Dealing with inconsistent and incomplete data in a semantic technology setting

Semantic and traditional databases are vulnerable to Inconsistent or Incomplete Data (IID). A data set stored in a traditional or semantic database is queried to retrieve record(s) in a tabular format. Such retrieved records can consist of many rows where each row contains an object and the associated fields (columns). However, a large set of records retrieved from a noisy data set may be wrongly analysed. For example, a data analyst may ascribe inconsistent data as consistent or incomplete data as complete where he did not identify the inconsistency or incompleteness in the data. Analysis on a large set of data can be undermined by the presence of IID in that data set. Reliance as a result is placed on the data analyst to identify and visualise the IID in the data set. The IID issues are heightened in open world assumptions as evident in semantic or Resource Description Framework (RDF) databases. Unlike the closed world assumption in traditional databases where data are assumed to be complete with its own issues, in the open world assumption the data might be assumed to be unknown and IID has to be tolerated at the outset. Formal Concept Analysis (FCA) can be used to deal with IID in such databases. That is because FCA is a mathematical method that uses a lattice structure to reveal the associations among objects and attributes in a data set. The existing FCA approaches that can be used in dealing with IID in RDF databases include fault tolerance, Dau's approach, and CUBIST approaches. The new FCA approaches include association rules, semi-automated and automated methods in FcaBedrock. These new FCA approaches were developed in the course of this study. To underpin this work, a series of empirical studies were carried out based on the single case study methodology. The case study, namely the Edinburgh Mouse Atlas Gene Expression Database (EMAGE) provided the real-life context according to that methodology. The existing and the new FCA approaches were used in identifying and visualising the IID in the EMAGE RDF data set. The empirical studies revealed that the existing approaches used in dealing with IID in EMAGE are tedious and do not allow the IID to be easily visualised in the database. It also revealed that existing FCA approaches for dealing with IID do not exclusively visualise the IID in a data set. This is unlike the new FCA approaches, notably the semi-automated and automated FcaBedrock that can separate out and thus exclusively visualise IID in objects associated with the many value attributes that characterise such data sets. The exclusive visualisation of IID in a data set enables the data analyst to identify holistically the IID in his or her investigated data set thereby avoiding mistaken conclusions. The aim was to discover how effective each FCA approach is in identifying and visualising IID, answering the research question: "How can FCA tools and techniques be used in identifying and visualising IID in RDF data?" The automated FcaBedrock approach emerged to be the best means for visually identifying IID in an RDF data set. The CUBIST approaches and the semi-automated approach were ranked as 2nd and 3rd, respectively, whilst Dau's approach ranked as 4th. Whilst the subject of IID in a semantic technology setting could be explored further, it can be concluded that the automated FcaBedrock approach best identifies and visualises the IID in an RDF thus semantic data set.