Although there is no common agreement on the definition of an explanation, when reviewing the disciplines in Cognitive Science we showed that those have a similar way of structuring explanations. We gave an overview of such structures, including the elements, the interactions and the characteristics that explanations have according to each of the disciplines, and presented a general model that could unify those views.
2.5 Summary and Discussion | 49 Motivated by the idea of using the Web of Data as a source of knowledge, we have reviewed the most recent methods to store and access data from it. We have seen how most of the approaches rely on data indexing and introduce a priori knowledge to select the desired data sources, while very few use link traversal, a less computationally expensive technique which allows the serendipitous discovery of knowledge.
In order to explore the Web of Data automatically, we reviewed Graph Theory techniques that offer scalable ways to manage and access graph-structured data. However, we have seen how most of these approaches are highly sensitive to scalability issues, which can be relieved by using the right heuristics accessing only relevant portions of the data.
Finally, we presented approaches where the knowledge from the Web of Data was used as a support for Knowledge Discovery. We demonstrated how the knowledge from the Web of Data has been successfully used as a support for improving results in both data pre-processing and data-mining, while in data post-processing the expert’s knowledge is still required to interpret results. On the other hand, ontological knowledge has been used to induce hypotheses in Inductive Logic Programming-based frameworks, which show potential for adapting such technique for our scenario.
Based on this, we can conclude that a successful approach to automatically generate explanations from the Web of Data should possess the following properties:
¨it has to generate meaningful explanations, where meaningful means that they have to
include the same components as the Cognitive Science model presented in Section2.1;
≠it has to explore the Web of Data on-the-fly, so that the computational costs are limited
and only the necessary background knowledge is introduced;
Æit has to induce explanations automatically from the Web of Data, without any assistance
from a domain expert.
In our thesis, we will approach these constraints in the following way:
∂complete explanations can be obtained by designing a process in which its sub-processes
aim at finding each of the components of the Explanation Ontology;
∑we can combine the link traversal and graph searches to avoid the retrieval of unnecessary
information from the Web of Data;
∏we can apply the idea of Inductive Logic Programming, which induce candidate hypothe-
ses to justify observations based on some background knowledge, to automatically generate candidate hypotheses and explain a specific pattern using background knowledge that is automatically extracted from the Web of Data;
Let us step back to the scenario of Section2.1: we aim to automatically explain why “A Song of Ice and Fire” becomes popular on the web with a very specific regularity. In terms
of the Explanation Ontology, such an observation consists in the explanandumN. To obtain
a complete explanation, our process needs to automatically identify whatM, F and⌅are.
The first step that has to be achieved is the generation of candidate explanantia for the observation, i.e. some plausible events that have caused the fact that some dates belong to the pattern of “popular dates”. The Web of Data, which provides the background knowledge about the dates, can be accessed using link traversal combined with a heuristic search, with the aim of collecting information about events that have happened during those dates. As in
Inductive Logic Programming, we can use induction to generate candidate explanantiaM
learnt from the set of positive and negative examples (popular and non-popular dates) and the background knowledge built from the Web of Data. For example, both “people search for A Song of Ice And Fire when a new Game of Thrones season is released” and “people search for A Song of Ice And Fire when an episode of the TV comedy series Veep is aired” are plausible since they are both correlated to “A Song of Ice and Fire” according to Linked Data information. Challenges to be faced are the identification of a heuristic function for the traversal, as well as the assessment of the validity of the generated candidates. We address
them in Chapter4and Chapter5.
If no contextF is specified, there is then no way to distinguish whether the induced fact
M and N are co-occurring by coincidence (as in the case of “A Song of Ice and Fire” and the
“Veep TV series”). The second step of the process is therefore to identify the contextF that
relates the anterior and posterior events, to remove implausible explanations. This can be achieved by using an uninformed search across Linked Data that identifies the contextual
relationship betweenM and N, but requires us to study what is the best way to assess such
relationships in the graph of Linked Data. This is achieved in Chapter6.
Finally, for an explanation to be complete, the process has to identify the theory⌅, i.e.
the assumption behind which the pattern has been created. Ontology-level searches can be used to partly identify the theory (e.g. “A Song of Ice and Fire” is a novel, “Game of Thrones” is a TV series, and TV series can based on novels); however, building a full theory requires knowledge from different domains, that might not be existing as representations in the Web of Data (for instance, “people search over the Web for what they are interested in”). We study
this aspect in Chapter8.
In the second part of this thesis we will present the challenges and the solutions we proposed to the first two processes, that we have implemented in Dedalo, a framework to
2.5 Summary and Discussion | 51 automatically generate pattern explanations from the Web of Data. As for the last process, we will discuss on its feasibility in the third part of the work.
Part II
Looking for Pattern Explanations in the
Web of Data
Chapter 3
Generating Explanations through
Manually Extracted Background
Knowledge
This chapter presents our preliminary work, in which we focused on answering our third
research question (Section 1.3.3), i.e. how to automatically generate explanations with
background knowledge from the Web of Data. Section3.1shows how we identified Inductive
Logic Programming as a valid framework to generate pattern explanations automatically;
Section3.2presents the foundations of Inductive Logic Programming; Section3.3describes
how we shaped our problem as an Inductive Logic Programming one, and Section 3.4
presents some preliminary results. Section3.5discusses the limitations of the approach and
the next steps to undertake.
3.1 Introduction
The goal of our research is the creation of a system that automatically generates explanations for a given pattern using the background knowledge from the Web of Data. With such information we could, for instance, automatically assess that the popularity of the term “A Song of Ice and Fire” increases when a new “Game of Thrones” season is released. In this scenario, one of the first required steps is to find a process that automatically generates explanations.
In the previous chapter, we have seen how frameworks based on inductive reasoning have been successfully applied to generate hypotheses based on some ontological knowledge. Our idea is therefore to study their feasibility in the context of the Web of Data and, more specifically, we explore the possibility of using the Inductive Logic Programming framework
explanations from Linked Data.
In Machine Learning, Inductive Learning systems start from some positive and negative
evidence to learn general rules, also called “induced theories” [Lavrac and Dzeroski,1994].
Emerging from both Machine Learning and Logic Programming [Lloyd,2012], the field
of Inductive Logic Programming introduced the idea that the learning process could be improved by adding some prior knowledge about the body of evidence. This additional feature has been widely recognised as one of the strongest points of ILP, when compared to
other forms of Inductive Learning [Lisi and Esposito,2009].
Nevertheless, it has been highlighted that the ILP frameworks fail in organising the background knowledge in a well-formed conceptual model. For this reason, fields such as Onto-Relational Learning and Statistical Relational Learning proposed to introduce ontologies in the background knowledge because, on the contrary, those were a more natural
mean to convey conceptual knowledge [Chandrasekaran et al.,1999]. Based on this idea, we
study the integration of cross-disciplinary knowledge from the Web of Data in the background knowledge of an ILP framework in order to generate explanations for patterns.
The aim of this chapter is to demonstrate that Inductive Logic Programming is a good candidate for automatically generating pattern explanations, and that Linked Data provide valid background knowledge for this purpose. To this end, we start from some data (the evidence) organised into patterns, and then use ILP to induce theories that explain why certain data points belong to a specific pattern. As already said, the background knowledge upon which the theories are built consists of information found in Linked Data. Through this process, we can show that meaningful explanations can be automatically obtained based on the knowledge provided from the Web of Data.