Implementation of an efficient Fuzzy Logic based Information Retrieval System

This paper exemplifies the implementation of an efficient Information Retrieval (IR) System to compute the similarity between a dataset and a query using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query. Each query is assigned a score value based on its fuzzy similarity with the index keywords. The relevant documents are retrieved based on the score value. The performance and accuracy of the proposed fuzzy similarity model is compared with Cosine similarity model using Precision-Recall curves. The results prove the dominance of Fuzzy Similarity based IR system.Comment: arXiv admin note: substantial text overlap with http://ntz-develop.blogspot.in/ , http://www.micsymposium.org/mics2012/submissions/mics2012_submission_8.pdf , http://www.slideshare.net/JeffreyStricklandPhD/predictive-modeling-and-analytics-selectchapters-41304405 by other author

DCU and UTA at ImageCLEFPhoto 2007

Dublin City University (DCU) and University of Tampere(UTA) participated in the ImageCLEF 2007 photographic ad-hoc retrieval task with several monolingual and bilingual runs. Our approach was language independent: text retrieval based on fuzzy s-gram query translation was combined with visual retrieval. Data fusion between text and image content was performed using unsupervised query-time weight generation approaches. Our baseline was a combination of dictionary-based query translation and visual retrieval, which achieved the best result. The best mixed modality runs using fuzzy s-gram translation achieved on average around 83% of the performance of the baseline. Performance was more similar when only top rank precision levels of P10 and P20 were considered. This suggests that fuzzy sgram query translation combined with visual retrieval is a cheap alternative for cross-lingual image retrieval where only a small number of relevant items are required. Both sets of results emphasize the merit of our query-time weight generation schemes for data fusion, with the fused runs exhibiting marked performance increases over single modalities, this is achieved without the use of any prior training data

A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function

A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics

APLIKASI DATABASE FUZZY KINERJA PEGAWAI SMKN 02 BANGKALAN MENGGUNAKAN JSP

ABSTRACT The database system that available at the moment only can save the certain (crisp), deterministic and precision. Whereas, in the real condition we often need a vague data to make decision. We can use fuzzy logic to make decision that have variables with vague values. This final project will implement fuzzy logic into the query, which is called Fuzzy Daabase Query. That is, a query that has a variable valued fuzzy variables. To get information about the performance of employees, then the required criteria attendance and performance criteria that employees valued ambiguous. Data is based on the criteria of degree of membership valueof each employee that has been processed with the method of fuzzy Tahani. Whit the end of this task is expected to help obtain information about the performance of employees and can provide encouragement for employees to become better. Keyword: Fuzzy Logic, Fuzzy Set, Fuzzy Query Databas

K-nearest neighbor search for fuzzy objects

The K-Nearest Neighbor search (kNN) problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. Existing research on fuzzy objects mainly focuses on modelling basic fuzzy object types and operations, leaving the processing of more advanced queries such as kNN query untouched. In this paper, we propose two new kinds of kNN queries for fuzzy objects, Ad-hoc kNN query (AKNN) and Range kNN query (RKNN), to find the k nearest objects qualifying at a probability threshold or within a probability range. For efficient AKNN query processing, we optimize the basic best-first search algorithm by deriving more accurate approximations for the distance function between fuzzy objects and the query object. To improve the performance of RKNN search, effective pruning rules are developed to significantly reduce the search space and further speed up the candidate refinement process. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments using both synthetic and real datasets

Fuzzy term proximity with boolean queries at 2006 TREC Terabyte task

http://trec.nist.gov/pubs/trec15/papers/ecole.tera.final.pdfInternational audienceWe report here the results of fuzzy term proximity method app lied to Terabyte Task. Fuzzy proxmity main feature is based on the idea that the clos er the query terms are in a document, the more relevant this document is. With this p rinciple, we have a high precision method so we complete by these obtained with Zettair search engine default method (dirichlet). Our model is able to deal with Boolean qu eries, but contrary to the traditional extensions of the basic Boolean IR model, it does not explicitly use a proximity operator because it can not be generalized to node s. The fuzzy term proximity is controlled with an influence function. Given a query term a nd a document, the influence function associates to each position in the text a value depe ndant of the distance of the nearest occurence of this query term. To model proximity, th is function is decreasing with distance. Different forms of function can be used: triangula r, gaussian etc. For practical reasons only functions with finite support were used. The sup port of the function is limited by a constant called k. The fuzzy term proximity func tions are associated to every leaves of the query tree. Then fuzzy proximities are co mputed for every nodes with a post-order tree traversal. Given the fuzzy proximities of the sons of a node, its fuzzy proximity is computed, like in the fuzzy IR models, with a mim imum (resp. maximum) combination for conjunctives (resp. disjunctives) nodes. Finally, a fuzzy query proximity value is obtained for each position in this document at the ro ot of the query tree. The score of this document is the integration of the function obt ained at the tree root. For the experiments, we modify Lucy (version 0.5.2) to implement ou r matching function. Two query sets are used for our runs. One set is manually built wit h the title words (and sometimes some description words). Each of these words is OR 'ed with its derivatives like plurals for instance. Then the OR nodes obtained are AND'ed a t the tree root. An other automatic query sets is built with an AND of automatically ex tracted terms from the title field. These two query sets are submitted to our system with tw o values of k: 50 and 200. The two corresponding query sets with flat queries are also su bmitted to zettair search engine

Towards a New Extracting and Querying Approach of Fuzzy Summaries

Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to handle imprecise data and to offer a flexible querying to their users. Several theoretical solutions have been proposed. However, the impact of this work in practice remained negligible with the exception of a few research prototypes based on the formal model GEFRED. In this chapter, the authors propose a new approach for exploitation of fuzzy relational databases (FRDB) described by the model GEFRED. This approach consists of 1) a new technique for extracting summary fuzzy data, Fuzzy SAINTETIQ, based on the classification of fuzzy data and formal concepts analysis; 2) an approach of assessing flexible queries in the context of FDB based on the set of fuzzy summaries generated by our fuzzy SAINTETIQ system; 3) an approach of repairing and substituting unanswered query.Comment: 22 pages, 6 figures, 8 tables. Multidisciplinary Approaches to Service-Oriented Engineering, 2018. arXiv admin note: text overlap with arXiv:1401.049