-44-CHAPTER 5DATAANALYSIS

5, 1Classification of ASKs

We have attempted a classification of the ASKs underlying problem statements, using easily computed characteristics of the derived associativestructures.

For the future, what we need is a classification which will

help us to select an appropriate retrieval strategy; that is, a classification with predictive power.
ambitious.

Our first efforts, however, have been less

We have tried to find a classification which is descriptiveof peoples' problematic situations (Wersig, 1971) which can be algorithmically generated.

This classification may or may not be useful for

determining how to resolve anomalies.

If we assume that(i) The representations produced by the text analysis
procedure are closely related to ASKs, and(ii) Types of anomaly are reflected in corresponding types ofstructural features in the representations,
we may expect a classification of representations on a structural basis
to classify ASKs in a meaningful way.

Thus, we present here a classifi-cation based on the (graphic) Association Map Format, and how how it
corresponds to a Bubjective view of the nature of the problem statements.
Both global and local structural features of association networks canbe significant (Kiss, 1975).

Perhaps the most obvious structuralcharacteristic of a network as a whole is the extent to which concepts
are interconnected.

Some networks are highly connected webs of concepts,

others are more widely dispersed or even fragmented.

This feature canbe measured by a connectivity score, which represents the extent to which
the network falls short of being maximally connected.

In the case of

our problem statements, a very simple connectivity score can be used.
Because the number of lines in the Association Map is constant (namely40), we can use the following formula without normalisation:
Connectivity, C = NJa- N .
minwhere Nis the number of nodes present in the network and N . is theaminminimum number of nodes possible, given that there are 40 lines. (Infact, N . = 10). For example, the number of nodes in the network of* mm^
figure7 is 25, so its connectivity score is 15.

Our sample of problem

statements was small, so the scores were pooled to produce 5 classes:
A...0-5; B...6-10; C...11-15; D...16-20; E...21-25.

-45-

Two local structural features (stars and connected components) wereconsidered for use in the classification scheme.

To define the notionof a star, we firstly define (following graph theory terminology, e.g.
Christofides, 1975) the degree of a node to be the number of lines incident with the node.
node of degree 1.

A star is a node which is linked to at least one
The number of stars in a network is the number ofsets of peripheral nodes, which may reflect a difficulty on the partof the enquirer in relating aspects of his ASK.

A connected component

is a set of nodes, any pair of which is joined by a path of links in thenetwork.

In the sample examined, only one structure had more than one

connected component, so attention was focused on the number of stars
present.

In Fig. 7, there are three stars, at the nodes labelled

INSTITUT, TYP and INFORM.
A class code of the form:

CONNECTIVITY CLASS; NO OF STARS was assigned

to each problem statement structure (e.g. the structure in Fig. 7 has
the code C3). A summary of the ASK types in the sample of 27 interviews,
as defined by this classification is given in Table 12. On the whole,
the classification does seem to divide the representations, and hence
the ASKs into meaningful groups in which common features can be discerned.
The eight written scripts, when analysed, produced representations notappreciably different from the oral scripts, and classification revealed
types of anomaly similar to the corresponding types found with the oralscripts.
5.2

Retrieval Strategies

The goal of information retrieval is to resolve those anomalies in apersonfs state of knowledge, which induced him or her to seek informationfrom literature.

Our approach is to select search strategies with expli-cit reference to characteristics of the enquirer's ASK structure.

As ageneral principle this approach is applicable to conventional information
retrieval systems.

The bibliographic tools one chooses to use, and theway one formulates the query should depend on the precision in thedefinition of information need.

A classification of problem statements

along the lines of that discussed above could thus be of use within aconventional framework.

We wish to go further than this, however, andbuild a system with heuristics for resolving anomalies.

Certain featuresof the problem statement structure will be interpreted as anomalous, anddocuments whose structures would help to remove the anomaly will bedisplayed.

The strategies will be used within an interactive environment,so that the system may use the searcher's reactions to judge the appro-

TABLE 1 2 NUMMARY-ROOF ASK TYPES
No. ofGroup interviews

ClassA
Well defined topic andproblem.

(AFTERBROOKS, 1 9 7 8 , P P 8 0 - 2 )Coiranents

01

Concise presentation of problem.
Information wanted for review
articles.

15References wanted to back up
hypotheses on which researchbased.

2

1

Further information about
subject of research required.

2

Problem involves relating two,or more, specific topics

1

3

Fairly general bibliographies
wanted.

3

2

Research clearly described.
Less sure as to what information
required.

1

2

Problem involved trying toextrapolate a set of conditions
from a known to an unknown
situation.

No hypotheses underlying
research.

2

2

No obvious similarities between
the members of this group.

Information needed toproduce directions forresearch.

3

2

Problem not clearly defined.
Information wanted which will
provide ideas for theformulation of hypotheses.

ETopics and problems
not well-defined.

1

32 relate a particular subject toa number of variables. 2 doing
literature search for project
not yet started.

Topics often unfamiliar.

22References not wanted forresearch but to write paper.
Paper deals with known subject
area but in an unfamiliar
context.

3

1

Very fragmented statement.
Neither problem nor research
clearly specified.

B
Specific topics. Problem
well defined. Information
wanted to back up research
and/or hypotheses.

|CTopics quite specific.
Problem not so well defined.
Research still at an earlystage.
|DTopics fairly specific.
Problems not well defined.

V

-47-

priateness of its choice of strategy.

This is important because, until

we can incorporate a model of natural language understanding, we must
assume that the interpretations that we put upon the association networks
are fallible.
In this section we would like to indicate the direction of our ideas
concerning retrieval strategies.

We must point out, however, that thisarea has received little specific attention during our preliminary project.
Let us assume that the retrieval programs operate on structures similar
to the simplified formats generated for the surveys reported above - i.e.
consisting of about 40 associations, divided into three levels of strength:strong, medium and weak.
It would seem, from a study of the structures obtained from problem
statements, that the precise pattern of associations among a strongly
linked group of concepts is arbitrary.

Thus, in order to identify

the significant features, we should condense the network, reducing clusters of strongly linked nodes to single ffsuper-nodes'1.

The following

example illustrates the process - we use the structure = Fig. 7:(i)Strong clusters are defined as components singly-linked at thestrong level, and are denoted by symbols within two concentric
circles.

(ii)

Figure 11 shows the condensed network,

Medium clusters are defined as sets of nodes (excluding nodes instrong clusters) which are singly-linked at the medium level.
These are denoted by symbols within a single circle.

Figure 12

illustrates this condensation,(iii)

Weak clusters can be similarly defined (there are none in theexample).

We now mention a few retrieval strategies which make use of the condensed
networks.

These will involve the matching of terms in selected parts ofthe problem statement structure, within some structural constraint in
the document network.

If A is the set of problem statement terms underconsideration, a matching set in a document will be denoted A .
(i)Strong Clusters
One of the more reliable assumptions about problem statement
structures is that strong clusters correspond to main topics inthe statement.

It will therefore be a part of most retrieval

strategies to select documents containing A

as a cluster, for atleast one strong cluster, A, in the problem statement.
(ii)

Multiple Strong and Medium Clusters
If the problem statement

contains distinct clusters linked by

-48-

INFORM ;SERVIC]****.
?e~oi>LEXPECTis,-oeflA

B = {lNSTi-ruT ; EVALU,FIGURE 11: CONDENSED PROBLEM STATEMENT NETWORK - 1

-49-

KEBV, OFFER,
NEC ESS; EXPECT,L-ITCRA, P E O P L }*K°UNJD

FIGURE 1 2 :CONDENSED

PROBLEM STATEMENTNETWORK - 2

-50associations at a weaker level than those within the clusters
our system might reasonably assume that stronger associations
between the clusters would help to resolve the ASK.

Thus, in

response to a problem statement such as this:®-the first strategy would be to look for documents containing either:or:

It may be that the problem of associating topic A with topic B is
not peculiar to the enquirer, but is an unresolved, or untackled,
problem in the literature.

In this case, the first strategy mayfail. A second strategy would be to select documents which contain:

As a last resort, it may be necessary to retrieve (at least two)
documents in which:@y\occur separately.

andThe enquirer will then have to deduce thelink for her/himself.
(iii)

Medium Links
Nodes, and medium clusters which are connected by medium links
to a strong cluster may specify the context of the main topic.
It would be appropriate to modify whichever strategy is used inconnection with the strong cluster to take account of this.
For example, if the problem statement structure contains:©

0©

the first search would be for documents containing:or:

These structures would be preferred to, for example:

(iv)

Stars
The essential property of a star, for our purposes, is that the-51nodes on the periphery are not linked to each other directly.
This suggests that we should try a strategy which seeks to linkperipheral nodes to each other.

For example, a strategy for theproblem statement structure:

is to look for documents containing:00

where P = jp, q, r, s?
A slightly more general form of the "stars'1 heuristic is to form
the independent node sets of the association network (Christofides,1975) i.e. sets of mutually disconnected nodes - and seek for
documents which contain matching sets as clusters.