Semantic Web in data mining and knowledge discovery

Research and industry increasingly make use of large amounts of data to guide decision-making. To do this, however, data needs to be analyzed in typically nontrivial refinement processes, which require technical expertise about methods and algorithms, experience with how a precise analysis should proceed, and knowledge about an exploding number of analytic approaches. To alleviate these problems, a plethora of different systems have been proposed that “intelligently” help users to analyze their data.This article provides a first survey to almost 30 years of research on intelligent discovery assistants (IDAs). It explicates the types of help IDAs can provide to users and the kinds of (background) knowledge they leverage to provide this help. Furthermore, it provides an overview of the systems developed over the past years, identifies their most important features, and sketches an ideal future IDA as well as the challenges on the road ahead.

/pdf/a-survey-of-intelligent-assistants-for-data-analysis-4a71y8cm7q.pdf

A survey of intelligent assistants for data analysis

In the assembly modeling of complex products, it is not unusual facing an explosive growth of assembly relations, or called combination explosion. Through integrating object-oriented methods with the knowledge template based modeling, in this paper, a new methodology is proposed to deal with the complexity of the assemblies of large-scale products. As such, the templates with various assembly relations can be defined for complex products; constitutive parts or subassemblies in a product structure are defined as object classes; their features can be encapsulated, and defined or modified freely via the parameters of object classes to meet the requirements of a specific instance. The new method supports reusing knowledge and simplifying assembly modeling of complex products. In this paper, recent literatures on object-oriented methodologies and knowledge template methods for assembly modeling are reviewed, the rationales of object-oriented templates are discussed, and a method is proposed for creating assembly templates for complex products. In the case study included in this paper, the developed method has been applied to digital modeling of aero-engines, thus, validating its performance.

Object-Oriented Templates for Automated Assembly Planning of Complex Products

http://www.cs.man.ac.uk/~stevensr/papers/DMOPpaper12.pdf

The Data Mining OPtimization Ontology

This chapter describes a principled approach to meta-learning that has three distinctive features. First, whereas most previous work on meta-learning focused exclusively on the learning task, our approach applies meta-learning to the full knowledge discovery process and is thus more aptly referred to as meta-mining. Second, traditional meta-learning regards learning algorithms as black boxes and essentially correlates properties of their input (data) with the performance of their output (learned model). We propose to tear open the black box and analyse algorithms in terms of their core components, their underlying assumptions, the cost functions and optimization strategies they use, and the models and decision boundaries they generate. Third, to ground meta-mining on a declarative representation of the data mining (dm) process and its components, we built a DM ontology and knowledge base using the Web Ontology Language (owl).

/pdf/ontology-based-meta-mining-of-knowledge-discovery-workflows-4dcdzx4h3n.pdf

Ontology-Based Meta-Mining of Knowledge Discovery Workflows

The problem addressed in this paper is the challenge of automated construction of knowledge discovery workflows, given the types of inputs and the required outputs of the knowledge discovery process. Our methodology consists of two main ingredients. The first one is defining a formal conceptualization of knowledge types and data mining algorithms by means of knowledge discovery ontology. The second one is workflow composition formalized as a planning task using the ontology of domain and task descriptions. Two versions of a forward chaining planning algorithm were developed. The baseline version demonstrates suitability of the knowledge discovery ontology for planning and uses Planning Domain Definition Language (PDDL) descriptions of algorithms; to this end, a procedure for converting data mining algorithm descriptions into PDDL was developed. The second directly queries the ontology using a reasoner. The proposed approach was tested in two use cases, one from scientific discovery in genomics and another from advanced engineering. The results show the feasibility of automated workflow construction achieved by tight integration of planning and ontological reasoning.

Petr Křemen

Papers

Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning