Search results for "Knowledge extraction"
showing 8 items of 58 documents
Ontology-based Integration of Web Navigation for Dynamic User Profiling
2015
The development of technology for handling information on a Big Data-scale is a buzzing topic of current research. Indeed, improved techniques for knowledge discovery are crucial for scientific and economic exploitation of large-scale raw data. In research collaboration with an industrial actor, we explore the applicability of ontology-based knowledge extraction and representation for today's biggest source of large-scale data, the Web. The goal is to develop a profiling application, based on the implicit information that every user leaves while navigating the online, with the goal to identify and model preferences and interests in a detailed user profile. This includes the identification o…
Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems
2020
Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposi…
An Introduction to Ontology Based Structured Knowledge Base System: Knowledge Acquisition Module
2013
The following text presents the method of supplementing and verifying information stored in a framework system of the semantic knowledge base. The indicated method refers to the knowledge of ontological character, in other words to information about definitions of concepts and relationships among them. The aim of the method is the constant supplementing and verifying of the knowledge, and making more precise and detailed information about existing connections between concepts. The key aspect of the method is questions generating strictly dependent on the preconceived structure of stored knowledge.
Conceptual Ontological Object Knowledge Base and Language
2008
This paper deals with AI in aspect of knowledge acquisition and ontology base structure. The core of the system was designed in an object model to optimize it for further processing. Direct concept linking was used to assure fast semantic network processing. Predefined attributes used in the core minimize the number of basic connections within the ontology and help in inference. The system is assumed to generate questions and to specify the knowledge. The AI system defined in this way opens a possibility for better understanding of such basic human mind mechanisms as learning or analyzing.
<title>Expanding context against weighted voting of classifiers</title>
2000
In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…
<title>Distance functions in dynamic integration of data mining techniques</title>
2000
One of the most important directions in the improvement of data mining and knowledge discovery is the integration of multiple data mining techniques. An integration method needs to be able either to evaluate and select the most appropriate data mining technique or to combine two or more techniques efficiently. A recent integration method for the dynamic integration of multiple data mining techniques is based on the assumption that each of the data mining techniques is the best one inside a certain subarea of the whole domain area. This method uses an instance-based learning approach to collect information about the competence areas of the mining techniques and applies a distance function to…
Knowledge Discovery from Microbiology Data: Many-Sided Analysis of Antibiotic Resistance in Nosocomial Infections
2005
Nosocomial infections and antimicrobial resistance (AR) are highly important problems that impact the morbidity and mortality of hospitalized patients as well as their cost of care. The goal of this paper is to demonstrate our analysis of AR by applying a number of various data mining (DM) techniques to real hospital data. The data for the analysis includes instances of sensitivity of nosocomial infections to antibiotics collected in a hospital over three years 2002-2004. The results of our study show that DM makes it easy for experts to inspect patterns that might otherwise be missed by usual (manual) infection control. However, the clinical relevance and utility of these findings await th…
Research literature clustering using diffusion maps
2013
We apply the knowledge discovery process to the mapping of current topics in a particular field of science. We are interested in how articles form clusters and what are the contents of the found clusters. A framework involving web scraping, keyword extraction, dimensionality reduction and clustering using the diffusion map algorithm is presented. We use publicly available information about articles in high-impact journals. The method should be of use to practitioners or scientists who want to overview recent research in a field of science. As a case study, we map the topics in data mining literature in the year 2011. peerReviewed