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The forthcoming text switches its focus to the solution implemented in this work, and discussed in future chapters.

Figure 5.1 presents a high level overview of the work proposed in this project. It demonstrates that an argumentation system will receive as input biological infor- mation from resources (EMAGE and GXD) and the knowledge of how to interpret that information from an expert. A system could then produce biological arguments, similar to those in Section 5.4. This simplistic view is augmented in Figure 5.3.

It is an argumentation system’s requirement to formalise information and knowl- edge that is central to Figure 5.3 - the formalism in the diagram is from the example in Section 5.4. In order to reason, the system must have its input presented in a particular manner - the exact style will depend on the toolkit used to facilitate the argumentation. In a similar vein, the system’s “formal” presentation style will be not suitable for most humans. In summary, there is a need for four translations to occur:

Figure 5.3: An extended version of Figure 5.1 in which the need for translation between the resources, expert, argumentation system and end user is highlighted.

1. Convert the expert’s natural language into a formal representation for the ar- gumentation system;

2. Convert the programmatic representation of EMAGE’s information into the argumentation system’s formal representation;

3. Convert the programmatic representation of GXD’s information into the argu- mentation system’s formal representation;

4. Convert the argumentation system’s formal representation of the argument into natural language for the end user.

The first translation process represents a significant obstacle and shall be the focus of the next chapter (Chapter 6); however, from Figure 5.3 it should be clear that Walton’s notion of schemes (see Section 2.4.3) is used as a bridge between the expert and the computer.

The remaining three renditions will be reserved for Chapter 7, where all imple- mentation decisions will be analysed. In addition to reporting on the translations,

this chapter will feature a discussion of the toolkit used in this work, the way it was employed, and a more general review of the testbed.

An evaluation of argumentation, and the arguments generated by the system will follow in Chapter 8, with a discussion of the results, and an analysis of this activity, being reserved until Chapters 9 to 11.

5.6

Summary

The conceptual notions of arguments and argumentation were considered during this chapter. The cogitation proceeded by re-examining the use case, and continued through an examination of by which method the argumentation could work, and where the arguments originate from.

Through these explorations it was discovered that the arguments would conclude that a gene was either expressed or not expressed in a tissue, and that it must have two presentations (one for the computer and one for the human end-user). Although there are two obvious choices of which method to employ to proceed with the argumentation, both have limitations. The best option is to use expert opinion to analyse the contents of the EMAGE and GXD databases. Accordingly, the arguments must be initiated from the contents of those databases, i.e. the annotations.

There is a need to map between the communication forms of the biologists, the resources, and the argumentation toolkit. These communication gaps shall be con- sidered in future chapters starting, in the next chapter, with a discussion of how to use the expert’s biological knowledge in the argumentation process.

Chapter 6

Expert knowledge to inference

rules

A brief outline of the approach taken in this thesis is given in Section 5.5. The basic idea, documented in Figure 5.3, involves capturing expert knowledge and un- derstanding of the best ways to interpret and evaluate information in two in situ gene expression resources for the developmental mouse, EMAGE and GXD. The expert knowledge shall be applied to the biological information in the argument generation process. This chapter will discuss the capture of the knowledge, its documentation, and conversion into inference rules for use in the argumentation process.

Figure 6.1 concentrates on the parts of Figure 5.3 relevant to this discussion. It demonstrates that the expert wishes to communicate in natural language, but that the component of the system that uses his knowledge to argue requires its input to be written more formally in logic. An additional issue to consider, is that the expert knowledge needs to be verified, and thus read, by the expert. This forces the knowledge to be documented in natural language. If the system is to receive accurate expert knowledge that knowledge must be documented in a manner that allows fluent conversion into the system’s desired format. As Figure 6.1 makes clear, ostensibly the solution is Walton’s notion of schemes.

This chapter will document the process of knowledge extraction, scheme creation and translation into inference rules. In the first section (Section 6.1) the notion of argumentation schemes will be revisited, and an explanation of their suitability for this work provided. Section 6.2 follows with a description of the process used to capture

Figure 6.1: Exploration of relationship between expert biologist and the argumenta- tion system developed in this work - a simplified version of Figure 5.3.

the expert knowledge and convert it into schemes, before the schemes are examined in Section 6.3. The next section, Section 6.4, chronicles the process of turning the schemes into inference rules. Finally Section 6.5 summarises this chapter.

6.1

Argumentation schemes

Argumentation schemes are discussed in Section 2.4.3; here a short review will be conducted before an explanation of their suitability for this work is provided.

It is the modern interpretation of schemes, as provided by Walton [2], that is utilised here. Schemes are formed from two components. The first is the actual scheme, which is a natural language inference rule of the modus ponens form. Asso- ciated with the scheme is a set of questions, which aid analysis (and criticism) of the scheme’s application, and arguments produced by its employment. One of the most commonly discussed schemes is Walton’s argument from expert opinion, this version

is from [2]:

Source E is an expert in subject domain S containing proposition A E asserts that proposition A (in domain S) is true (false)

Plausibly A may be taken to be true (false). 1. How credible is E as an expert source? 2. Is E expert in the field that A is in? 3. What did E assert that implies A? 4. Is E personally reliable as a source?

5. Is A consistent with what other experts assert? 6. Is E’s assertion based on evidence?

Although there is a close link between these schemes and natural language argu- mentation, it is their secondary purpose of argument generation that is of interest. A scheme can be converted into a range of formal logic rules, for use in an argumentation system, by applying the method documented by Verheij [62] and discussed in Section 2.4.3.

Initially it was hoped that the above argument from expert opinion could be specialised to create a range of schemes similar to the following1:

EMAGE contains an experiment suggesting that X is expressed in Y with confidence score Z.

EMAGE is a leading biological resource in this field.

Therefore we may be“Z-confident” that X is expressed in Y

Where Z represents the result of a mechanism to quantify the quality of an ex- periment. The exceptions and attacks on this scheme may be captured in a range of questions for example:

1. Does EMAGE truly believe that X is expressed in Y ? 2. Does EMAGE really have Z confidence in this statement? 3. Is EMAGE genuinely a leading resource in the current field?

4. What evidence does EMAGE have that X is not expressed in Y ? 5. What relevant evidence does GXD contain?

Although this scheme is too simplistic, it was hoped that a range of specialised schemes, similar to this, could be developed to cover both EMAGE and GXD.

As the argumentation toolkit applied in this work (see Chapter 7 for details) re- solves conflict using the strength of the arguments, calculated from the degrees of belief assigned to the component parts, it is necessary to capture these too. Ideally, there would be some form of agreed confidence measure for the biological data. How- ever, this is not the case. It is impossible to know how accurate the data in EMAGE and GXD is. Accordingly, the degrees of belief must be associated with the rules. This requires the expert to assign a level of confidence to each scheme he creates.