24,938 research outputs found
SABIO: Soft Agent for Extended Information Retrieval
In the current study, an integrated system called SABIO is presented. The current system
applies Information Retrieval (IR) techniques developed for collections of textual documents to nontextual
corpa. SABIO integrates a fuzzy logic-based procedure for IR. Its search algorithm improves
the IR efficiency and decreases the computational burden by using a fuzzy logic-based procedure for
IR. This procedure is integrated in a flexible and fault-tolerant, human-reasoning-based search
algorithm. The Accumulated Knowledge Set (AKS) of the system is sorted in a hierarchic
multilevel tree-structure-like ontology. The objects in the AKS are represented using a novel
human-reasoning-based-method. This representation takes into account the occurrence of related
terms. The system uses a novel fuzzy logic-based term-weighting (TW) method. The developed fuzzy
logic method improves the classical term frequency–inverse document frequency (TF=IDF) method,
generally used for TW. The abovementioned system is the core of a wizard for search into the website
of the University of Seville, www.us.es, which is currently in testing
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
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
A document management methodology based on similarity contents
The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain
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