volatile intensification of internet has posed a exigent problem in extracting significant data. Conventional web crawlers focus merely on the surface web while the hidden web remains intensifying following the prospect. Hidden web pages are created dynamically as a result of queries masquerade to precise web databases. The structure of the hidden web pages crafts it unfeasible for conventional web crawlers to access hidden web contents. We proposed innovative Vision-based Page Segmentation (IVBPS) algorithm for hidden web retrieval and develop intelligent crawler and interpretation of hidden web query interfaces. Hidden intelligent crawler and interpretation of hidden web query interfaces split the method into two stages. The primary stage comprises query analysis and query translation and the subsequent wrap vision-based extraction of data from the dynamically created hidden web pages. There are numerous conventional approaches for the extraction of hidden web pages but our proposed technique intends at overcoming the inherent limitations of the former.

Preference-based search technology for the user query semantic interpretation

Typical semantic search query for Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. Existing keyword-based retrieval system is Preference for the semantic interpretation of a user's query is not the meaning of the user keywords of interconnect, you can not search. In this paper, we propose a method that can provide accurate results to meet the user's search intent to user preference based evaluation by ranking search. The proposed scheme is Integrated ontology-based knowledge base built on the formal structure of the semantic interpretation process based on ontology knowledge base system.

Monochromatic and Bichromatic Reverse Top-k Queries

Nowadays, most applications return to the user a limited set of ranked results based on the individual user's preferences, which are commonly expressed through top-k queries. From the perspective of a manufacturer, it is imperative that her products appear in the highest ranked positions for many different user preferences, otherwise the product is not visible to potential customers. In this paper, we define a novel query type, namely the reverse top-k query, that covers this requirement: “Given a potential product, which are the user preferences that make this product belong to the top-k query result set?.” Reverse top-k queries are essential for manufacturers to assess the impact of their products in the market based on the competition. We formally define reverse top-k queries and introduce two versions of the query, monochromatic and bichromatic. First, we provide a geometric interpretation of the monochromatic reverse top-k query to acquire an intuition of the solution space. Then, we study in detail the case of bichromatic reverse top-k query, and we propose two techniques for query processing, namely an efficient threshold-based algorithm and an algorithm based on materialized reverse top-k views. Our experimental evaluation demonstrates the efficiency of our techniques.

Derived types in semantic association discovery

Semantic associations are direct or indirect linkages between two entities that are construed from existing associations among entities. In this paper we extend our previous query language approach for discovering semantic associations with an ability to retrieve semantic associations that, besides explicitly stated (base) associations, may contain associations derived using logic-based derivation rules. As will be shown, this makes it possible to find semantic associations that are both compact and intuitive. To implement this new feature, we introduce a rewriting principle that utilizes derived associations to reduce resulting semantic associations if possible. Other proposed means to assist the interpretation of query results include answer expansion and the ordering of answers. The incorporated answer expansion feature lets the user investigate rewritten semantic associations in a query result at the desired level of detail. The ordering of answers is based on the lengths of the resulting semantic associations, whereby priority is given to shorter semantic associations which often express close and relevant relationships.

The effect of a database feedback system on user performance

Abstract There are two main approaches to improving the effectiveness of database interfaces. One is to raise the level of abstraction for the content of the user-database interaction. The relational model belonging to the logical level has replaced the hierarchical and network models that belong to the lower physical level. It is likely that the relational model will eventually be replaced by models belonging to the even higher conceptual level, such as entity relationship models and object-oriented models. The second approach is to enhance the actual interaction process. This can be done by providing better feedback to the user. Feedback can be in the form of more comprehensible error messages, and the provision of a natural language interpretation of user's query. Such a feedback system was developed, and its effectiveness tested in an experiment. The results showed that the feedback system enhanced user performance greatly. Specifically, users who used the feedback system were 12.9% more accurate than those without the feedback system. They were also 41.2% more confident of their answers, and they took 29.0% less time than those without the feedback system.

Did Deflation Ruin the Farmer and Would Inflation Save Him?

The recent marked improvement in the agricultural situation has led many persons to assume that the pressing problems of the individual farmer, as well as questions of national agricultural policy, have by some miraculous process solved themselves and that discussions of the ills of the farmer and remedies therefor are now mere objects of historical interest or academic speculation. Many of us, however, who are concerned with the permanent well-being of agriculture, while we grant that some progress has been made during these sadder and wiser days since 1920, look upon the present relatively good fortune of the farming class as having been achieved to a considerable degree at the expense of Europeans, Argentines, and other foreign competitors in the world markets who have been the victims of unfavorable crop conditions. Furthermore, this increased well-being among American farmers is by no means universal, as a survey of certain corn, dairy and livestock producing sections will readily show. The first half of my topic, viz., Did deflation ruin the farmer ? lends itself to widely varying interpretations. That farm products underwent a radical price deflation and that agriculturists as a class suffered more severely than any other group seems scarcely open to argument. Consequently, I would be presuming upon your patience if I addressed myself to the details of that deflation. A strictly literal interpretation of this first query in the title might just as logically confine one also to the question as to whether the farming class could truly be said to have been ruined by the deflation. But here again the facts are too patent to warrant serious difference of opinion, for it is universally conceded that a quite appalling number of individual farmers, especially in the newer sections of the country, were completely ruined as a result of the price debacle. With your permission, then, I am going to adopt a rather free interpretation of this part of my title and shall assume that it reads: Did the individuals