Whilst Knowledge Graphs (KGs) are increasingly used in business scenarios, the construction of enterprise ontologies and the population of KGs from existing relational data remains a significant challenge. In this paper we report our experience in supporting CSols (an SME operating in the analytical laboratory domain) in transitioning their data from legacy databases to a bespoke KG. We modelled the KG using a streamlined approach based on state of the art ontology engineering methodologies, that addresses the challenges faced by SMEs when transitioning to new technologies: lack of resources to devote to the transition, paucity of comprehensive data governance policies, and resistance within the organisation to accepting new practices and knowledge. Our approach uses a combination of UML diagrams and a controlled language glossary to support stakeholders in reaching consensus during the knowledge capture phase, thus reducing the intervention of the ontology engineer only to cases where no agreement can be found. We present a case study illustrating the generation of the KG from a UML specification of part of the analytical domain and from legacy relational data, and we discuss the benefits and challenges of the approach.

Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved This paper presents and compares three mechanisms for learning an ontology describing a domain of discoursed as defined in a collection of tweets. The task in part involves the identification of entities and relations in the free text data, which can then be used to produce a set of RDF triples from which an ontology can be generated. The first mechanism is therefore founded on the Stanford CoreNLP Toolkit.; in particular the Named Entity Recognition and Relation Extraction mechanisms that come with this tool kit. The second is founded on the GATE General Architecture for Text Engineering which provides an alternative mechanism for relation extraction from text. Both require a substantial amount of training data. To reduce the training data requirement the third mechanism is founded on the concept of Regular Expressions extracted from a training data “seed set”. Although the third mechanism still requires training data the amount of training data is significantly reduced without adversely affecting the quality of the ontologies generated.

A mechanism for identifying bandings in large "zero-one" N-dimensional data sets, using a sampling technique, is presented. The challenge of identifying bandings in data is the large number of potential permutations that need to be considered. To circumvent this a banding score mechanism is proposed that avoids the need to consider large numbers of permutations. This has been incorporated into a proposed banded pattern mining algorithm, the Exact ND Banded Pattern Mining (END BPM) algorithm. Although this operates well on reasonably sized datasets, there is still a challenge with respect to large N-dimensional data sets that cannot be held in primary storage. To this end a sampling technique is also proposed. The approach is fully described and evaluated using the GB cattle movement database, a "real life" database that records all movements of cattle in GB.

In this paper we examine the effect that the choice of support and confidence thresholds has on the accuracy of classifiers obtained by classification association rule mining. We show that accuracy can almost always be improved by a suitable choice of threshold values, and we describe a method for finding the best values. We present results that demonstrate this approach can obtain higher accuracy without the need for coverage analysis of the training data.

In this paper a number of classification rule evaluation measures are considered. In particular the authors review the use of a variety of selection techniques used to order classification rules contained in a classifier, and a number of mechanisms used to classify unseen data. The authors demonstrate that rule ordering founded on the size of antecedent works well given certain conditions.

Two new structures for association rule mining (ARM), the T-tree, and the P-tree, together with associated algorithms, are described. The authors demonstrate that the structures and algorithms offer significant advantages in terms of storage and execution time.

We consider a technique (DATA-VP) for distributed (and parallel) association rule mining that makes use of a vertical partitioning technique to distribute the input data, amongst processors. The proposed vertical partitioning is facilitated by a novel compressed set enumeration tree data structure (the T-tree), and an associated mining algorithm (Apriori-T), that allows for computationally effective distributed/parallel ARM when compared with existing approaches.

This paper is directed at two central objectives. The first is to identify and to establish areas of overlap between the expert and database system domains. The second is to present a view of existing and ongoing work concerning the verification, validation, and integrity (VV&I) of rule base and database systems. The paper combines reviews from the two perspectives of the expert systems and database systems communities, with the express aim of identifying possibilities where VV&I knowhow of the one may also be of value to the other (and, vice versa), especially with respect to the identified areas of overlap. © 2001 John Wiley & Sons, Inc.

Presents a region representation scheme and comparative analysis methods based on a tesseral addressing system. The proposed scheme is described in the context of a performance analysis of page segmentation methods, a document image analysis area that is particularly sensitive to both a successful region description scheme and efficient methods for comparative analysis. The proposed tesseral representation is more economical in storage than other Cartesian-based approaches and can be advantageous for comparative analysis.