1. Dynamic Hypertext: Querying and Linking
Richard Bodner and Mark Chignell
University of Toronto Web: http://www.utoronto.ca/
Interactive Media Laboratory Web: http://www.imedia.mie.utoronto.ca/
5 King's College Road, Toronto, Ontario, Canada M5S 3G8
Email: rbodner@mie.utoronto.ca, chignell@mie.utoronto.ca
Web: http://www.imedia.mie.utoronto.ca/people/rbodner,
http://www.imedia.mie.utoronto.ca/people/chignell
Abstract: There are many models of hypertext, distinguished by a number of factors such as the underlying
semantic data model (link typing and node typing), the degree of dynamic linking in the hypertext, and how
dynamism and other behaviours are implemented. This essay examines a particular approach to dynamism
in hypertext, based on the degree of similarity between a text passage in a source node and the text of a
target node. It reviews work carried out over the past decade in creating systems for markup-based
querying and dynamic hypertext, with particular emphasis on a model of dynamic hypertext that computes
hypertext links on the fly using queries.
Categories and Subject Descriptors: H.1.2 [Models and Principles]: User/Machine Systems-human
information processing; H.3.3 [Information Storage and Retrieval] Information Search and Retrievalretrieval models; H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia-navigation
General Terms: Information Interfaces, Information Systems
Additional Keywords and Phrases: Information retrieval interface, dynamic linking, hypertext browsing
1. Introduction
The idea of linking documents and images to one another can be attributed to Bush [Bush 1945]. Bush envisioned a
"memex" machine, which, among many other things, could assist the scientist in developing associative indexes or
"trails" through a collection of documents. Essentially, Bush described a hypertext structuring mechanism. Bush
viewed collections of links as forming trails through hypertext, or paths that expert trailblazers would build to help
other readers.
Traditional approaches to hypertext assumed that authors created well-defined (pre-defined) paths or trails in
hypertext. When the reader approached the hypertext, the trail was already available. In this approach, inter-node
links changed only when an author either updated or deleted them. The role of the author was to create the hypertext
and the role of the user/reader was to browse through it. Thus, the reader was faced with the task of understanding
the author's mental model of the hypertext documents in order to navigate the collection of linked nodes (hyperbase)
effectively. There was no opportunity for readers to personalize the hypertext according to their interests, or for the
types of links that were available to be adapted to the particular situation or task.
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In the remainder of this essay we discuss the implications of a more dynamic model of defining links in hypertext,
situating it within a decade-long research program concerned with how innovative text markup interfaces can
integrate the tasks of querying and browsing. Our definition of dynamic hypertext will focus primarily on the
dynamism of links, with links being created on the fly, based on knowledge of the corpus and the interests of the
user. In this approach user interests are inferred by recording the interactions (selections made) with the system.
2. Hypertext and Strong Authoring
Conklin describes the concept of hypertext as being "windows on the screen [that] are associated with objects in a
database, and links are provided between these objects, both graphical (as labelled tokens) and in the database (as
pointer)" [Conklin 1987a], p. 17 . In a completely static hypertext, links, once authored, do not change over time.
However, in many contexts, links created for a "one size fits all" mode do not cater to the requirements of particular
users or tasks.
Cooke and Williams [Cooke 1989] , and Baird and Percival [Baird 1989] stated that users should be able to freely
explore a hypertext document and collection via multiple pathways instead of being confined to a fixed structure.
This worthy goal is difficult to implement when links are pre-authored and fixed. No matter how many pathways are
authored, it is impossible to implement all the possible pathways required by each individual user carrying out a
specific task.
A "strong authoring" model of hypertext assumes that the author "knows best". Authors express their vision of
relationships within and between texts (nodes), and readers must then discern this structure and respond
appropriately when navigating the resulting hypertext. Not surprisingly, readers often have difficulties navigating
authors' structures. In amongst all the possible node relationships, one has to follow the particular path set by the
author, a path that may not always seem natural or obvious to the reader. This may lead to cognitive overload and
disorientation -- or a sense of being "lost in hyperspace" (see [Conklin 1987], [Nielsen 1990] and [Parunak 1989]).
However, the embedding of links within hierarchical structures (as is the case in many Web sites) and the use of
orientation cues and maps makes such problems less likely to affect users. Navigational aids, such as maps and
tables of contents primarily provide structural links, and the problems still remain for links embedded within the text
of the node. Due to these deficiencies, a hypertext collection is often supplemented with some sort of search facility.
The use of search facilities typically causes the reader to create a "spiky" navigation pattern (see [Campagnini 1989]
and [Parunak 1989]) or a description). Due to this spiky pattern users navigate in two modes: searching (e.g.,
querying the system) and browsing (e.g., reviewing the documents retrieved based on the previous query). This can
be a difficult process for many users, especially novices who tend to have difficulty expressing their information
need and then judging the relevance of a retrieved document due to insufficient domain knowledge. The user
typically iterates between the tasks of querying and browsing during a search session. In the World Wide Web, for
instance, this leads to a style of interaction where a search engine is used to form queries, followed by browsing
around the Web pages returned by the search engine, followed by further searching.
As the space of electronic documents increase, along with the number of users and the different styles of usage, a
smaller proportion of the many possible meaningful relationships between texts are captured through authored links.
This leads to a need for more personalization and customization of hypertext. This need can be addressed through
the use of adaptive and dynamic hypertext.
3. Dynamic Hypertext and Adaptive Hypertext
A link is a point within one hypertext node from which one can traverse to another node. This general notion of a
link can be instantiated in many ways. One link can point to multiple documents. The link can actually be selected
from a list of possible texts based on a profile of user characteristics (e.g., knowledge of a given topic area) or other
criteria. A number of methods have been proposed for this type of adaptive linking [Brusilovsky 1996], including
link sorting, link annotation, and link hiding. Earlier work on computed and adaptive linking included the implicit
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linking mechanism described by DeRose [DeRose 1989], generalized hypertext envisioned by Bieber and
Kimbrough [Bieber 1992], and the discussion of dynamic adaptation provided by Stotts [Stotts 1991].
Adaptive approaches typically modify the availability of accessibility of links based on characteristics of the user or
task. For instance, in the COOL link model [Wantz 1997], an evaluation function selects from a set of destination
resources (e.g., URLs) in a multi-ended link, based on a user's profile. In contrast, dynamic links are created at runtime rather than being generated earlier (precomputed links) or through modification of or selection from existing
sets of links (adaptive links).
One approach to dynamism in hypertext focuses on computing links based on relationships or similarities between
texts or passages of text. In this approach, the link is not defined as a pointer from one hypertext node to another, but
rather as a query that leads to a different node. Different forms of link types can then be envisioned based on how
and when a query is constructed. Precomputed links can be constructed at any time, whereas dynamic links are
computed at the moment they are required [Ashman 1997]. An example of a precomputed link would be a link from
a politician's name occurring anywhere in a hyperbase (e.g., a website for the Australian parliament) to the
biographical details for that politician contained in the handbook for the legislature [Thistlewaite 1997]. The
advantage of such a precomputed link is that it could regulate all such instances of politician's names, and it could be
updated automatically as the composition of the legislature changed. In contrast, a dynamic query (computed at runtime) can take into account the specifics of the current user interaction. The query might be based on a combination
of the browsing history, user profile, content of the current document, etc. In this approach, dynamic hypertext
represents an intermediate point on a continuum between querying and browsing [Waterworth 1991].
In one form of query-based dynamic linking, queries are marked up directly on texts while they are being read
[Golovchinsky 1993], [Golovchinsky 1993a]. For instance, clicking on the word "hypertext" and then dragging with
the mouse button down to the word "search" (at which the point the mouse button is raised), would form the query
"hypertext AND search". This form of query markup has been shown to yield reasonable results in large-scale text
retrieval tasks [Charoenkitkarn 1995]. The querying interface can be further modified so that users simply click on
previously marked up text or phrases (e.g., marked up in blue with underlines, to imply the presence of a hypertext
link). The text markup is created based on terms that have been previously selected, content-bearing words related to
those terms, and possibly automated indexing techniques (e.g., using term-frequency based measures developed for
information retrieval [Salton 1989]), including phrase identification methods. The sentence that the link (i.e., the
marked up text that was clicked on) occurs in is sent to a search engine as a query. It is assumed that the user selects
a particular link due to an interest in the content surrounding the link. The most relevant node to the query becomes
the endpoint for the selected link. This creates a hypertext "point and click" interface to an underlying search
functionality [Golovchinsky 1997].
Query-based dynamic hypertext can look and feel like a static hypertext system. Interaction consists of clicking on
marked up words or phrases in the text, which are then treated as if they were links. In experimental testing of this
type of system [Tam 1997] [Tam 1997a], users perceived the system to be a human-authored static hypertext, in
spite of the fact that it was actually working as a search system with a highly simplified user interface. For further
description of this query-based model of dynamic hypertext see [Bodner 1997], [Tam 1997a].
3.1 Properties of Dynamic Hypertext
Dynamic hypertext unifies querying and browsing by providing a browsing point and click front-end or interface to
searching functionality. It also avoids the need for mode switching (or spikiness) between searching and browsing,
as frequently happens when using search engines on the Web. The same interface is seen at all times, that is text,
which can be read or clicked on to move to related material which is also presented as a page of text. Moving to
related material is based on a search query. However, the user may interpret this movement as search or as a link
traversal, depending on the look and feel of the interface, or the training or instructions that are provided. The
available research results indicate that dynamic hypertext can be both an effective interface for text retrieval and a
satisfactory implementation of hypertext. Dynamic hypertext, even with future advances in algorithms and
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methodology, is unlikely to challenge well crafted and targeted hypertexts created by human authors. However, for
large scale collections it has the significant advantage of not requiring additional authoring effort once the text for
the nodes or documents in the hyperbase has been written. Dynamic hypertext will generally work best when the
text is well written and when the vocabulary used is reasonably consistent across the document collection.
Query-based dynamic hypertext can be used in conjunction with static or adaptive hypertext since links are simply
added to what is previously available. The dynamic hypertext is also self-customizing, since the links that are
presented by the system, and the implementation of those links, depends on the history of the userÌs interaction with
the system. This "user profile" changes the apparent behaviour of the system from user to user and from session to
session.
The surprising property of the dynamic hypertext system is that the same system can be used as a hypertext [Tam
1997], or as an effective text retrieval system [Bodner 1999]. In this grey area between querying and browsing, the
apparent nature of the system depends on the perception of the user and the nature of his or her task motivation.
The query-based approach to dynamic hypertext is also amenable to "weak authoring" approaches, that allow
authors to express relationships without forcing readers to deal with fixed structures or follow specific paths. These
weak authoring approaches allow authors to specify rules concerning how dynamic links are to be constructed based
on user selections within marked up texts in various contexts.
Arguments in favour of query-based dynamic hypertext include: a simplified interface to search functionality,
reduced authoring effort, and greater opportunity for customization based on the current user's interaction history or
specific task context. An additional benefit is decreased cost of maintenance, since dynamic links do not get broken,
and new material is automatically available for dynamic linking (i.e., automated updating of the implied hypertext
structure is a side effect of dynamic linking). However, disadvantages include computation of each link at run-time
(instead of using stored, precomputed links), which can be expensive in a large system with many users [Ashman
1997]; over-completeness, which occurs when the reader is presented with more links then he/she can comprehend
[Thistlewaite 1997]; and problems with "paradoxical" or false links. False links may occur because of polysemy,
i.e., multiple word senses that lead the search algorithm to incorrectly judge the similarity of text fragments. This
may lead to unsound links [Thistlewaite 1997] in the hypertext. While our experience has been that the advantages
of query-based dynamic hypertext seem to outweigh the disadvantages in the research environments that we have
studied, further research is needed to determine when it should be used in applied settings.
4. Conclusions
The concept of hypertext subsumes a broad family of models that share the concept of links as connections between
nodes (usually segments of text). Several classes of adaptive and dynamic hypertext have been proposed, and the
pursuit of dynamism and adaptation in hypertext continues to be actively researched. Links can be authored,
computed, annotated and filtered, among other operations. This flexibility with respect to how links are created,
computed, and selected is creating new opportunities for personalization and customisation of hypertext. In the
future, we can expect more sophisticated methods of user profiling, semantically richer techniques for building
relationships between different texts (see [Kopak 1999] for a discussion on link types), and new methods of
authoring that define rules for how links might be created or filtered.
Acknowledgements
This research was supported by grants from Communications and Information Technology Ontario (CITO) and the
National Science and Engineering Research Council of Canada (NSERC) to the second author. The authors would
like to thank John Waterworth, Nipon Charoenkitkarn, Bernd Nordhausen, Felix Valdez, and Jim Tam for
contributing to our research on dynamic hypertext. We would especially like to acknowledge Gene Golovchinsky's
role in developing the query-based model of dynamic hypertext. Finally, we would like to thank the reviewers of
this paper for their insightful comments.
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