Digital objects such as textual documents may have associated annotations that differ substantially in the kind of thing they are about. For instance, there may be notes on formatting (identifying bad line breaks or word spacing), notes on linguistic features (grammar, word lemmas, etc.) and notes on content (factual corrections, challenges to the reasoning, etc.). These differences have consequences for subsequent processing. In a production scenario that reformats a document the linguistic and content comments might be retained, but comments on formatting are probably no longer be relevant. If the text is translated into another language then neither the formatting nor linguistic comments will be carried forward -- but the content comments will still apply and might be included. Although the management of annotations in document processing and publishing routinely reflects this differential treatment of annotations, there is no general theoretical framework that would help us design systems that would support the general systematic management of annotation propagation.
The issues here are not merely practical problems, but go to the heart of the nature of annotation, which is itself a fundamental communicative activity in many spheres of human intellectual life. Annotation is one of the important "primitive" activities that scholars in many disciplines employ when they pursue a topic of interest [Unsworth 2000]. As more and more of the scholarly process has been enhanced by, and in some cases migrated to, digital formats the need to create tools that permit scholars to enjoy the same broad range of primitive activities that they do with physical formats has become evident. This is especially true of the annotation primitive.
Research on systems and strategies for annotation in the digital environment began with the early work on hypertext systems in the 1960s and has produced a considerable body of academic research, experimental systems, as well as some steps towards an analytical framework [Renear et al. 1999]. With the development of the Web the opportunity for practical annotation systems began to attract interest in Web-oriented standards and sytems. Beginning in the late 1990s and early 2000s efforts, both large and small, began to coalesce with the ultimate goal of extending the practice of annotating to digital documents. These efforts were continued by more generalized, community-oriented, collaborator initiatives such as the W3C Annotea Project and more recently the Open Annotation Collaboration (OAC), the Open Annotation Community Group (OACG), and the World Wide Web Consortium (W3C)’s Web Annotation Working Group (WAWG).
While these efforts have been primarily concerned with the production of a technical standard against which annotation tools and systems can be designed and implemented, relatively little discussion has been given to the technical standard’s theoretical underpinnings. In this paper we take up what we believe to be a particularly important theoretical challenge for any annotation standard or system. In short it is unclear exactly what is being annotated when we create a digital annotation.
We believe this is an important issue because it isn't clear what should happen to annotations when the documents they annotate change or go away. Do annotations have a broader target beyond the particular electronic document to which we anchored them? For instance if we annotate an article on the CNN website, should that annotation carry through to other copies of that article that appear on news aggregator websites? What is it that we are really annotating? When we anchor an annotation by selecting a string of characters that represents a sentence, are we annotating that string, the byte sequence that encodes it, the sentence that it represents, or the propositional content that the sentence communicates? If the only tools that we have to anchor the substance of an annotation to its target is a selector that chooses a particular string of text based on the strings immediately preceding and succeeding it, how do we know whether or not the target of that annotation is the string that it is anchored to.
The W3C Web Annotation Data Model has two relationships, motivation and purpose, that provide some help and orientation in addressing these issues. However those relationships are not sufficient for supporting the functionality desired. This is not surprising as target identification is a very difficult problem.
We are proposing to explore a possible third relationship, targetfocus which by providing additional semantic information will bring the W3C Web Annotation Data Model closer to supporting the reliable identification of annotation targets.
In what follows we first describe the problem in more detail, providing additional terminology that may be helpful to developing potential solutions. We than illustrate the difficulties through several worked examples that employ the latest version of the WAWG’s Web Annotation technical standard which provides a mechanism for anchoring annotations of a particular representation. It does this through the use of a predicate "hasTarget", which can be refined through a class called "Selector". We note that the various kinds of selectors (e.g., "text quote selector", "fragment selector", etc.) suggest that the focus of the annotation is different than the anchor. Finally we suggest how an additional predicate, "hasTargetFocus," might be used to extend the existing technical standard in ways that can better facilitate linking user intentions regarding the intellectual focus of their annotations and the technological limitations of anchoring on digital objects.
A major challenge in designing standards and systems for annotation is identifying the "target" of the annotation, that is, what the annotation is about in the sense of: "what is it annotating?." The target of an annotation can be distinguished from the annotation's anchor. The anchor is, in the typical case of digital documents, a data structure feature that locates the annotation in some part of the digital object. Although this location does provide an indication of what the annotation is about it does not do so explicitly and univocally, and in particular, it does not make this information available for automated processing.
For example, several annotations anchored to a particular character sequence in a document could in fact be about quite different things. An annotation anchored in this way might be about propositional content: this is false; about a linguistic entity: this is in the passive voice; or about a layout feature: this is ugly. In the first case the annotation might be, more specifically, about a particular claim, in the second case it might be about a sentence, and in the third case it might be about, say, letter spacing.
If an annotation's target cannot be reliably identified the functionality of annotation standards and systems will be suboptimal. Obviously we will miss opportunities for information extraction and retrieval, but management, preservation, and interoperability will be diminished as well. For instance, reformatting should typically retain annotations such as "this is false" and "this is passive voice", but not "this is a bad break"; while translation should typically retain "this is false" but neither "this is passive voice" nor "this is a bad break".
Text and entities that contain or bear text frequently operate at multiple levels of abstraction. Similarly annotations of text may actually be annotating one of these other abstract entities. An annotation that anchors on a formatted string of text may actually be annotating any of the following (or other abstract entities beyond those illustrated here):
the font of the symbols that comprise that formatted string
the syntax of the sentence represented by that formatted string
the propositional content expressed by the sentence represented by that formatted string
When annotations are intended only for direct human engagement, disambiguating targets across ontological categories is often relatively easy for the reader. The ostensive demonstrative in our example ("this"), or its functional equivalent, will be charitably given a referent appropriate for the predication being made. We know for instance that sentences are the sort of things that are in the passive voice and so an assertion that something is in the passive voice is probably an assertion about a sentence—dynamic typing being one reason human communication systems are so efficient.
Of course some assistance will be found in statistical techniques for inducing the target of the annotation from various cues, but even if this approach can sometimes determine targets relatively accurately it is unlikely to be practical in all circumstances. As Barend Mons has noted: "we wouldn’t have to mine the data if we didn’t bury it in the first place" [Mons 2005]. In any case the problem of how to identify targets remains: the results of statistical induction will still need to be recorded.
Applying automated processing to realize the full potential of digital annotations will be assisted by the explicit identification of the objects being described or characterized—we need to know exactly what we are talking about in order to extract information, store information usefully, and further analyze or calculate for knowledge discovery and management.
W3C Annotation Model
The WAWG conceptualizes an annotation in terms a body and a target [Figure 1].
Figure 1: Basic Web Annotation Model
While this basic model provides a very simple data structure to implement it does so at the cost of semantic precision. It assumes that the annotator is just annotating the representation in front of them. It makes no remarks upon how or in what way the body relates to the target. It makes no remarks on the annotator’s intentions for the annotating the target in the first place. It is completely agnostic with regards to what kinds of things can annotate other kinds of things.
Designers of digital annotation standards and systems are aware of these problems and the WAWG has provided additional vocabulary to facilitate recording information regarding annotator intentions that goes some ways towards clarifying relationships and types. In particular annotator intention is partially preserved by the concepts of motivation and purpose. A motivation is the reason the annotation was created and, the purpose is the reason the body was associated with the target.
In some respects motivations are intended to provide conceptual clues regarding additional class identities for an annotation. For example, in addition to being an annotation, the annotation may also be a comment, or an edit, etc. Similarly a body’s purpose provides some contextual clues regarding the class identity of the relationship between the target and the body. The relationship might be that of correcting, explaining, etc., but unlike the motivation, the purpose provides some distinct information regarding the role that the body plays within the annotation, which suggests some potential limitations for the class identities of either the body or the target.
Even though the annotation provides some information regarding the relationship between the target and the body its real purpose in the digital ecosystem is to establish a link between the stuff that the body is composed of and the stuff the target is composed of. We call this linking process anchoring. The important factor is that anchoring always happens at the most concrete level available (e.g., if I am writing in the margin of a book then the part of the paper my pen scratches its ink into becomes the anchor).
The anchor and the target have subtle differences that are exacerbated by the agnosticism regarding body and target class identities. By remaining agnostic about them and leaving the door wide open with regards to what may be annotated and by what it may be annotated the annotation begins to fall prey to a common ambiguity problem with regards to digital resources in general and web resources in particular—the manifestation problem.
Expressing Annotator Intentions
Determining the semantic nature of the level of abstraction of an annotation’s focus could be relived if there was a mechanism for recording the annotator’s intentions. The WAWG’s Web Annotation technical standard tries to do this through motivation and purpose. However, some limitations immediately become apparent.
One limitation of this approach is that the motivations themselves can operate at different levels of abstraction. Unfortunately, assuming that the motivation is meant to express the focus of the annotation overloads the semantics of motivation as a concept. The annotation may have a motivation of "editing" but the annotator’s intention was "suggesting." In the case of the WAWG, it was suggested that each motivation not only supply some sense of additional class identity for annotations but also be used as the basis for some expected user agent behavior. [Schepers 2015] This was a subject that had been discussed several times by the working group and the general consensus was that motivations are much too coarsely defined for such purposes.
To make matters worse, it is possible for motivations to actually obscure an annotator’s intentions. For example, an editor may use an editing tool to suggest an alternative sentence to one appearing in an editorial workflow but the purpose of the tool itself is to produce edits. Constraints on the motivation may be placed by the tool. In the example it may be the case that developers have provided the editor with some means of providing motivation information or, it may be the case that the tool’s motivating purpose is seen to be the overarching intention of its users by the developers and so all annotations produced by the tool have the motivation of "editing" but no other information. In this last case the annotator’s true motivation has been lost, subsumed by the annotating software.
Providing information regarding the purpose that a body fulfills within the annotation may help to alleviate this problem. The annotation may have a motivation of "editing" while the body’s purpose is "suggesting." This is helpful because additional information can be provided. It is also helpful in cases where an annotation has multiple bodies. For instance, imagine another editing scenario where we are providing both the correction to be made (e.g., a word has been misspelled and the corrected spelling is provided) and the reason we provided (e.g., a second body appears with the purpose of "explaining" that communicates the explanation "misspelled").
Unfortunately this approach is also vulnerable to the kind of silent assumptions that developers build into tools. Annotators can only provide the information if the developer has provided a way for them to and the developers may make assumptions as to the purposes of bodies in the annotations produced by the tool. In the specific case of the Web Annotation standard, there is an additional problem—purpose is strongly tied to bodies whose class identity is text. In this case, only strings are allowed to have purposes. This is problematic if we intend to annotate a target with an image or other non-text media-type.
Expressing Annotator Focus
As with other hypermedia applications, annotation target anchors are linked regions of whatever resource is receiving the annotator's attention [DeRose & Durand 1994]. But our usual methods of highlighting a region (pointing with a sprite, sweeping with a cursor, drawing a rectangle, etc.) provide no distinctions among the levels of abstraction to which our attention is actually drawn. A highlighted region of text, for example, could be understood as anchoring a span of characters, a sentence, an assertion, the proposition asserted, or some entity in the world to which the text alludes. The annotator's attention may drift across these levels without conscious effort, resulting in (for example) a claim that the targeted text is both misspelled and misattributed. This would be an example of the same ontological variation in reference that complicates other kinds of markup interpretation [Renear et al. 2002]. Identification of the actual annotation target has implications for how the OA markup is processed (as described above), so our goal is to simplify that identification.
In an earlier paper we suggested that an annotation framework structured as an enabling architecture might offer domain and codomain constraints on the annotating relation [Dubin et al. 2013], and one could imagine a "misspelled" relationship that would apply only to words or a special "broken link" annotation, each of which would contribute its own codomain constraints to specializing the annotation superproperty. But not only would this approach require a large number of very specific annotation subproperties, it's likely that most of them would require broad enough ranges to admit ambiguous targets. Both physical artifacts and works of authorship, for example, could be "forged" or "lost to history."
The OA "motivation" and "purpose" qualifiers are much too general to provide any reliable disambiguation. We illustrate these difficulties using a figure from Pietro Liuzzo's presentation at the 2015 Symposium on Cultural Heritage Markup. Liuzzo's Figure 1 (encoded in the Balisage proceedings as a JPEG) presents a photograph of a drawing in a manuscript [Liuzzo 2015]. The drawing depicts a bronze seal, upon which is a latin inscription that alludes to two Romans who lived during the late third and early fourth centuries. Our Figure 2 shows how an OA annotator might understand the text of the inscription to be an allusion to or comment on Stefanilla Aemiliana, while in Figure 3, the paragraph on Stefanilla Aemiliana might be understood to identify one of the people mentioned in the inscription. Of course the only difference between the two examples is which web resources is tagged as the annotation body and which the annotation target. General motivations like "Commenting" or "tagging" give us no clue to either the relationship obtaining between these resources or what persons, texts, objects, or images are participating in that relationship.
Figure 2: Annotation Example 1
ex:anno1 a oa:Annotation ; oa:hasBody liuzzo15:d40179e141 ; oa:hasTarget abitofhistoryS:stefanillaaemiliana ; oa:hasMotivation oa:Commenting .
Figure 3: Annotation Example 2
ex:anno2 a oa:Annotation ; oa:hasBody abitofhistoryS:stefanillaaemiliana ; oa:hasTarget liuzzo15:d40179e141 ; oa:hasMotivation oa:Tagging .
Figure 3 makes an image the annotation target, but which image is being tagged? Is it the digital photograph of the drawing? Is it the drawing itself? Is it the original object that bore the inscription? Is it all of them? Or, is the annotation not about the images at all but about the text contained within it? A motivation like “tagging” is simply too coarse for us to tell.
One can find a variety of different resources on the web that concern the seal, the inscription, and the family to which it refers. Any of those might be a candidate annotation body for an image annotation on Liuzzo's figure. Alternatively, someone might offer a provenance connection to the manuscript, or even metadata on the JPEG (such as a color profile). In our Figure 4, a epigraphic database record about the inscription is understood to describe Liuzzo's figure 1:
Figure 4: Annotation Example 3
ex:anno3 a oa:Annotation ; oa:hasBody eagle:HD032681 ; oa:hasTarget liuzzo15:d40179e141 ; oa:hasMotivation oa:Describing .
Unfortunately we still have no idea if the annotation is using the description to say something about the image, the thing in the image, or the text that the thing in the image contains. If we also had a purpose associated with the annotation’s body we might be able to rule out some of the possibilities and narrow our choices but, in this instance, since from the standard’s point of view purpose is a property only simple textual bodies can have, the Web Annotation model works against us.
It seems clear to us that the target construction conflates the intellectual focus of the annotation (i.e., what's actually annotated) with the anchor for the annotation’s body (a region within an image, string, etc.). Class identities for the targeted resources offer a partial solution to this problem, as shown in Figure 5:
Figure 5: Annotation Target Classes
liuzzo15:d40179e141 a dctype:Image . abitofhistoryS:stefanillaaemiliana a dctype:Text . eagle:HD032681 a dctype:Dataset .
Although these class identities tell us what kind of web resource has the annotator's attention, they don't give us the level of representation to which that attention is directed. The examples below make use of an proposed extension property, "hasTagetFocus", for identifying that level in situations where the target class or anchoring representation are unclear.
In Figures 6, 7, and 8 below, we've add this property to the prior examples. The target foci are a person (Stefanilla Aemiliana), a string (the inscription text), and the inscription itself, respectively. We've employed the CIDOC Conceptual Reference Model to illustrate how the proposed property links the annotation to one possible domain-specific interpretation of the target foci.
Figure 6: Annotation Example 1 with Target Focus
ex:anno1 a oa:Annotation ; oa:hasBody liuzzo15:d40179e141 ; oa:hasTarget abitofhistoryS:stefanillaaemiliana ; oa:hasMotivation oa:Commenting ; ex:hasTargetFocus ex:person1 . ex:person1 a crm:E21_Person .
Figure 7: Annotation Example 2 with Target Focus
ex:anno2 a oa:Annotation ; oa:hasBody abitofhistoryS:stefanillaaemiliana ; oa:hasTarget liuzzo15:d40179e141 ; oa:hasMotivation oa:Tagging ; ex:hasTargetFocus ex:text1 . ex:text1 a crm:E62_String .
Figure 8: Annotation Example 3 with Target Focus
ex:anno3 a oa:Annotation ; oa:hasBody eagle:HD032681 ; oa:hasTarget liuzzo15:d40179e141 ; oa:hasMotivation oa:Describing ; ex:hasTargetFocus ex:inscrip1 . ex:inscrip1 a crm:E34_Inscription .
The proposed predicate allows distinctions to be drawn between the web resource to which an annotation is attached (typically text, an image, or other digital data) and the resource that participates in the relationship represented by the annotation. Our proposed property doesn't resolve all ambiguities: for example, it's not obvious in Figure 6 which particular person is denoted by ex:person1. But suppose that placeholder ID were generated by an annotation tool after the annotator has chosen crm:E21_Person from a list of CIDOC/CRM classes. That class identity could inform processing and presentation details for ex:anno1, and individual identity relations could later be asserted from ex:person1 to other annotation foci or to the subject of a more detailed description of that historical person.
The proposed extension—adding a new predicate that expresses target focus—provides additional information that can be exploited by annotation clients and servers. While this does not eliminate all uncertainty, it clearly provides clearer semantic information than motivation and purpose by themselves and serves as at least a first step towards resolving this issue. One potential use is to suggest circumstances under which an annotation’s body might appropriately be anchored to other instances of the target’s content. For example, an annotation that annotates the text contained within the image used in our examples should rightfully annotate all depictions of that text.
Another potential use is clarify the ontological commitments being made by the annotators themselves. If their intention is to annotate abstract entities like sentences, persons, etc., then the annotation client’s data vocabulary needs to be rich enough to differentiate between abstract entities and the concrete entities that depict them. It might not necessarily need to realize the distinction between a proposition and the sentence that denotes it, unless our hypothetical annotators are operating at that level, but it does need to realize that there is a distinction between sentences and the strings that depict them. Only by providing means to adequately record an annotator’s ontological commitments can an annotation client begin to capture and record some sense of annotator intentions.
There is no reason why target focus need be a monolithic solution. Like motivation and purpose, target focus should be represented using an extensible framework that fully engages the annotators in the creation and transmission of their intentions. In the examples illustrated in Figures 6-8, the CIDOC-CRM ontology was used to assign class identities to the foci of the annotations. This is just one possible class assignment for those foci. Other domain ontologies could have been used, (e.g., foaf:Person instead of crm:E21_Person). By reusing domain-specific ontologies (or even allowing annotators to select the most appropriate domain-specific ontology for themselves) the annotations can be more fully embedded with the intellectual content they are meant to discuss, edit, tag, etc.
Our next step will be to begin exploring a possible extension to the Web Annotation standard within a specific domain. This will allow us to ascertain the viability, scalability, and utility of the proposed extension property while working directly with domain users. One of the benefits that we anticipate is the ability to realize the difference between tagging a web resource with a concept and assigning a class descriptor from a class hierarchy to it, which under the current regime would be conflated by the "tagging" motivation.
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 Image source: https://www.w3.org/TR/annotation-model/#introduction