529,001 research outputs found
Problem-Solving Knowledge Mining from Usersâ\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence.
In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. This exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. The framework was also used for iteratively expanding a domain-specific thesaurus. Furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.Formal concept analysis; Emergent self organizing map; Text mining; Actionable knowledge discovery; Domestic violence;
Mining geo-referenced databases: a way to improve decision-making
Knowledge discovery in databases is a process that aims at the discovery of associations
within data sets. The analysis of geo-referenced data demands a particular approach
in this process. This chapter presents a new approach to the process of knowledge
discovery, in which qualitative geographic identifiers give the positional aspects of
geographic data. Those identifiers are manipulated using qualitative reasoning
principles, which allows for the inference of new spatial relations required for the data
mining step of the knowledge discovery process. The efficacy and usefulness of the
implemented system â PADRĂO â has been tested with a bank dataset. The results
obtained support that traditional knowledge discovery systems, developed for
relational databases and not having semantic knowledge linked to spatial data, can
be used in the process of knowledge discovery in geo-referenced databases, since some
of this semantic knowledge and the principles of qualitative spatial reasoning are
available as spatial domain knowledge
AX: SEARCHING FOR DATABASE REGULARITIES USING CONCEPT NETWORKS
In many organizations, both business and scientific, we collect ever increasing amounts
of data using information technology. Indeed, the technology for collecting data has
outpaced our ability to analyze and interpret these very large databases. In this paper,
we discuss the interaction of heuristic search and domain knowledge in the AX
knowledge discovery tool. The search process rests on the use of rule quality measures
and the organization of domain knowledge. A small loan application database from
the machine learning repository is used to illustrate the process.Information Systems Working Papers Serie
Using Knowledge-based Information Systems to Support Management of Wireless Sensor Networking Systems
Currently, researches on Wireless Sensor Networks (WSN) mainly focus on how to efficiently gather sensing data from WSN, but little attention has been paid to how to effectively manage the large amount of collected sensing data. Information Systems (IS) are appropriatetools for data input, storage, processing, and output. Knowledge Management (KM) further transforms useful information into domain knowledge for decision making by domain experts. In this paper, we propose an approach to management of sensing data and transformation of sensing data into valuable knowledge using knowledge-based information systems. Firstly we propose a frameworkfor knowledge-based information systems which deals with internal and external information using intelligent agents to generate domain knowledge with KM methods. Then we definite a model of knowledge-based information system for WSN to implement intensive sensing data storage, knowledge discovery, statistical analysis, sharing, inquiry, decision support. Finally, a prototype system is developed and tested for the aforementioned ideas
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