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Explaining Data Patterns using Knowledge from the Web of Data

Tiddi, Ilaria (2016). Explaining Data Patterns using Knowledge from the Web of Data. PhD thesis The Open University.

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Knowledge Discovery (KD) is a long-tradition field aiming at developing methodologies to detect hidden patterns and regularities in large datasets, using techniques from a wide range of domains, such as statistics, machine learning, pattern recognition or data visualisation. In most real world contexts, the interpretation and explanation of the discovered patterns is left to human experts, whose work is to use their background knowledge to analyse, refine and make the patterns understandable for the intended purpose. Explaining patterns is therefore an intensive and time-consuming process, where parts of the knowledge can remain unrevealed, especially when the experts lack some of the required background knowledge.

In this thesis, we investigate the hypothesis that such interpretation process can be facilitated by introducing background knowledge from the Web of (Linked) Data. In the last decade, many areas started publishing and sharing their domain-specific knowledge in the form of structured data, with the objective of encouraging information sharing, reuse and discovery. With a constantly increasing amount of shared and connected knowledge, we thus assume that the process of explaining patterns can become easier, faster, and more automated.

To demonstrate this, we developed Dedalo, a framework that automatically provides explanations to patterns of data using the background knowledge extracted from the Web of Data. We studied the elements required for a piece of information to be considered an explanation, identified the best strategies to automatically find the right piece of information in the Web of Data, and designed a process able to produce explanations to a given pattern using the background knowledge autonomously collected from the Web of Data.

The final evaluation of Dedalo involved users within an empirical study based on a real-world scenario. We demonstrated that the explanation process is complex when not being familiar with the domain of usage, but also that this can be considerably simplified when using the Web of Data as a source of background knowledge.

Item Type: Thesis (PhD)
Copyright Holders: 2016 Ilaria Tiddi
Keywords: knowledge discovery; linked data; background knowledge; pattern interpretation; pattern perception; knowledge management
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Item ID: 47827
Depositing User: Ilaria Tiddi
Date Deposited: 28 Nov 2016 16:01
Last Modified: 02 May 2018 14:24
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