A Fuzzy Logic intelligent agent for Information Extraction: Introducing a new Fuzzy Logic-based term weighting scheme

In this paper, we propose a novel method for Information Extraction (IE) in a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. The aim of this system is to design and implement an intelligent agent to manage any set of knowledge where information is abundant, vague or imprecise. The method was applied to the case of a major university web portal, University of Seville web portal, which contains a huge amount of information. Besides, we also propose a novel method for term weighting (TW). This method also is based on Fuzzy Logic, and replaces the classical TF–IDF method, usually used for TW, for its flexibility

Information Extraction in a Set of Knowledge Using a Fuzzy Logic Based Intelligent Agent

A method for Information Extraction (IE) in a set of knowledge is proposed in this paper in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portal

A Method for the Access to the Contents in a Set of Knowledge Using a Fuzzy Logic Based Intelligent Agent

This paper proposes a method for the classification of the contents in a set of knowledge in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portalMinisterio de Educación y Ciencia DPI2006-15467-C02-0

Structured and Unstructured Information Extraction Using Text Mining and Natural Language Processing Techniques

Information on web is increasing at infinitum. Thus, web has become an unstructured global area where information even if available, cannot be directly used for desired applications. One is often faced with an information overload and demands for some automated help. Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents by means of Text Mining and Natural Language Processing (NLP) techniques. Extracted structured information can be used for variety of enterprise or personal level task of varying complexity. The Information Extraction (IE) in also a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. Information extraction is structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. Data mining research assumes that the information to be “mined” is already in the form of a relational database. IE can serve an important technology for text mining. The knowledge discovered is expressed directly in the documents to be mined, then IE alone can serve as an effective approach to text mining. However, if the documents contain concrete data in unstructured form rather than abstract knowledge, it may be useful to first use IE to transform the unstructured data in the document corpus into a structured database, and then use traditional data mining tools to identify abstract patterns in this extracted data. We propose a novel method for text mining with natural language processing techniques to extract the information from data base with efficient way, where the extraction time and accuracy is measured and plotted with simulation. Where the attributes of entities and relationship entities from structured and semi structured information .Results are compared with conventional methods

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

Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea