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ABSTRACT: Hypertexts may be implicitly structured, based on either
node content or context. In this paper, we examine implicit structures
that rely on the interpretation of node's spatial context. Hypertext
authors and readers can perceive and understand these idiosyncratic
structures, but, because they are implicit, they cannot be used by the
system to support users' activities. We have explored spatially
structured hypertext authored in three different systems, and have
developed heuristic recognition algorithms based on the results of our
analyses of the kinds of structures that people build. Our results
indicate that (1) recognition of implicit structures in spatial
hypertext is feasible, (2) interaction will be important in guiding
such recognition, and (3) the hypertext system can provide layout
facilities that will render later systematic interpretation much
easier. Found structures can be used as a basis for supporting
information management, as a straightforward way of promoting
knowledge-base evolution, as a way of solving representational
problems endemic to many hypertext systems, or as a basis for
collaboration or interaction. KEYWORDS: implicit structure, spatial hypertext, link automation
Since then, many assumptions about hypertext have relied on this groundwork, expanded over time to include a broader notion of what linking is. Halasz's recently reconsidered definition of hypermedia referred to nodes as being "explicitly or implicitly organized into one or more structures." [Hala91] Thus, links and networks have given way to a newer, more general, notion of structures that may sometimes be implicit. But how are implicit hypertext structures realized?
In this paper, we examine the role of structures in hypertext and some ways and reasons that hypertexts become implicitly structured. We explore the spatial arrangement of text as an alternative means for creating hypertext, and look at some examples authored in existing hypertext systems to give us insight into the kinds of hypertextual structures we might find in space. Based on these examples, we have developed heuristic algorithms for interpreting spatial hypertext structures. We briefly describe these algorithms and suggest ways they might be applied to find the missing links.
Links also serve a second crucial function: they are a representational form. They can be used to articulate specific semantics for interconnection. Typed links and nodes in IDE [Russ88], gIBIS [Conk88], and MacWeb [Nana91], relations in Aquanet [Mars91], and typed nodes and links contextualized by activity spaces in Sepia [Stre92] are all good examples. These links make structure explicit, and are thus useful in writing, argumentation, problem structuring, or capturing semi-formal knowledge representations of a domain.
In this paper, we focus on structure not as a means of traversal, but rather as the rhetorical and semantic basis for hypertext.
Where does structure lie? Can it be inferred from the content of nodes or documents, as navigation tools such as Superbook [Egan91] suggest, or is it defined by context -- how the nodes are organized and used, either implicitly or explicitly? At one extreme, content-based structures can be computed on the fly, and are never explicitly present in the hypertext. At the other extreme, in systems that treat structures as first-class objects, explicit connections can be created independent of the nodes that they connect [Gron92].
How is structure constrained? Is it ad hoc, is it constrained by authors, or is it constrained by the system according to a cognitive model of the activity the system was designed to support [Thur91]? We find examples along a continuum. Unconstrained hypertexts are the norm, but embedded constraints -- whether they are defined by authors of the system or authors of the hypertext -- can help effect coherence and consistency. In a few cases like HyperSet's taxonomic sets [Paru91], type constraints define the structure; hypertext structures are determined on the basis of type.
Finally, how is structure conveyed? Does it exist only in the reader's head? Is it generated by the system in the form of a structure map [Bern91], or is it recorded in a spatial layout by the author? Once again, we can find great variation: systems where structure is never made concrete to the reader, systems where structure is conveyed graphically to the reader, but cannot be directly modified, and systems where graphical forms can be manipulated by the author to modify underlying structures (as in NoteCards browsers [Hala87]). In some cases, the layout is the structure; for example, NoteCards tabletops [Trig88] constrain their constituent objects (in this case, nodes appearing in windows) to be arranged on the screen according to a user's specifications.
In this paper, we examine implicit structures that do not rely on system interpretation of content, but rather on interpretation of spatial context. The structures that we are concerned with are freewheeling, although they are often subject to authors' visual conventions or self-imposed layout constraints. These structures are conveyed graphically, in a manner often orthogonal to a hypertextual framework.
Within these dimensions, many hypertexts involve explicit, extensional links; connections are individually defined. Autolinking strategies may yield implicit intensional links (such as in Perseus [Mylo90]). Other forms of virtual linking, such as the Analyst's rule-based linking program Assistant [Anal89], IDE's templates [Jord89], and McCall's virtual structures in PHIDIAS [McCa90], are more in the spirit of explicit intensional links; explicitly defined kinds of connections are computed. The hypertexts we consider use implicit extensional links -- idiosyncratic connections intended by the author, perceived by the reader, but not necessarily stored, nor recognized by the system. Because the connections are not defined explicitly, but inferred by computational analysis of a particular hypertext, these structures extend and sharpen DeRose's distinctions.
For this purpose, we can conceive of hypertext as using contextualizing forms other than links. Nelson introduced the idea of "stretch text" [Nels84] as one way of presenting text in context, but in this paper, we will look at mechanisms for implicit structure in another way. Spatialized text, explored in some depth by Bolter in his ECHT `92 keynote address [Bolt92], has already demonstrated its ubiquity in many of today's hypertext systems (as well as other presentational forms [Tuft90]).
In our own experiences with Aquanet, a hypertext system that allows authors to create graphical/ textual objects in a shared information space, we found spatialized text to be a primary mode of creating hypertext; people created spatialized text in preference to using Aquanet's relational model for semantically and graphically expressing interconnections [Mars92].
In spatialized text, a reader/author can perceive the intended structures in space, just by noticing their geometrical relationships ("if this text is close to that text, then the two must be related"), visual characteristics ("if this text is in a twelve-point italicized serif font, it must fill an annotative role similar to the other twelve-point italicized text that's an annotation"), and recurrence ("this segment of text means something completely different when I read it over here"). If this structure is perceived by heuristic algorithms, it can then fill the same function as explicitly represented structure.
We encoded the data in a uniform representation to capture spatial and visual aspects of the text objects; relative planar location and extent of each object was recorded. Each object was also assigned a type based on distinguishing graphical characteristics. For example, to encode the NoteCards sketches, we assigned one of three types to each textual object according to whether its font was regular, bold, or italic, and calculated its approximate extent according to number of lines and maximum line length. VNS objects were encoded according to representational type, font, and conversations with the author about their intent. Aquanet objects required no additional interpretation to fit into our data analysis framework.
The NoteCards samples we collected resulted from an anthropological data analysis task that took place over slightly less than a month. Since the author confined himself to text and a few graphical notations like circles and arrows, the three examples pertaining to this application provided us with what can literally be called spatialized text; all structure (including the application of font styles, alignment, drawn annotations, and organization of text items) was created ad hoc by the author and never recorded as hypertext.
Figure 1. Spatialized text in NoteCards.
Figure 1 shows a schematized version of one of his NoteCards sketches; text has been replaced by boxes that cover the same area as the text. Shading indicates whether the text was bold, italic, or plain. Graphical notations have been preserved.
This example contains 35 individual pieces of spatialized text. One recurrence -- the two boxes with the heavy borders -- is the only text object-to-text object link. It is noted graphically as well, by a two-headed arrow. A second graphical link connects two of the higher-level structures. The clusters of objects are both apparent from layout density (we see 5 distinct areas), and they are noted graphically by drawn ellipses (as well as a rectangle and lasso).
If we consider layout constraints like alignment, we can spot several lists or sets. The group of objects in the box in the lower right corner is very probably a named set or labelled list, since the aligned elements all appear to be of the same type. If we consider the visual typing, we can see that the top three encircled groupings are probably a type of composite -- the layout pattern "crosshatched box above gray boxes above a striped box" is repeated in all three. If we examine the lassoed objects, we might infer that text replaced by diagonally striped boxes refers to the text it is next to (shown as gray boxes).
So in our first example, structures using layout constraints, reference, and type constraints are all created outside the hypertextual framework provided by NoteCards.
Figure 2. Spatialized text in the Virtual Notebook System.
In our VNS example, 7 or 8 collaborators used the system's hypermedia capabilities to track system bugs; Figure 2 shows an encoded version of the page. The page contains 28 textual/iconic link markers showing links to other VNS pages, 10 image objects that implement a tabular layout, column headings that are a composite of numbers and text, and miscellaneous blocks of text and link markers (near the top of the schematized version shown in Figure 2). A total of 63 objects were arranged on the page when we collected this data, after six months of use. We could perceive six distinct types.
In this example, we see how the multiple authors have spatialized text to create recognizable structures orthogonal to the hypertext. All of the boxes in Figure 2 represent some kind of text, whether it's within a link marker, or just a textual object. The link markers are mostly in the columns (although some appear in a separate list at the top of the table). Because the authors have agreed upon a conventional structure -- a table -- the space retains mutual intelligibility to the many authors. Some graphical notation is used to delineate the rows and columns.
So in our second example, reference is still done hypertextually, but structures are composed through spatial layout and graphical notation.
Figure 3. Spatialized text in Aquanet.
We originally looked at four different Aquanet discussions (although subsequently we have run the structural analysis algorithms we describe on other Aquanet layouts). The most complex was the analysis of recent activities in the field of machine translation, an application we describe in detail in [Mars92]. Others include a bug tracking discussion very much analogous in function to the VNS example we described earlier, a collaborative look at Spanish-English machine translation software (a portion is shown in its encoded form in Figure 3), and a discussion we used to organize topics for a paper. Subsequent applications that we evaluated involved the same types of structures that we found in our eight examples.
Two of the four Aquanet examples had the unique property of having grown large enough to have distinct structural regions and many complex higher-level structures (structures formed of other structures). Figure 3 shows one of the simple cases of this. Note that there are 16 roughly similar composite structures. These 16 composites can also be seen as a single set, a higher-level structure.
Aquanet does not support "drawing on the space," so there are no graphical hints about how the space is organized. Because there's no alignment or gridding, the structures take on a very informal appearance (in contrast with our other examples of spatialized text). Our more complex Aquanet examples use recurrence to provide reading context for the objects.
So in our third example of spatialized text, typing and reference are done within the framework Aquanet provides; constraints on spatial layout are enforced informally, by the user.
Spatial hypertext structures like aggregates can be perceived strictly on the basis of proximity. Other kinds of hypertext structure, such as ABC's lists [Smit91], can be perceived without links by relying more on conventions of spatial orderliness. So some hypertext structures can be identified strictly on the basis of how they are laid out.
Spatialized, HyperSet's taxonomic sets [Paru91] might be perceived through proximity, but they could be identified visually, based on apparent homogeneity and heterogeneity of objects. More general kinds of hypertextual structures like composites rely on the distinguishability of multiple types, in addition to uniform relative proximity.
Finally, perception of referential structure can come from graphical cues such as enclosing or connecting arcs and arrows. The lack of such graphical cues does not necessarily mean there is no referential structure, but that any such structure must be discovered by considering larger patterns of proximity and typing.
Figure 4. Proximity-based structures: (a) an aggregate, and (b) a list.
We define aggregates to be textual objects near each other or piled on top of each other, items that share a spatial context. Figure 4 shows two examples of proximity-based structures, abstracted from our Aquanet examples. In practice, proximity analysis of all of our sample spatial text arrangements extracted useful kinds of structure.
Identification of proximity-based structures can also take advantage of different layout characteristics. For example, if all the textual objects are aligned vertically or horizontally, then we might have a list. If they are aligned both vertically and horizontally, we might have a table (of the sort demonstrated in [Mars87]). We located meaningful, intentionally aligned spatial structures in all eight of our samples.
Because all of these textual objects are treated equally under proximity analysis, they can be created without any preliminary distinctions, not even between links and nodes; all explicit elements have become equal.
Figure 5. Layout and type-based structures: (a) a taxonomic set and (b) two instances of a composite.
These distinctions, used in conjunction with layout characteristics (proximity and alignment), can form the basis of the systematic detection of structures like taxonomic sets (sets where all the members belong to the same category). Figure 5a shows an example of a taxonomic set identified based on homogeneity and proximity; of course, proximity is inessential to identifying taxonomic sets if there is a well-developed notion of type.
Considering relative spatial positions in conjunction with type allows us to readily identify composites. By Halasz's definition, composites are higher level hypertext constructs that include (rather than reference) other hypertextual objects [Hala88]. Gronbaek and Trigg advance this definition by proposing structured composites [Gron92]. In our spatial framework, we take this to mean that composites can be defined with both layout constraints and slot constraints; in other words, an instance of a particular type occupies a known position in the composite structure. Figure 5b shows two instances of a particular type of composite; the patterns of type and layout are the same in each.
Why is it so important to recognize composite objects? Finding composites can be an important step in solving the problem of premature commitment to structure; they make certain kinds of decisions ("Is this a property of an object, or a separate thing?") less far-reaching. Surprisingly, all of our sample layouts had at least some examples of implicit composites.
Our examples show us two ways to perceive reference-based structures: signified recurrence, (e.g. highlighting the use of a visual reference in more than one place in a structure), and informal graphically noted connections (e.g. fences that corral groups of objects and lines to signify standard hypertextual reference). When working within a limited domain, a specialized graphical vocabulary, like that provided by XNetwork's palette of computer network devices, can support the construction of more shades of referential meaning [Ship93a]. An example is that text placed near a particular design unit or network connection may imply a reference that other designers will understand.
We also find that recurrence and graphically noted structures are useful adjuncts to spatialized text, since the informal arrangements are as volatile as they are expressive. Collaboration and self-annotation are difficult when so much of the structure is expressed implicitly, in the layout; just moving an object around changes the meaning of the reference, and the other objects that refer to it. Drawing on the space (and interpreting drawn notations) gives authors a way to extend and stabilize the meaning of volatile layouts.
First, the found structures can be used as the basis for supporting simple but repetitive information management tasks; they can help authors interact with ad hoc organization. For example, if objects are identified as implicit members of a taxonomic set, an operation on one may cause the system to ask the author if the change -- possibly reorganization or a change in internal structure -- should be propagated to the others.
Second, allowing structures to emerge gradually, then be recognized, reduces some of the representational problems introduced by node-link models. Most node-link models of hypertext introduce a variety of authoring requirements -- for example, text segmentation, deciding whether an object is a node or link, deciding whether a characteristic of an object should be part of the object itself (as a property or slot) or whether it warrants the creation of a separate object that is linked to the first, and other impediments to the task at hand.
Heuristic structure recognizers can solve many of these problems. Spatialized text allows the articulable content to be expressed as objects, and the more difficult to express connections between objects to be identified later, thus ameliorating the problem of premature structuring. It also allows properties that are expressed as separate objects to later be folded back into objects in the form of the recognized composites we describe in Section 4.2; the distinction between internal and external structure is usefully fused.
Third, structures that are perceptably regular or can be identified by simple heuristics can provide an important means for communicating about how a space is used; they can promote mutual intelligibility [Trig86]. Collaborators can use found structures as a basis for explaining implicit organization. For example, if the heuristic algorithms find clustered objects, it may be vital that the author of the cluster explain to her collaborators why the objects have been pushed together. Found structure can thus be the basis for interaction between humans, not just human-computer interaction.
Finally, if a more formal knowledge base is a desired end of the task, recognizing structures is an important method for helping people notice and express the regular structure of their domain and maintain its consistency.
As we pointed out earlier, while these structures are not explicit, they are extensional; they have meaning to authors and readers even though these meanings may be fraught with ambiguity. Thus, parses are only partial, and it's no surprise that they are sometimes not well aligned with human intent.
We experimented with recognition order, and found that different spatial layouts have different preferred parsing orders. Thus, interaction should guide how these heuristic algorithms are applied -- in what order and when. In general, the parses were fairly successful when aggregates based on overlap were found first, sets based on homogeneity and alignment (possibly vertical or horizontal) were located next, and composites were located after that. Identification of heterogeneous structures (piles of dissimilar objects and composites) generated a new type; structures formed from homogeneous objects assumed the type of their constituents. By repeated application of the recognition algorithms using previously inferred structure, higher levels of structure are perceived.
Figure 6. Parsing a structure in spatialized text.
Figure 6 simulates a parse that the recognition algorithms can perform; this example is a segment of a structure built in Aquanet. For the purposes of the figure, when a parsed structure inherits a type from its constituents, it is represented the same way; if a new type has been generated, an arbitrary new visual representation is shown.
First, the piles of similar overlapping objects are parsed into a single structure that has the same type as its constituents. Then the two composite substructures, one on the right, and one on the left, are parsed as new types. The two composite substructures as joined into one. Finally, the set of two complex composites is parsed, resulting in a single structure.
Using our examples of spatialized text as test cases, we found that this kind of parsing was fairly successful. The recognition algorithms were not tuned to any particular style of layout, since they ran independently of the hypertext systems that produced the spatialized text. Specific structures that were identified included aggregates, aligned taxonomic sets, and composites. The structure identified was often consistent with the authors' intent, although the higher the level of the perceived structure the greater the occurrence of incomplete recognition.
Figure 7. The heuristic algorithms parse a structure created in Aquanet.
Figure 7 shows a sample parse of an Aquanet discussion. Different structures found by the recognition algorithms are shown with different shading. The right-most column of objects is one of many ambiguous situations in this relatively simple spatial layout. In this case the layout could be interpreted as a list of items with a larger-than-normal gap or, as the recognition algorithms decided here, there could be two separate shorter lists. Neither alternative is necessarily the correct one; the spatial freedom allows the author to leave such determinations implicit, and left up to the interpretation of the reader.
From this automatic heuristic parsing experiment we can conclude that (1) such parsing is feasible; (2) interaction will be important in guiding this kind of recognition (especially since the structures can be both idiosyncratic and ambiguous); and (3) that the hypertext system can provide facilities for spatialization that will render later systematic interpretation much easier.
We distributed the objects in each layout randomly, such that the entire layout occupied the same planar extent as did the original; also, the type and individual extent of each object was preserved. In some of the new layouts, random placement of objects conformed to a grid (i.e. the objects appeared to be aligned); in others, objects were placed freely on the limited plane.
When the recognition algorithms were applied to these randomly generated layouts the number and complexity of the structures that were identified was significantly reduced compared to the original layouts. Most of the structures that were found involved a small number of objects, and were likely to be two member aggregates or sets. No spurious composites were identified, and no higher-level structures (structures composed of other structures) were parsed. Only one of the machine-generated spatializations -- the one whose human-created counterpart was least amenable to this type of parsing -- appeared roughly equivalent to its original counterpart. Thus, disorganized structures that are not readily perceived by human interpreters were not mistakenly perceived by the algorithms.
But spatialized text can also make us recall why the node-link model became normative: It enables us to refer from one textual entity to another; it supports comprehensible, navigable structuring of a complex space; and it prescribes and constrains interconnection where it is useful.
The ability to find and use implicit structures can support authors and readers in a system that extends the node-link model with spatial hypertext. As we noted in Section 5.1, this kind of found structure can be used as a basis for supporting information management, as a straightforward way of promoting knowledge-base evolution, as a way of solving representational problems endemic to many hypertext systems, or as a basis for collaboration or interaction.
We have also found that spatial hypertext is necessarily idiosyncratic and ambiguous; people use implicit structures of this sort to maintain the fluidity of how textual content is organized and interpreted. Thus any sort of heuristic recognition should be guided by human interaction.
Where should this work lead? Our heuristic algorithms are just a start; additional structure finders can be developed, including algorithms that can interpret graphical as well as spatial notations. Drawings on the space can thus have hypertextual meaning.
In situ investigations also need to be performed. Currently, this work analyzes pre-existing structure; integration with a spatial hypertext system would require more attention to interactive, incremental recognition of structure. It may not be sufficient to retrofit current hypertext systems with these capabilities; a more promising approach involves designing for spatial hypertext. More extensive use of spatial hypertext will allow us to observe how this work scales; our largest analyzed space so far contains 2000 nodes; it is interesting to contemplate how these techniques may be applied to a much larger information space.
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