Link-based Shaping of Hypermedia Webs Assisted by a Neural
Universita' di Catania, Italy
Concordia University, Montreal, Canada
Universita' di Catania, Italy
Abstract: The paper proposes a neural agent that performs
self-organizing classification to assist in searching and contributing
to webs of documents and in the process of document reuse. By applying
the Kohonen self-organizing feature map (SOFM) algorithm to patterns
of influence links among documents it is possible to originate
clusters of documents that help infer the aspects that such documents
implicitly share. The approach complements search techniques based on
semantic indexes. The resulting classification is sensitive to the
multiple aspects of a document that may belong to multiple classes
with a varying degree and allows for treating effectively items that
typically have a limited life span, either because they are means to
the collaborative production of a more complex item, or because they
belong to fast evolving domains. The method has been implemented by
Lotus Notes Domino Web server for a case-based application in the
domain of information systems design.
Key Words: information retrieval, neural networks, hypertext
Categories: H.3.3, H.5.1, I.5.1, I.5.3
Webs of hypermedia documents need support for interactive exploration,
to orient the user and to facilitate effective document retrieval. Among
the solutions that have been proposed recently are perspective walls [MacKinlay
et al. 91], interactive dynamic maps [Zizi &
Lafon 95], dynamic landscapes [Chalmers et al. 96].
Regardless of which specific front-end visualization technique is adopted,
the critical issue for effective use of such webs is finding adequate forms
of a document's organization to reflect the task domain and support different
user typologies. The same collection of
 This is an extended version of a paper presented at the WebNet'97 conference in Toronto, Canada. The
paper has received a "Best Full Paper Award".
documents may benefit from different,
possibly co-existing, forms of organization that become more or less suitable
according to what is the specific goal involved in retrieval, especially
if each document is of a grain size such that its contents involve many
facets. In particular, retrieval for the reuse of documents is a scenario
that deserves attention because reuse is an activity integral to many tasks
that can be supported by the web, such as case-based problem solving and
those tasks that involve the collaborative production of documents (e.g.,
design specifications, building shared models, legal agreements).
Documents can be organized with a varying degree of semantic and structural
constraints [Wang & Rada 95], nonetheless there
are limitations inherent to retrieval based on semantic indexes. In fact,
whether the documents are organized in a conventional database or in a
hypertext, searches based on keywords are not robust because of the "vocabulary
problem", i.e., the fact that spontaneous word choice for the same
domain by different subjects coincides with less than 20% probability [Furnas
et al. 87]. This can be ameliorated by techniques for generating particularly
sophisticated thesauri such as, for example, the concept space proposed
in [Chen et al. 96] or for performing automated semantic
analysis of the text, such as the latent semantic analysis proposed in
[Landauer & Dumais 97]. Without delving, for the
moment, into a detailed analysis of the performance of each of the above
techniques, the problem that remains open is that terms or indexes rarely
support the psychological process of flexible framing of contents [Medin
& Ross 89], and of perceiving their multiple facets. As a result,
the set of documents retrieved after a search often share only a shallow
semantics, in which the context that makes a particular document salient
tends to be lost.
When the base of documents is fast evolving, because of content updates,
or because the documents are temporary means to produce a deliverable in
a cooperative setting, more flexible and evolving classification techniques
are needed. Flexibility is required in order to track a classification
process that is fundamentally emergent and to retain a discriminating power
for the multiple aspects and issues coexisting in a document, or, with
a small leap of abstraction, in a "case". For example, the same
piece of information may become irrelevant with respect to a problem, but
still retain some value with respect to an issue that was unforeseen at
the time of document creation. Also, the same piece of information can
become obsolete or become incorporated in the web in a more refined form,
thus discarding the original source or precedent versions is warranted.
Therefore the shortcomings of index based retrieval techniques with respect
to capturing the temporal dimension of meaning (topicality, obsolescence,
evolution with respect to an issue) are apparent.
On the other hand, it is practically impossible to classify documents
according to all the facets that they may possess, since these facets do
not have an ontological status and mainly emerge from the modes of discourse
adopted by a specific community. Therefore we are faced with a twofold
problem: i) what clustering of documents can reveal the aspects being adopted
by a community for framing experiences, and ii) how can the users enter
the setting resulting from this clustering in a point near to the documents
conveying the aspects being sought? This latter issue is referred to, in
the context of hypertexts, as the "entry point" problem [Carlson
Bearing these problems in mind, the paper proposes an approach that
is complementary to symbolic retrieval and is based on the influence links
that trace the document evolution, and whose regularities may be used to
discover aspects otherwise concealed. The underlying classification technique
is based on a self-organizing mapping [Kohonen 89]
of a web of documents linked by weighted reference relations into a set of neurons to highlight classes according to topological
properties of the original data space and operates on the reference links
that take into account the influence relations among the documents. Such
links are generated by the documents' authors, who acknowledge influence
relations by creating citation links to other documents when contributing
to the web. Reference links are not typed, to avoid incurring in the indexing
problems highlighted above (as the approach of treating a web as a semantic
network would entail) and also because research shows that users resist
creating and using typed links [Wang & Rada 95].
The goal is to let emerge from a geography of links a classification that:
- takes into account multiple aspects of a document, so that an item
can be considered as belonging to more than one class, with a varying degree;
- allows for treating items in the web that typically have a limited
life span, because they are means to the collaborative production of a
more complex item or because they belong to fast evolving domains;
- facilitates searching the web and orients the process of contributing
an item to the document base.
The remainder of the article is organized as follows. [Section 2] briefly
characterizes some approaches based on lexical analysis to improve recall
and precision in retrieval and points out how they stand with respect to
the issues of support for flexible framing of contents and the overall
organization of the base of documents. [Section 3]
describes how the Kohonen self-organizing feature map (SOFM) algorithm
can be applied to patterns of influence links, to originate documents'
clusters that help infer aspects implicitly shared by the documents. [Section
4] discusses how the proposed self-organizing classification assists
in consulting, reusing, and contributing to the web and how conventional
retrieval methods can support the user in approximating the best entry
point of the web. [Section 5] illustrates an implementation of the method
by Lotus Notes Domino Web server and a neural agent performing a Kohonen-like
classification applied to information systems design. Finally, [Section
6] deals with a case study to point out the strengths and weaknesses of
the proposed methodology.
2 Semantic retrieval based on lexical analysis
The two key measures of performance in retrieval are recall and precision.
Recall is defined as the ratio between the number of
documents and all of the existing relevant documents. Precision is defined
as the ratio between the number of retrieved relevant documents and the
total number of retrieved documents. In the classical retrieval techniques,
any set of keywords is able to recall the documents in which such keywords
appear most frequently, and precision is obtained by restricting the retrieved
documents to the ones that share specific keywords, i.e., keywords that
are present in a few documents of the database [Salton
92]. In the modern retrieval techniques, such as the "concept space" for automated thesaurus
generation [Chen et al. 96], any set of keywords
that belong to the thesaurus are able to recall not only the documents
in which the set of keywords appear most frequently but also the documents
in which the keywords that co-occur with the original ones appear most
frequently. This increases the recall power of the keywords but, generally,
it does not increase precision since the ratio of relevant documents to
non-relevant ones is approximately the same both before and after consultation
of the thesaurus [Chen et al. 96]. In fact, keyword
co-occurrence or term co-occurrence with respect to the entire document
has been proved to be unsatisfactory to grasp the "similarity"
between the keywords or the terms of a thesaurus [Salton et al. 96]. For
this reason, techniques in which word similarity is computed based on their
co-occurrence within the paragraphs of the documents rather than within
the entire document (such as the Latent Semantic Indexing (LSI) [Landauer
& Dumais 97]) are becoming increasingly adopted to improve precision
for information retrieval.
Modern information retrieval techniques, including the aforementioned
concept space and latent semantics indexing approaches, are not necessarily
bound to generate a flat list of documents in response to a query. On the
contrary, they are able to map both the query and the retrieved documents
onto a n-dimensional space in such a way that the closer the documents
are to the query, the more likely they correspond to the user's needs.
In particular, the concept space organizes the documents in a two-dimensional
setting by using a Kohonen neural net [Kohonen 89].
The N input neurons of the neural net are exposed to Nt vectors, each input
vector Vi being associated to one of the Nt documents of the base and each
vector's component Vij measuring the occurrence of the j-th term of the
thesaurus in the whole document i. The Kohonen net is trained in such a
way that each vector (i.e., each document) activates only one neuron of
the output array and similar vectors (i.e., similar documents) activate
close output neurons. After the neural network has been trained, any term
of the thesaurus (i.e., a vector having only one non null element corresponding
to this term) or any query issued by the users (i.e., a vector whose non
null elements are only the ones corresponding to the features of the query)
may be associated with only one output neuron. Therefore the two dimensional
array of output neurons originates a concept space in the sense that it
may be subdivided into areas representing similar terms conveying a concept
[Chen et al. 96], whereas any query activates one
neuron that is in an intermediate positions with respect to the ones activated
by the elementary terms contained in the query. In this condition, the
documents that correspond to a given query are the ones that activate the
output neurons belonging to a small area around the neuron activated by
that query. Let us note that the documents retrieved in this way are not
necessarily labelled by terms contained in the query, but also (according
to the concept space approach) by terms that frequently co-occur with the
ones issued by the user. A simplification of this
approach may be found
in [Kohonen 96] where the thesaurus is restricted
to the set of keywords labeling the documents under classification.
So far, approaches that apply the Kohonen neural net have been proposed
to reduce the complexity of n-dimensional classification by mapping it
into a bi-dimensional plane. This allows the discovery of more or less
obvious clusterings but has limitations in pointing out the different kinds
of analogies that may co-exist among the documents. This is the reason why latent semantics analysis
and latent semantic indexing (LSI) use a 300-dimensional space identified
by a specific factor analysis of the word/paragraph co-occurrence matrix.
However, the n-dimensional clustering of the latent semantics approach,
as the mentioned concept space, has essentially a lexical basis that might
fail in pointing out other important ways of clustering (e.g., behavioral,
ontological, emotional) that often are more consonant with the user's query.
In fact, in textual communication important elements (such as the activities,
the situations and the emotions that are distinctive of the practices of
the community addressed by the document) are often left implicit. Even
if these elements are not written in the documents, they are active in
the background and play an important role in framing both narration - on
the author's part, and comprehension - on the reader's part. Some weaknesses
of lexical clustering are also outlined in [Foltz 90],
where it is pointed out that documents close to each other in the n-dimensional
LSI setting cannot always be considered satisfactory responses to the query
issued by the user. Additionally, it must be noted that the lexical approaches
above do not take into account notions of relevance that may originate
from the documents being organized in a hypertext structure, and do not
support the process of contributing to such web.
3 Shaping the Web by Neural Classification
In this section we illustrate a method for inferring a similarity degree
among documents from the information embedded in the references links and
for creating clusters of related documents. The method starts by asking
the author to link every new document to those documents dealing with relevant
ontologies, by using a quantifier I (Influence weight) defined as follows:
I = 0.5 if the new document takes into account some marginal aspects of
the referenced item, I = 1 if the new document inherits several important
aspects of the referenced item, and 0.5< I < 1 for the intermediate
situation. The influence weights in the range between 0 and 0.5 are not
used since they do not produce any practical effect, as explained below.
[Fig. 1a] shows the influence weights placed on the reference links between
There are several ways to classify elements in classes not known
a priori, for example, methods based on the information exchanged between
the new element and the other ones [Alexander 64].
Here we adopt a neural approach based on Kohonen self-organizing networks
(or maps) [Kohonen 89] which aggregates the documents in classes, not known
a priori, that preserve a meaningful topological distribution, i.e.,
in our case, the more aspects are shared, the closer the classes. By this
approach, self-organization of the input documents is obtained by fixing
the synaptic weights among the input and output neurons of the neural network
as resulting from an unsupervised learning process that depends on the
difference between the synaptic weights and the
values of the input neurons,
rather than resulting from a supervised learning process that depends on
the difference between the actual output and the desired one (since the
desired output is not known a priori). Thus the learning process
terminates when the synaptic weights of the output neurons are not significantly
changed by the input vectors that, as shown in [Fig. 1b], we propose to
be associated to the documents in such a way that vector i represents document
i and the j-th component of the input vector i (i.e., Xij) represents the influence degree between
document i and document j. At the end of the learning process a document
i is characterized by a vector Di whose general element Dik measures the
distance between document i and neuron k as follows:
Dik = Const. [(Xi - Wk)T (Xi
being Wk the vector representing the set of the synaptic
weight between the output neuron k and the input neurons. The class of
the document i is the class associated to the output neuron k to which
document i is closest, i.e., the output neuron k that satisfies the following
min Dik, k=1 to total number of output neurons.
The implementation of this algorithm is as follows:
- the reference graph consisting of documents interconnected by the above
influence weights [Fig. 1a] is extracted from the web;
- this graph is transformed in matrix form [Fig. 1b];
- this matrix is given, as the input space, to the neural network [Fig.
1c], whose number of output neurons is set equal to the number of classes
in which we want to classify these documents.
Under these conditions, the neural algorithm not only classifies documents
according to the aspects they share, but also operates a classification
in which the spatial distance among the neurons that represent the classes
mirrors the one of the input space, i.e., close output neurons represent
ontologically close classes. This latter feature depends on the fact that
the learning algorithm reinforces not only the synaptic weights of the
output neuron to which the present document is closest (i.e., the winner
neuron) but also it reinforces the synaptic weights of the neurons in the
neighborhood of the winner (even if by an intensity lower than the one
adopted for the winner neuron).
Let us note that the activation function adopted for the output neurons
is a sigmoid, thus the input neurons whose value is less than 0.5 barely
affect the output neurons; consequently, influence weights between documents
range from 0.5 to 1, unless two documents are unrelated, in which case
their influence weight is zero.
After having presented our classification method it might be evident
that one main difference between our method and other ones that apply the
Kohonen neural net [Chen 96]; [Kohonen
96] is the nature of the input space, since we use an inter-document
influence matrix, whereas the other methods use a term (or keyword) occurrence
matrix. This is an important difference because the influence matrix
us to discover evolving classifications, whereas thesauri or keywords are
fixed a priori and therefore they are not able to capture rapidly
evolving clustering. However, this is not the only difference since we
believe that linear or two dimensional settings are not able to represent
effectively the neighborhood of a class: in fact, the complexity of the
class relations requires that they be represented in more than two dimensional
spaces, some times even in non-Euclidean ones. For this reason when we need to navigate from a class to its neighboring classes we
do not move in a linear or planar space, as requested by the Kohonen approach,
rather we use inter-class links, whose weights are computed by a n-dimensional
formula that measures the link weights between class i and class r as follows:
Lir = [j Erj] / ni where j =
1 to ni
Erj = 1/Drj = 1/[(Xij - Wr)T (Xij
- Wr)]1/2 if Drj > otherwise Erj = 1
where Xij is the vector characterizing item j of class i
(e.g., vector (1,0.8,0.5,1) characterizes item 1 in [Fig. 1b]), Wr
is the vector of the synaptic weights of the neuron representing
class r, whereas ni is the number of the items belonging to
class i. Constant may be fixed in several ways, e.g., as the minimum Drj
or, in the case min(Drj)=0, as the average of the two or three lowest Drj.
Figure 1: a) Space of references and citations in the web; b) influence
matrix between documents; and c) self-organizing feature map W to classify
documents in classes (input space = influence matrix, output space = neurons
representing the classes).
[Fig. 2] shows how the method works assuming a binary
decomposition scheme. Starting from the initial class containing all the
cases (Class1), the algorithm
subdivides it into two classes and then re-subdivides
Class1.1 and Class1.2 in other two classes and so on. Thus, to classify
a new document it is sufficient to start from the class that contains all
the items referenced by the new one. For example, if the new document refers
to items in Class1.2.1 and Class1.2.2 the method restarts classification
When a new document is inserted in the web with its reference links,
the set of the already existing classes to which it belongs is computed.
Thus the neural classification is repeated every time a new document enters
the web; the classes are created and dynamically refined with the web evolution.
If the new document does not belong to any existing class, the author is
invited to introduce a general description (pattern) to provide some clues
concerning the meaning of the newly created class. If the document is placed
on an existing class, but the author does not agree with the proposed patterns,
s/he can add a new version of the patterns that presently denote the class.
If the document belongs to a class not denoted by a pattern yet, the author
is "challenged" to identify a general pattern, which is likely
to emerge if all the documents referenced by the new one with I > 0.8
belong to the same class.
Figure 2: Classification of the documents
in the web by the neural algorithm.
Other outputs of the classification are: a) for each class, a measure
of the interconnectedness Ai of the elements in the class i (aggregation
factor) and, b) for each element j, the degree Eij with which
it belongs to all the existing classes j. A discussion on the possible
ways of measuring these factors is outside the scope of the paper, however,
as an example, we show the ones adopted in our classification algorithm,
Ai= [j Eij] / ni where
j= 1 to ni
Eij = 1/Dij = 1/[(Xij - Wi)T (Xij
- Wi)]1/2 if Dij > otherwise Eij =1.
where Xij is the vector characterizing document j of class
i, Wi is the vector of the synaptic weights of the neuron representing
class i, whereas ni is the number of the items belonging to
class i. Constant is fixed as described above. By this convention, fully aggregated classes are characterized by Ai = 1. Analogously, item
j completely fits to a class i if its Eij=1. Let us note that even if Eij=1,
it is possible that Erj 0 since item j could convey all the aspects of
class i but also some aspects of class r.
Experimental evaluation of the classification method that we have implemented
has shown that binary decomposition of the initial class into 2k
classes (after k successive refinements) is more accurate than the one
step classification obtained by using a Kohonen network with 2k
output neurons, i.e., the N=2k classes obtained by applying
k times the binary classification are more aggregated than the ones obtained
by subdividing the initial set of documents into N classes in only one
step. Depth of classification, e.g., the number of levels, can be fixed
by the user. In any case, classification is stopped when all the subclasses
cannot be further subdivided due to their high aggregation factor (lowest
Adding a new document could modify the structure of the existing classes,
i.e., some old document could pass from a class to a different one. However,
this phenomenon involves only few documents of the existing classes, and
modifies only marginally the structure of the classes. This happens because
as long as classes become consolidated, the links introduced by the new
item are significantly less in number with respect to the existing ones.
The documents that migrate to new or different classes are important to
originate new ontologies or to reinforce the existing ones. At the end
of the decomposition, we have these types of classes:
- classes that are denoted by general patterns, i.e., ontological descriptions
that shape the web (e.g., class 1.1 or class 1.2.2 in [Fig.
2]); such classes are characterized by a high aggregation factor;
- classes that cannot be denoted by a single description, either because
there is no underlying ontology or because their ontology is so ill-structured
that it cannot be expressed explicitly (e.g., class 1.2 in [Fig.
- classes that are denoted by partial descriptions pointing out particular
aspects that can be taken into account when authoring documents that will
be aggregated in the same or the neighboring classes (e.g., class 1.2.1
in [Fig. 2]); such classes are characterized by an
intermediate aggregation factor.
4 Use and Reuse in Self-Organizing Webs
Our scenario, which emphasizes web documents retrieval for reuse, is
inspired by the case-based reasoning (CBR) paradigm [Kolodner
93], i.e., an approach to problem solving based on finding the best
similar "case" matching the current problem and then adapting
it to solve the problem. The new generated case and the "lessons"
conveys can be contributed to the base of cases, which thus learns the
new experience and makes it available for future use.
CBR can be considered an effort in the direction of querying the system
in cognitively plausible ways, by resorting to sophisticated indexing schemes
and to a carefully chosen vocabulary to ensure a proper level of abstraction.
In fact, too abstract indexes may collapse the difference among cases and over-generalize
them, thus providing little heuristic power in finding few best matching
cases; on the other hand, highly specific indexes may fail to capture relevant
similarities. Although indexing has been criticized as not being a psychologically
plausible model of analog retrieval [Thagard & Holyoak
91], still it proves useful whenever the adopted classification scheme
is stable and sufficiently descriptive of the problem and of the domain.
For example, a fixed classification scheme, e.g., indexing, can be adequate
for the retrieval of documents based on stable categories such as authors,
title or date.
The way in which the proposed neural approach is complementary to symbolic
retrieval is explained in the following. By creating a web of documents
linked by references that do not have an explicit semantics but that only
capture strength of influence, it is possible to originate, as discussed
in [Sect. 3], a space that can be dynamically classified
by extending the self-organizing Kohonen map. Following the metaphor of
conventional "folders", one might think of a folder as representing,
more or less explicitly, the aspects shared by the documents contained
in it. The assumption of the paper is that "folders" do not have
an a priori ontological status, and the method attempts to support
the processes underlying the folders origination and evolution and the
placing of documents in multiple folders. This is helpful especially in
two situations : 1) when there is a huge quantity of documents to
scan (consultation mode) and 2) when an author, or a team, wants to place
a document in context (contributing mode). Conventional retrieval methods
allow the user to find some relevant documents, whereas neural classification
helps the user in: a) identifying the folders to which the relevant documents
belong, i.e., the folders that highlight aspects co-existing in the documents
relevant to the users' needs, and b) passing to neighboring folders where
the user may discover other important facets of the problem under study.
In particular, the proposed approach supports a search and retrieval
mechanism based on four main steps:
- first the documents are classified by the neural net described in [Sect.
3] and the interclass distances are computed as proposed in the same
- a document, or a set of documents, is identified based on semantic/lexical
conventional criteria (e.g., by full text search or conventional indexes);
- each document is proposed with the context (i.e., the class) to which
it more strongly belongs;
- finally, the closest classes are highlighted also, to suggest other
relevant items or contexts.
This approach allows us to implement a sort of spreading activation
mechanism that makes it likely to find the information, suggestion, or
item being sought in the surroundings of the initially retrieved documents.
One peculiar advantage afforded by the proposed technique is to provide
Step 1 of the approach has been widely described in [Sect.
3]. How to enter and to navigate the web by taking advantage of the
global forms emerging from the self-organizing classification is discussed
in [Sect. 4.1]. The conditions under which a class can be considered a
stable form that facilitates the search process and the conditions under
which considering only one or few forms may be too restraining for solving
the problem under study are pointed out in [Sect. 4.2].
A concrete example of entering, navigating, contributing and innovating
the web is discussed in the case study, illustrated in [Sect.
6], which is concerned with a community of learners who use a web of
linked design artifacts to get inspiration for solving problems of data
and interactions modeling in information systems design. [Sect.
6] also discusses in detail why the net s topological self-organization
in classes provides an implicit representation of the aspects shared by
the documents classified as belonging to that class.
4.1 Entering and Navigating the Web
Generally, the more structured the documents, the more precisely they
can be retrieved from the set of words (or keywords) that co-occur together
with the query words (or keywords) with respect to the structural units
in which the documents are subdivided [Salton et al. 96];
[Faro & Giordano 97a]; [Landauer
& Dumais 97]. The internal structure of the documents in a web
generally depends on the modes of discourse adopted by the community to
whom the documents belong and on the type of document. For example, articles
are subdivided in sections, the notes of meetings are organized according
to agendas, playwrights progress through scenes, narration consists of
intertwining stories that evolve though episodes. However, many documents
have only a lexical structure, i.e., they are subdivided in paragraphs.
In [Foltz 90] it is shown that retrieval methods
that take advantage of the internal structure of the documents, such as
Latent Semantic Indexing (LSI) [Landauer & Dumais
97], increase both the recall and, especially, the precision performances
of the keywords matching, respectively, by 13% and by 26%. Given that the
figures for keywords matching are about 65% for recall and 54% for precision
[Chen et al. 96] we can expect about 70% of average
recall and 65% of overall precision by using LSI.
However, meaningful results can be attained also by using a full text
searching engine and a contextualized filter [Bourigault
92] consisting of a set of words (and their synonyms) each referring
to a unit of the document. This is especially true if these units are connected
in such a way as to form a unique context, such as, for example: the first
word refers to the title/abstract of a paper, the second one to the title/introduction
of a chapter and the third one to the title/body of a section. In this
case it is reasonable to expect some increase in the above performances
with respect to the uncontextualized filters powered with some synonyms,
whose recall and precision typically are, respectively, in the range 15%-30%
and 30%-50% [Salton 92]; [Chen et
Thus we can assume that a reasonable order of magnitude of
the performance of contextualized filter-based searching is 30% in recall
and 50% in precision, i.e., that performance is at least shifted towards
the highest levels of the ranges above.
By entering the web by a contextualized filter, firstly we have to discover
the relevant documents (about 50% of retrieved items) and then we should
move around these documents to increase recall without greatly decreasing
precision. Local links certainly assist in understanding better the meaning
and context of the retrieved documents, but the mechanism of local exploration
is costly, thus it is recommended to resort to it especially when the organizing
principle underlying the current class (returned with each retrieved document)
is not evident yet. This is likely to occur when the documents are
not stable, or when the user has some difficulty in framing the search
problem. In this sense, after entering the base of documents it may be
helpful to navigate in the neighborhood of the retrieved documents by using
the hypertextual influence links proposed in the paper, whereas using links
automatically generated by the lexical similarity of documents, such as
the ones proposed in [Goffinet & Noirhome-Fraiture
96], would allow the user to visit only lexically relevant documents,
thus loosing many other documents important for the query. However, as
long as the documents' configuration, following the self-organizing classification,
evolves towards more stable forms, local links become less useful in the
search process, even if they still play a role in letting the "global
forms" of the web emerge. As discussed in the previous section, the
evolution towards clearer forms of the documents organization will be
determined by the insertion of new elements that will update the pre-existing
configuration of links.
Identifying stable or emergent global forms may improve recall and precision
in information retrieval. In fact, knowledge of what are the other items
of the classes, say entry classes, to which the entry set documents belong
facilitates discovering the most relevant documents among the ones retrieved
by the full text searching mechanism mentioned above. As explained above,
to identify the relevant documents in the entry set amounts to having more
or less 1/3 of all the relevant items. The other 2/3 of the relevant documents
may belong to the entry classes or to the neighboring classes. Thus a good
heuristic may be moving from the set of the entry classes to the neighboring
classes only if the total number N of documents of the entry classes is
significantly less than three times the number M of documents in the entry
set, i.e., N<< 3M. In this case the other relevant items have to
be discovered in the neighborhood of the entry classes. If N >> 3M,
it may be useful to decompose the global forms into sub-forms that make
more specific aspects emerge, as discussed in [Sect. 3].
Of course, N 3M is the termination condition for the
searching process. This way of proceeding aims at recalling more or less
all the relevant documents and at discovering the most significant facets
for the user query.
If the query is not expressed by significant words
and synonyms or if the documents are either not structured or structured
by a superficial framing, precision and recall can dramatically decrease.
Low precision makes it difficult to discover important items among the
ones of the entry set, whereas low recall may increase navigation in the
neighborhood of the entry classes to find other important facets for the
query. In these cases using more sophisticated thesauri (e.g., the concept
LSI) could be, even if it is costly, the only way for obtaining
an entry set that facilitates the discovery of the most relevant items
for the query.
4.2 Emergent vs. Stable Global
It must be noted that meaningful global forms tend
to emerge and become stable when a huge quantity of interrelated documents
is available. Under these circumstances large scale dynamic classification
cannot be the sole responsibility of a human processor. In real life, a
small scale approximation of the dynamic classification process occurs
when a problem is framed and solved by incorporating incrementally the
suggestions coming from peer reviewing and expert consultations, each suggestion
highlighting some particular aspect of the problem. The process validity
increases when the number of consultations increases, and when everybody
is aware of each other's suggestions, as in a meeting or brainstorming
session. This is quite rare and quite costly, but, fortunately, CSCW technologies
and models now make it possible to collect contributions in a shared electronic
environment, in which the role of the above neural agent is justified,
also to support the asynchronous sharing of experiences for reuse.
However, reasoning on a knowledge base for reuse
cannot take place successfully if one is not able to manage the contradictions
that are inevitably present in every dynamic collection of documents. This
reverberates as a potential weakness in the use of global forms for search
and retrieval. Solving this problem is not easy. In fact, contradictions
can arise because of item "misplacement", or because the item
contains errors or misconceptions. The first problem can be solved by a
finer classification of the space of the documents, or by "migration"
of the item to a more appropriate partition of the documents' space. The
second one can be solved either by document elimination or by amendment,
to inhibit the creation of a new class that would be based on faulty hypothesis.
Related to both these points is the observation that neural classification
assists in managing the documents' space growth by allowing the elimination
of obsolete documents only when they belong to classes that are consolidated
in stable ontologies, thus keeping the overall web organization stable
in spite of the deleted links. Thus, on the one hand, neural classification
of a documents' web assists the user in discovering contradictions by comparing
an item with the other items of a class and, on the other hand, it avoids
eliminating "productive" contradictions, i.e., ones that may
let emerge other forms of documents' organization in which they are possibly
resolved (web progress).
Another important consideration in reusing experience
is whether one has to restrict the analysis to the items (and related classes)
directly linked to the user s problem or whether consulting other items
may be fruitful even if they are not linked very precisely to the query
issued by the user. Precision and recall are certainly important performances
when the web is used within the context of a bibliographic search, for
example to know the state of the art of the problem under study (as in
the scientific practice) or to identify relevant precedents (as in the
legal practice) or to highlight the origins of a question (as in the historiographic
practice). On the other hand, in the context of CBR-like searches, especially
for design purposes, re-using
experience embedded in documents retrieved
under "quasi-perfect" precision and recall may promote uniformity
or idiosyncratic ways of approaching design problem-solving. Thus a certain
degree of fuzziness in information retrieval may be desirable in some cases
so that opportunities for incidental learning are not cut off [Levitt
March 88] and the user can
be exposed to items that are possibly suggestive of new aspects or solutions
even if they do not appear relevant immediately. This implies that when
the web is used as a shared design memory it is important to encourage
the users to enter the web by assuming an exploratory disposition towards
a priori "not relevant" documents that may be conducive
to new solutions. For this reason if all of the entry classes in response
to a query are stable global forms it may be fruitful to explore the neighboring
classes even if the condition N<< 3M does not hold. This suggestion
supplements the heuristics discussed in [Sect. 4.1].
If the innovative links generated in this way among the documents are gradually
reinforced by the consensus of the other users, such links may underscore
the development of new practices within an organization.
5 Self-organizing Documents'
Webs in a Lotus Notes Based Environment
A Lotus Notes based environment, called StoryNet
[Faro & Giordano 97b] for the collaborative
production of documents structured in stories and episodes, has been enhanced
by a neural agent performing classification according to the SOFM algorithm
previously outlined. The StoryNet's architecture has been conceived to
manage evolving systems, and is proving useful for information systems
(IS) collaborative design. The rationale for the story based organization
is that in such a format experiences can be cognitively represented and
recollected [Bruner 90]. In the application of StoryNet
to IS design, a project consists of a set of use stories and episodes of
the information system. Each episode is linked to the ones it refers to,
and may be reused for specifying analogous episodes. The episode's categories
(title, assumptions, what, who, why, when, where, rituals, how, what can
go wrong, exception handling ) are used as a probe to extract the episodes
that best fit the specific design needs [Faro &
Giordano 97b]. After adapting these episodes, the designer inserts
the new episodes in StoryNet. To support reuse, any new document should
be inserted as a justified evolution of the previous ones, i.e., as an
enhancement of the experience already captured in the knowledge base. This
can be pointed out in comments mediating the references links.
StoryNet has been implemented by Lotus Notes Domino
Web server, to afford easy access to the designers without requiring a
Lotus Notes client. The story-episode organization is easily supported
by the Domino Web server, as it is capable of full text search on all the
documents. To link episodes belonging to different stories it is necessary
to extend the Domino Web server by a suitable software library of C modules
that supports the referencing process as follows :
- the designer first creates special documents to
comment the episodes that have been proposed as the result of the search;
typically only the subset considered
potentially relevant to the current
purposes is marked by a comment ([Fig. 3], step 1);
- while detailing the new episodes, the designer
may scan the comments for possible suggestions ([Fig. 3], step 2);
- after having specified the episode, the designer
creates references to the comments that were taken into account ([Fig.
3], step 3).
Figure 3: Supporting the referencing
process in StoryNet (Ei = episode, Ci = comment, Ri = reference).
Passing from an episode to its comments and to its
references is supported by the Domino Web server facilities; passing from
an episode to the referred ones is supported by the above extension. For
example, to pass from a Enew to its referenced items one can obtain the
list of all the references, i.e., R1and R2 , then pass from Ri to Ci
by simply clicking a special field inside the reference Ri. After reaching
Ci it is easy to pass to episode Ei by the Lotus Notes facilities.
[Fig. 4a] shows how the user can navigate from a
document, e.g., "driving lesson reservation", to its source,
e.g., "flight lesson reservation", via a reference link. Episodes
are organized in a graph whose oriented arcs are labeled by a number measuring
how much an existing episode has influenced the new one. The graph is put
in a inter-episodes influence matrix stored into a file external to StoryNet,
to be elaborated at regular intervals by the neural agent. The agent stores
the hierarchical classification of the episode into another file, so that
StoryNet can superimpose this classification scheme on the existing episodes.
The current version of StoryNet labels each episode by the lowest level
class it mainly belongs to, and provides all the classes to which the episodes
belong. [Fig. 4b] shows the StoryNet's user interface for the
results. Note that "driving lesson reservation" and "flight
lesson reservation" belong to the same class, due to the reference
link. If an episodes belongs to a class with a degree greater than 0.8, the two
relevant lowest level classes are shown too.
Figure 4: (a) StoryNet reference links;
(b) StoryNet classification performed by the neural agent.
6 Case Study
In this section we illustrate how the proposed method
works for a realistic Web consisting of 150 design documents whose links
simulate the consultation process of the students of a course in information
systems design. In particular, each document describes either a "use
story", i.e., a story specifying the scenario of a use case of the
information system as a sequence of episodes, or a "use episode"
detailing the actions of the users to achieve goals productive for the
progress of the higher-level scenarios. According to the method exposed
in [Sect. 3], two documents are linked if the old document
was deemed relevant by an author for producing a new design.
This example refers to a simulation performed during
the preliminary phase of StoryNet, during which it was important to prove
the feasibility of the method. Presently StoryNet is populated by about
1500 documents and an accurate evaluation is in progress to highlight the
subtle process of concept formation and knowledge transfer within a community
of learners. However, the simulation is illustrative
enough to clarify
the strengths and weakness of the neural classification and the related heuristics to support the reuse of experiences.
The documents of the simulation belong to the following information systems'
- Community development in the city
- Hospital first aid department
- Walk-in clinic
- Music (CDs and recordings) store
- Musical instruments store
- Electronic equipment store
- Video rental service
- Photographic studio
- Car rental service
- Automobile equipment and supplies
- Ski school
- Vocational training center
- Conventions and exhibits center
Each of the above projects consists of about 3 use
stories, each consisting of about 4 use episodes. For example, one story
of the "Ski School" is "Equipment rental", which entails
the episodes of "Reserving the equipment", "Equipment preparation",
"Pick-up and Payment", and "Restitution". In the simulation
each use episode is linked to the story to which it belongs (with weight
0.5) and to the relevant (with weight 0.8) and highly relevant (with weight
1) episodes or stories of other projects. The overall number of links between
episodes belonging to different projects is approximately 150. This number
was purposely so small to reflect the average number of links that the
students tend to deploy, as it was observed in StoryNet. A discussion on
how to facilitate the insertion of links is outside the scope of the paper,
however, because links affect both the recall and precision of the method
it is useful to report that particular attention was devoted to this problem
by interviewing the students about the reasons of the apparent reluctance
in placing the links. From the analysis of their responses we have concluded
that the majority of the students are interested in consulting the links,
and don't consider difficult the identification of the relevant sources
of inspiration, if any, of their projects [Giordano 98]. Rather, it is
the process of linking documents that is considered to be somewhat laborious.
For this reason we are improving the interface for linking the items and
we expect that the present linking hindrance will be overcome with the
next version of StoryNet.
Now we turn to explain the method in practice. Let
us assume that we are interested in designing the registration procedure
to a training course. In this case it is quite natural to issue the query
"course AND registration" to the Lotus Domino full
engine. This query behaves as a contextualized filter since "course"
refers to a story and "registration" deals with
an episode of this story. After processing the query, the engine finds
the four items that seem to address some aspects relevant for query, that
I1) registration to a course organized in the convention
I2) registration to vocational courses (story)
I3) application for registering to the first year
of a vocational course (episode)
I4) application for registering to the second year
of a vocational course (episode).
By subdividing the 150 items in 5 classes applying
the proposed neural classification we find that I1 belongs to a class containing
18 items, whereas items I2, I3 and I4 belong to another class containing
25 items. Thus we are faced with 43 items, whereas we expect [see Sect.
4] that the relevant items be about three times the number of the items
of the entry set, i.e., about 12 documents. Therefore, following the heuristic
proposed in [Sect. 4], we further subdivide the above
5 classes into 5 inner sub-classes thus partitioning the 150 documents
into 25 classes. In this case we obtain that I1 belongs to a class, say
"A", containing 5 items, I2 and I4 to a class "B" containing
3 items, and I3 to a class "C" containing 7 items. By inspecting
these classes we find that:
- class A is especially dedicated to the problem
of allocating space and staffing to support a course in a convention center,
or ski runs and instructors for the ski school;
- class B deals with the problem of students enrollment
in vocational courses;
- class C deal with the problem of students enrollment
in courses held in the convention center.
Class A is of partial interest for the query issued.
On the contrary, both classes B and C convey relevant aspects. Since the
partial interest towards class A may be interpreted either statistically
(i.e., about 3 of the 7 items of class A could be of interest) or as a
fuzzy expression (i.e., each item of class A could have some aspect of
minor interest) we decide to weight class A by 0.5. Classes B and C are
weighted by 1, thus obtaining about 11 relevant items for the query. Since
this number is of the same magnitude of the overall number of the expected
relevant items (i.e., 12), we could decide to terminate the searching process.
Let us note that in this case we have taken advantage
of the high precision of the entry set. Generally, we may have less precision.
This causes a higher effort in identifying the relevant items among the
ones of the entry set and increases the need for navigating in the neighborhood
of the entry classes.
However, as pointed out in [Sect.
4], entering the web by a crisp query, as is the previous one, can
obscure some other relevant aspects. Thus students with a broadening attitude
could decide to repeat the searching process by issuing a more general
query consisting of only one word, i.e., "registration". After
new query, the Lotus Domino engine finds 7 items: the four
mentioned items I1, I2, I3 and I4 plus three new items. Two of these new
items, say I5 and I6, do not produce any interesting consequence since
they belong to the class A mentioned above. On the contrary, the third
new item, say I7, deals with the registration at a distance to the activities
organized in the convention center, and thus is useful to point out another
aspect relevant to the query, i.e., the one of allowing people to register
to a training course either by post or electronic mail. This aspect emerges
because after partitioning the 150 documents into 25 classes we would find
item I7 belonging to a class "D" whose seven items aim at providing
services via data nets.
By applying the heuristics mentioned above and weighting
the items of class D by 0.5, we would find that the number of the retrieved
relevant items is about 15, which is not so close to the expected number
of the overall relevant items (i.e., 21). Thus we could decide to continue
the search by adopting the exploratory disposition typical of Web's navigators
rather than the point of view derived from the traditional information
retrieval methods. In this case the neural classification has to be considered
as a way suggestive of interesting routes to discover relevant aspects
unforeseen a priori.
Accordingly, we have to focus our attention not
only on the classes to which the documents initially retrieved by the full
text-searching engine belong but also on the neighboring classes, and in
particular to the ones that are more tightly linked to the entry classes.
[Fig. 5] shows the classes' geography around the entry classes A, B, C
and D mentioned in the discussion above. These classes are surrounded by
six other classes whose links significantly differ from zero. Let us note
that the neural agent computes the links' weights Lir between classes i
and r by using the formula introduced in [Sect. 4].
To increase precision, it may be useful to apply
the following formula:
Lir = [j Erj] / nI
by imposing that j ranges over the set
of the nI relevant documents belonging to class i; this avoids
to pass to classes that are neighbors of the initial ones but are not close
to the relevant items. By reflecting on this geography and on the related
descriptions it seems quite natural that one may perceive the suggestions
emerging from the shared design memory as follows: "The main aspect
of the problem of registration to a course deals with the procedures for
managing the applications coming from users (see entry classes B and C).
However, some special consideration should be given to the procedures that
facilitate the registration of people who have taken part to previous courses
(see classes E and F surrounding class C). Registration at distance could
be supported (see entry class D and its neighboring classes G an H), whereas
allocating necessary resources and people has not to be overlooked (entry
class A, and its neighboring classes I and L)". Concrete suggestions
for implementing such a scenario can be found in the items belonging to
the classes already mentioned .
Figure 5: Class geography around the entry
classes A, B, C and D. These classes are retrieved by the neural agent
in response to the query "registration AND course".
7 Concluding remarks
The use of neural networks for assisting people
in finding information is becoming increasingly diffused. Kohonen networks
allow the classification of web documents in a two-dimensional setting
that has proved useful for information retrieval. Recall of relevant documents
may be increased if one navigates in the two-dimensional setting. Also
Hopfield neural networks have been used with the aim of increasing recall
[Chen 96]. However, all the existing neural methods
suffer from the lexical nature of the input space and from the reduced
dimensions of the classification setting. The method illustrated in this
paper aims at overcoming these limitations by proposing an input space
that consists of documents interrelated by influence links and by referring
the classes identified by the Kohonen net to a n-dimensional setting. This
allows fine-grained navigation to discover relevant documents and allows
meanings implied in the evolution of the issues
and aspects dealt with in the documents.
The neural agent has been tested on a small scale
set of documents produced for information systems specification, and has
generated classifications deemed plausible and useful for guiding searches.
We are currently working at large scale testing in the context of collaborative
design assisted by webs of design cases and at testing heuristics for deploying
the links and policies for document elimination. If performance of the
neural agent scales up, the next step is to find more effective visualization
techniques to reflect the classes' topology and for highlighting items
belonging to multiple classes.
Moreover, some studies are being conducted for identifying
if and when automatic descriptions of the classes can be made available
to the users without introducing unnecessary biases in their perception
and exploration process. Finally, the efficiency of an algorithm that avoids
repeating neural classification from scratch is now under evaluation. In
particular, what conditions of the documents distribution in the space
generated by the neural classification warrant a new global classification
rather than a local re-arrangement when a document is inserted or deleted
from the Web is another related issue now being investigated.
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