Search results for "Knowledge extraction"

Flexible pattern discovery with (extended) disjunctive logic programming

2005

The post-genomic era showed up a wide range of new challenging issues for the areas of knowledge discovery and intelligent information management. Among them, the discovery of complex pattern repetitions in string databases plays an important role, specifically in those contexts where even what are to be considered the interesting pattern classes is unknown. This paper provides a contribution in this precise setting, proposing a novel approach, based on disjunctive logic programming extended with several advanced features, for discovering interesting pattern classes from a given data set.

Semantic web service discovery system for road traffic information services

2015

Create a multi-agent platform for a traveller information system (FIPA standards).Extend Paulucci algorithm with the use of seven similarity measures.Weight the similarity measure according to semantic relation and parameter nature.Improved running-time with a filtering pre-process for non-functional parameters.Improved the recall by measuring the sibling relationship concepts. We describe a multi-agent platform for a traveller information system, allowing travellers to find the road traffic information web service (WSs) that best fits their requirements. After studying existing proposals for discovery of semantic WS, we implemented a hybrid matching algorithm, which is described in detail …

Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language

2016

Nowadays, most of the data on the Web is still in the form of unstructured text. Knowledge extraction from unstructured text is highly desirable but extremely challenging due to the inherent ambiguity of natural language. In this article, we present an architecture of an information extraction system based on the concept of Embedded Controlled Language that allows for extracting formal semantic knowledge from an unstructured text corpus. Moreover, the presented approach has a potential to support multilingual input and output.

Machine Learning and Knowledge Discovery in Databases. Research Track

2021

Defining Interaction Design Patterns to Extract Knowledge from Big Data

2018

[EN] The Big Data domain offers valuable opportunities to gain valuable knowledge. The User Interface (UI), the place where the user interacts to extract knowledge from data, must be adapted to address the domain complexities. Designing UIs for Big Data becomes a challenge that involves identifying and designing the user-data interaction implicated in the knowledge extraction. To design such an interaction, one widely used approach is design patterns. Design Patterns describe solutions to common interaction design problems. This paper proposes a set of patterns to design UIs aimed at extracting knowledge from the Big Data systems data conceptual schemas. As a practical example, we apply the…

Does relevance matter to data mining research?

2008

Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of several DM algorithms from different paradigms in order to produce a better solution elevating the importance of practice-oriented aspects also in DM research. In this chapter we strongly emphasize that DM research should also take into account the relevance of research, not only the rigor of it. Und…

On the use of information systems research methods in data mining

2006

Information systems are powerful instruments for organizational problem solving through formal information processing (Lyytinen, 1987). Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it (Fayyad, 1996). Data mining bridges many technical areas, including databases, statistics, machine learning, and human-computer interaction. The set of data mining processes used to extract and verify patterns in data is the core of the knowledge discovery process. Numerous data mining techniques have recently been developed to extract knowledge from large databases. The area of data mining is historically more related to AI (Artificial…

Natural Language-Based Knowledge Extraction in Healthcare Domain

2019

There is a growing amount of data in the databases of hospitals. These data could be exploited to alleviate the decision-making process of hospital managers, physicians and researchers. However, these types of end-users often lack the expertise necessary for extracting those data from the database. Several approaches exist in the field of how to allow non-programmers writing queries in a convenient manner, but none of them has yet reached fully satisfactory results. This paper sketches a solution to this problem by introducing means for writing queries in a keywords-containing natural language thus alleviating the query writing process for the end-user. Introducing this approach in the know…

Knowledge management challenges in knowledge discovery systems

2006

Current knowledge discovery systems are armed with many data mining techniques that can be potentially applied to a new problem. However, a system faces a challenge of selecting the most appropriate technique(s) for a problem at hand, since in the real domain area it is infeasible to perform a comparison of all applicable techniques. The main goal of this paper is to consider the limitations of data-driven approaches and propose a knowledge-driven approach to enhance the use of multiple data-mining strategies in a knowledge discovery system. We introduce the concept of (meta-) knowledge management, which is aimed to organize a systematic process of (meta-) knowledge capture and refinement o…

Comparing the applicability of two learning theories for knowledge transfer in information system implementation training

2004

This study reviews two traditional learning theories from the viewpoint of knowledge transfer in information system implementation training. The main goal of this study is to determine which is more applicable from the view of knowledge transfer in this context. In this study, behaviourist learning theory is found suitable for the transfer of data and information. Being more learner-centered, constructivist learning theory suits better for information system implementation training, as it enables combining system specific knowledge with knowledge of the existing organisational processes. This creates new organisation-specific knowledge necessary for the effective use of the information syst…