=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards New Scholarly Communication: A Case Study of the 4A Framework
|pdfUrl=https://ceur-ws.org/Vol-721/paper-07.pdf
|volume=Vol-721
|dblpUrl=https://dblp.org/rec/conf/esws/SmrzD11
}}
==Towards New Scholarly Communication: A Case Study of the 4A Framework==
https://ceur-ws.org/Vol-721/paper-07.pdf
Towards New Scholarly Communication:
A Case Study of the 4A Framework
Pavel Smrz and Jaroslav Dytrych
Brno University of Technology
Faculty of Information Technology
Bozetechova 2, 612 66 Brno, Czech Republic
E-mail: {smrz,idytrych}@fit.vutbr.cz
Abstract. This paper discusses the use of semantic web technology to
realize the vision of future scientific publishing and scholarly communica-
tion. It introduces a novel knowledge system that builds on the popularity
of social tagging and collaboration systems as well as on the idea of “An-
notations Anywhere, Annotations Anytime” (hence 4A). Key technical
characteristics of the realized components are presented. User experi-
ence observations and the results of a preliminary experiment are also
reported.
1 Introduction
Recent years brought various proposals for changes in the model of scientific
publishing and scholar communication. Most radical ones call for a complete
shift from pre-publication peer review and impact factor measures towards social
popularity of papers [13, 1]. Moderate views, e. g., [15, 16], consider how the
success of community-specific services (arXiv1 , ACP2 ) could be replicated in
other fields.
The 4A framework introduced in this paper adds a piece to the mosaic of
tools that enable such changes. It can be seen as a modularization and unit-
testing system in the parallel between scientific knowledge artifacts and software
artifacts [6]. Pieces of knowledge on a very low granularity level (e. g., particular
ideas) being annotated play the role of modules or units in this parallel. Testing
corresponds to validation of annotations in collaborative knowledge building and
to community appreciation of automatically generated summaries, inference-
based re-uses, and other applications of the semantically enhanced resources.
Transformations in scholar communication are often motivated by benefits
they could bring to a research community. On the other hand, business models
applicable in the new publishing era need serious consideration [21]. Publishers
obviously try to find their position in the evolving environment. It can be demon-
strated by their effort to engage authors and get their feedback or to support
sharing of scientific citations among researchers in the form of reference manager
1
http://arxiv.org/
2
http://www.atmospheric-chemistry-and-physics.net/
services (e. g., Connotea3 or CiteULike4 ). The 4A framework is also intended to
encourage the publishers to see opportunities in making money by adding value
to open access data, rather than restricting the access to outcomes of research [9].
Publishers could build on their experience in organizing bodies of experts (e. g.,
editorial boards and review teams) able to identify the quality of the content.
By means of advanced knowledge processing tools, they could extend these com-
petences to trace expertise in specific domains, to aid the knowledge structuring
process, its sharing and reuse, to support the community work around research
topics, and to measure the impact of individual as well as group activities.
The framework emphasizes the role of annotation in the process of knowledge
creation. It is crucial to realize that tags can be associated not only with a
scientific publication, but also with any other material relevant for the research.
If the experimental data referred to in a paper is made available (as required in
some disciplines today) and also annotated, it is easier to verify its interpretation
presented in the text. The annotation of data that is employed in experiments by
other researchers also simplifies tracing its reuse and thus provides an alternative
to the current state characterized by reusing and not appraising [7].
A special attention has been also paid to the means of tracking down particu-
lar ideas described in papers to the source code of computer programs described
in the text. One can employ the concept of program documentation generation
(for example, by means of the popular Doxygen tool5 ) and interconnect par-
ticular pieces of code with the description of their functionality on the higher
level.
Last but not least, the concept of annotation is general enough to cover not
only the textual material related to the presentation of a particular research
result (slides from talks, blogs, tweets or other formats of messages referring
to the results) but also related audio- or video recordings. For example, the
proof-of-concept case study involved a material from the Speaker and Language
Recognition Workshop Oddysey 20106 and it employed the search-in-speech ser-
vice Superlectures7 . Many ideas from referring texts are not easy to link to the
material available in textual form so that working with a multimedia content is
often necessary.
To summarize the directions of motivation mentioned in this section, the
new scientific publishing and scholarly communication model should be accom-
panied by an annotation framework supporting the whole life-cycle of scientific
papers – from ideas, hypotheses, identification of related research, and data col-
lection, through setting and running experiments, implementing solutions and
interpreting results, to submitting, reviewing, reflecting reviews, preparing final
versions, publishing complementary material, getting feedback, discussing con-
tent and reflecting previous results in a new work. Annotations on all levels pave
3
http://www.connotea.org/
4
http://www.citeulike.org/
5
http://www.stack.nl/~dimitri/doxygen/
6
http://www.speakerodyssey.com/
7
http://www.superlectures.com/odyssey/
the way for shared knowledge understanding. The social dimension of ubiquitous
annotations of knowledge artifacts can also bring immediate benefits to research
communities in terms of better models of fine-grained impact characteristics.
The elaborated annotation system helps to pinpoint an expertise in particular
fields. Researchers can also benefit from instant gratification – the annotation is
immediately available for others, it can be shared and re-used in other contexts.
This paper discusses the use of semantic technologies to realize the above-
mentioned vision. The name of the annotation framework – 4A – refers to the
concept of “annotation anywhere, annotation anytime”. Key features of the re-
alized solution are introduced in Section 2. A proof-of-concept implementation
of the 4A annotation client and its application in experiments are presented in
Section 3. The paper concludes by discussing the directions of future work.
2 The 4A framework
Semantic search and other advanced functionality of the future web offer clear
benefits for the semantic annotation of knowledge artifacts. On the other hand,
the semantic enrichment of resources presents a tedious work for users. It is
therefore crucial to lower the barrier to annotate by means of immersing the
annotation into everyday work of users.
We address this requirement by defining a general interaction schema and
protocols that can be supported in various contexts. An ultimate goal of the
4A framework is therefore to let users annotate naturally in any application
used. The current implementation focuses mainly on the textual resources and
implements the functionality of server components as well as several clients.
From the annotation support perspective, it is crucial to distinguish two
types of environments users work in viewer- and editor applications. As viewers
do not modify the source content, changes in annotations need to be transferred
only. On the other hand, it is tricky to synchronize editing annotation sessions
as the changes in text may invalidate annotations.
To serve both types of clients, a general annotation exchange protocol has
been defined. The 4A synchronization server implements the protocol and en-
ables coordinated work of 4A clients. An annotation extension to general Javascript-
based editors has been developed as the first 4A client. A PDF reader add-on
and a Firefox browser extension are being implemented.
The 4A framework goes beyond the current practice of simple keyword tag-
ging and knowledge structuring curated in advance. It introduces an intuitive
knowledge structuring schema of structured tags [5]. In the experiments, it has
been successfully used for describing necessary conditions, conflicting views, and
comparison patterns. The 4A clients present structured tags in the form of a
relation attribute tree.
Unfortunately, none of the annotation formats applied in existing tools is
general enough to suit our purposes without modifications. Even Annotea [10]
– a format resulting from a W3C initiative to standardize annotations – cannot
express relations among structured annotations. That is why we could not sim-
ply reuse an existing format. The 4A format extends Annotea by introducing
structured annotations with attributes of various types, embedded annotations
and interrelations among annotations. It is based on RDF where the subject
is always the annotation. It includes: annotation ID (URI), type, time of cre-
ation, its author, URI of an annotated document (or its server copy), XPath
to an annotated textual fragment, its offset, length and textual content, anno-
tation content and a specification of annotation attributes. The RDF Schema
corresponding to the RDF model is available at http://nlp.fit.vutbr.cz/
annotations/rdfs/annotation-ns.rdf.
The position of an annotated fragment is given by the path in the document
object model (DOM), the offset and the size. The representation is robust to
changes in general formatting. It is usually not necessary to process the whole
document. For example, a web page boilerplate and other parts that are not in
a DOM node on the path to an annotated fragment will be ignored.
The annotation types form a hierarchical structure. They include common
comments (a note, a description) and basic types of entities (a thing, a person,
etc.). Users can add new types and create complex type hierarchies.
The URI of an annotated document identifies a copy of the document that
is stored on the server. The annotation process starts with a synchronization
step in which the client sends the document URI and its content to the server.
The server returns the URI of the local copy of the document which will be
used in annotations. This procedure enables annotating documents that the
server could not access directly. Processing the original document on the server
side also enables removing irrelevant attributes (e. g., session ID) and applying
changes to the correct version of a document as the stored version is updated
together with all annotations at every access.
A new annotation interchange protocol has been defined for the communica-
tion between 4A clients and the server(s). In addition to actual annotations, it
can be used for simple authentication, synchronization of annotated documents,
subscription to annotations from defined sources, annotation suggestions, inter-
change of knowledge structures (annotation and attribute types) and various
annotation-related settings.
The protocol enables two-way asynchronous communication between clients
and servers. If a user adds an annotation, the server sends it immediately to
all other users that annotate the same document and are subscribed to a given
channel (defined by an author, a group or an annotation type). Changes of an-
notation types, of the document content and of relevant settings are distributed
immediately as well.
Messages are defined in XML. They can be therefore easily sent over various
protocols on a lower level and parsed on the client side. It is also possible to
combine the messages into one XML and make the communication even more
efficient.
Session management messages include the protocol version negotiation, log-
ins and log-outs. Subscription management enables specifying what types of
annotations from what sources should be sent to a particular client. Annotations
can come from another user or a URI representing an automatic annotation
server, user group or other general source (e. g., an external service).
The server gets a copy of the current version of an annotated document by
means of a document synchronization process. If it is the first time the document
is sent, the server just stores it. If there is already a copy of the document, the
server compares the new version with the stored one and updates the stored
version together with all annotations. If the new version impacts no previous
annotation, the operation is confirmed instantly. If there are annotations that
could be invalid, the server informs users who can consequently correct possible
errors.
To support clients that are not able to work with structured texts, the server
can linearize the text from documents. The client transforms the document to
plain text and sends it to the server. If there is a structured form of the document
at the server, it is linearized and compared to the received version. If they are
the same, it is possible to start the annotation process. The server will then
adapt all incoming annotations for the structured version of the document. The
linearization also enables cooperating with clients working with other structured
formats than the server. The client will adapt positions to the linearized text,
the server will adapt it to its structured form. This way it is possible to annotate
the same text synchronously e. g. in HTML and in PDF.
3 Proof-of-Concept Experiments
As a proof of concept, we employed the current version of the JavaScript an-
notation editor in experiments . The tool is a universal component which can
be easily integrated into various JavaScript-based editors such as TinyMCE8 .
It implements the functionality of a 4A client – it enables editing complex an-
notations, synchronizes tagging with the server side and presents annotation
suggestions provided by information extraction components.
The client makes the annotation process manageable. The type of annotation
can be specified in a text field. Instant search in type names is supported – the
input serves as an intelligent filter. It is also possible to browse the hierarchical
structure of types. This reduces diversity of annotation types.
It is possible to add new attributes to identified relations. A name, a type
and a value are associated with each attribute. The selection of an attribute
type is similar to that of the annotation type. The way information is presented
also corresponds to the types. An attribute can be of a simple data type, of an
extended type (e. g., a geographic location), or of an annotation type. Simple
and extended data types are added as new branches of the type tree. If an
annotation type is utilized, it is possible to choose one of the existing annotations
(an annotation reference) or to create directly a nested annotation.
It is possible to select more textual fragments in a document. Annotating
more fragments simultaneously enables identifying all occurrences of a relation
8
http://tinymce.moxiecode.com/
in a document. When storing it, a separate annotation is generated for each
fragment. The same procedure can be applied for attributes (nested annotations)
with the same name, type and content which are then displayed as a list. Clients
employ suggestions to facilitate the tagging process (see Figure 1 for an example).
Fig. 1. Suggesting relation attributes based on the previous context
One senior researcher (the first author of this paper) and six PhD students
(including the second author) participated in the annotation experiments. Source
texts included not only scientific papers, but also blog messages and tweets re-
lated to specific research topics and the material referred from these sources
(e. g., a particular part of a dataset, a PowerPoint presentation, a source-code
file). The papers dealt mainly with natural language processing, information
extraction and machine learning (correspondingly to the expertise of the partic-
ipants). However, there were also general topics discussed (especially in the blog
messages).
The experiments aimed at identifying particular pieces of text (selecting tex-
tual fragments) that correspond to specific types of content patterns (e. g., state-
ments showing equivalence of two approaches or suggesting a resource for a spe-
cific group of readers). A part of the task also involved tagging the fragments
supplying evidence that a particular method is really applied for a particular
task (not just referred to as an alternative the paper does not really deal with).
For citations, the task was to find the part of a referred text illustrating the
referring context.
The primary objective of the analysis was to learn what form of tags (simple
or structured ones) users prefer in given situations and how tag suggestions help
to annotate the content (even if they are far from being perfect). Annotation
suggestions based on automatic information extraction were switched off to not
influence the answer to the first part of the question. This setting simulates
specialized tagging where the set of available annotated examples is too limited
to be used for learning-based methods or too complex to be described by a
handful set of rules. The participants have been instructed that the annotation
should be usable for future knowledge acquisition from annotated resources.
Suggestions were turned on for the second part. Figure 2 shows a typical setting
of the environment.
Fig. 2. Structure tag suggestions
Results of the first part (annotation form preference) are conclusive. All
annotators resort to structured tags in the task of method comparison. On the
other hand, they prefer flat tags over structured ones when dealing with simple
annotations of individual methods (“conjugate gradient” is a “ML method”). As
a side effect of the advanced functionality of the tool, it was also observed that
the annotation form will probably stay unchanged if a user defines the structure
of a tag for a specific task and others are able just to follow and re-use the
definition.
The second part of the question turned to be hard to answer. Automatic
annotation suggestions accelerate the tagging process but they also distract an-
notator’s attention. The level of acceptance is subjective and significantly varies
with respect to the precision and recall of the information extraction process gen-
erating suggestions. It is also questionable what the granularity of structured-tag
suggestions should be, i. e., what information (that does not need to be correct)
should be combined and potentially confirmed by one click. For example, one
of the annotators pointed out that “momentum” should not be referred to as
“a method” in the context shown in Figure 2 so that he could not accept the
suggestion as a whole.
4 Related Work
A lot of previous work has been done in fields related to the presented research.
The general topic of future research, scientific publishing and scholarly com-
munication is discussed in various contexts – linking scholarly literature with
research data [3], advocating open access [8], demonstrating a role of social me-
dia in scholarly communication [4], etc.
First tools supporting general annotations on the web date back to mid-
nineties [11, 18]. Among current solutions, Annozilla9 is conceptually the closest
to 4A browser add-ons – it is realized as an extension to the Firefox browser,
tags are stored on local or remote servers, XPointer is used to identify the an-
notated part of a document. The annotation protocol developed in the Annotea
project [10] is employed. In contrast to the 4A framework, Annozilla is intended
for simple tagging only. A pre-defined set of annotation types is limited to gen-
eral categories such as a comment, a question, an agreement/a disagreement. It
is therefore not possible to use the tool for advanced knowledge structuring.
PREP Editor [14] and Bundle Editor [24] can illustrate innovative ideas in
the area of interweaved text authoring and tagging. The former represents one
of the first real-time collaborative text writing tools. Implemented annotations
are limited to this functionality. The latter enables annotation structuring by
means of grouping them into bundles. Annotations can be filtered, sorted etc.
Zheng [23] states that the structured annotations proved to be more efficient
and user-friendly than the simple ones.
Popular web-based editors and other office applications such as Google Docs10
or Microsoft Office Live11 show current trends in the development of collabora-
tive tools. Even though there is either no or very limited annotation function-
ality, they need to be considered as “opinion makers” in terms of user interface
simplicity.
Google Docs is also noteworthy as being a successor to Google Wave12 – an
envisioned distributed platform of Email 2.0 where clients should be connected
to their particular servers and the servers should communicate by means of
the Google Wave Protocol13 . Compatible solutions, such as Novell Vibe14 , only
started to appear when Google turned away from a strong support of the plat-
form. Nevertheless, the vision of a distributed platform transferring user actions
with a very low granularity stimulated the development of the 4A framework.
9
http://annozilla.mozdev.org/
10
https://docs.google.com
11
http://www.officelive.com/
12
https://wave.google.com
13
http://www.waveprotocol.org/
14
https://vibe.novell.com/
The area of social tagging is also relevant for general topics discussed in this
paper. Various tools exist for the specific subdomain of collaborative scientific
citation managers such as CiteULike, Connotea, Bibsonomy15 , or Mendeley16 .
The systems offer advanced support for the particular task (automatic extraction
of relevant metadata, export to various formats, etc.) but they are usually not
able to link references to particular pieces of text and do not explicitly deal with
knowledge structuring.
On the other side of the knowledge processing support scale, there is a fam-
ily of ontology, topic maps and other knowledge representation format editors.
Protégé17 defines a kind of standard, other tools such as Neon Toolkit18 , On-
tolingua19 , or TopBraid Composer20 stress the collaborative-, user support-, or
integration aspects, respectively. Anchoring created knowledge structures in real
data is often limited to examples or glosses that can be stored together with
concepts and relations. In this context, resources resulting from semantic anno-
tation efforts in computation linguistics, such as FrameNet21 or OntoNotes22 , as
well as enriching folksonomies by formal knowledge sources [2] are relevant for
our research.
The presented work also extends the concept of semantic wikis. Several sys-
tems have been created around the idea of semantic web technologies enhancing
the wiki way of content creation [12]. The 4A framework directly draws on our
experience from the development of the KiWi system [17]. For example, the
knowledge emergence approach based on structured tags gains from [5] resulting
from the KiWi project.
Last but not least, one of the key components of the 4A framework – the infor-
mation extraction module – relates to previous research on automatic knowledge
extraction. In particular, we take advantage of the KiWi extraction elements [20,
19] that employ general purpose solutions such as Gate23 or GeoNames24 . In
spite of the fact that information extraction does not form a main topic of this
paper, relevant solutions for semi-automatic learning of ontologies from text such
as TextToOnto25 and OntoGen26 or ontology-based information extraction [22]
need to be mentioned as a source of inspiration.
15
http://www.bibsonomy.org/
16
http://www.mendeley.com/
17
http://protege.stanford.edu/
18
http://neon-toolkit.org
19
http://www.ksl.stanford.edu/software/ontolingua/
20
http://www.topquadrant.com/products/TB_Composer.html
21
http://framenet.icsi.berkeley.edu/
22
http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2008T04
23
http://gate.ac.uk/
24
http://www.geonames.org/
25
http://texttoonto.sourceforge.net/
26
http://ontogen.ijs.si/
5 Conclusions and Future Directions
The 4A framework presented in this paper incorporates annotation into knowl-
edge acquisition and knowledge sharing processes. The proof-of-concept imple-
mentation and the use of an annotation client in tagging experiments proved
validity of the idea but also revealed imperfections of the model and flaws in the
user interface of the realized tools. The future work will focus on removing these
deficiencies. The concept of knowledge emergence and knowledge structuring
needs to be refined. We will flesh out the support for social knowledge-creation
processes. New information extraction modules will employ advanced machine
learning methods to provide better suggestions in situations when only a few
training examples are available.
The number of 4A clients will also grow. The development of the JavaScript
annotation component and its integration into various web-based editors will
continue. The Firefox browser extension and the PDF reader add-on will be
finished. We will initiate the work on other extensions for LibreOffice and the
Semantic Desktop. The annotation format will be proposed as an extension of
the official W3C Annotea system.
New experiments will explore advanced tagging collaboration patterns. The
question on the acceptable error-rate for the automatic suggestions will be also
tackled. A higher number of participants and a more advanced setting of ex-
periments are necessary to prove the efficiency of the knowledge structuring
processes in the 4A framework. Last but not least, consistency and adequacy
of the knowledge representation resulting from the use of the 4A tools will be
studied.
Acknowledgements
The research leading to these results has received funding from the European
Community’s 7th Framework Programme FP7/2007-2013 under grant agreement
number 270001 – Decipher.
References
1. Adler, B., de Alfaro, L., and Pye, I. Redesigning scientific reputation.
The Scientist 24, 9 (Sept. 2010), 30. Online: http://www.the-scientist.com/
article/display/57645/.
2. Angeletou, S., Sabou, M., and Motta, E. Improving folksonomies using for-
mal knowledge: A case study on search. In ASWC (2009), pp. 276–290.
3. Attwood, T. K., Kell, D. B., McDermott, P., Marsh, J., Pettifer, S. R.,
and Thorne, D. Calling international rescue: knowledge lost in literature and
data landslide! Biochemical Journal 424, 3 (2009), 317–333.
4. Bilder, G. Social media and scholarly communication. In ISMTE European
Conference, International Society of Managing and Technical Editors (2010).
5. Bry, F., and Kotowski, J. A social vision of knowledge representation and
reasoning. In SOFSEM 2010: Theory and Practice of Computer Science, J. van
Leeuwen, A. Muscholl, D. Peleg, J. Pokorny, and B. Rumpe, Eds., vol. 5901 of
Lecture Notes in Computer Science. Springer, 2010, pp. 235–246.
6. Casati, F., Giunchiglia, F., and Marchese, M. Liquid publications: Scien-
tific publications meet the web, 2007. Online: https://dev.liquidpub.org/svn/
liquidpub/papers/deliverables/LiquidPub%20paper-latest.pdf.
7. Enriquez, V., Judson, S. W., Weber, N. M., Allard, S., Cook, R. B.,
Piwowar, H. A., Sandusky, R. J., Vision, T. J., and Wilson, B. Data citation
in the wild, 2010. DOI: 10.1038/npre.2010.5452.1, Online: http://precedings.
nature.com/documents/5452/version/1.
8. Furnival, A. C. Open access to scholarly communications: advantages, policy
and advocacy, 2011. Online: http://eprints.nottingham.ac.uk/1419/.
9. Hall, M. Efficiency and effectiveness: Digital futures in innovation, Oct. 2010.
Presentation at the JISC Future of Research Conference on 19th October 2010.
10. Kahan, J., and Koivunen, M.-R. Annotea: An open RDF infrastructure for
shared web annotations. In Proceedings of the 10th International Conference on
World Wide Web (2001), ACM, pp. 623–632.
11. LaLiberte, D., and Braverman, A. A protocol for scalable group and public
annotations. Comput. Netw. ISDN Syst. 27 (Apr. 1995), 911–918.
12. Lange, C., Schaffert, S., Skaf-Molli, H., and Völkel, M., Eds. 4th Seman-
tic Wiki Workshop (SemWiki 2009) at the 6th European Semantic Web Conference
(ESWC 2009), Hersonissos, Greece, June 1st, 2009. Proceedings (2009), vol. 464
of CEUR Workshop Proceedings, CEUR-WS.org.
13. Moody, G. Abundance obsoletes peer review, so drop it. http://opendotdotdot.
blogspot.com/2010/06/abundance-obsoletes-peer-review-so-drop.html.
14. Neuwirth, C. M., Kaufer, D. S., Chandhok, R., and Morris, J. H. Com-
puter support for distributed collaborative writing: defining parameters of interac-
tion. In Proceedings of the 1994 ACM conference on Computer supported coopera-
tive work (New York, NY, USA, 1994), CSCW ’94, ACM, pp. 145–152.
15. Nielsen, M. The future of science, 2008. Online: http://michaelnielsen.org/
blog/the-future-of-science-2/.
16. Poeschl, U. Interactive open access publishing and public peer review: The
effectiveness of transparency and self-regulation in scientific quality assurance.
IFLA Journal 36, I (2010), 40–46. DOI: 10.1177/0340035209359573, Online:
http://www.atmospheric-chemistry-and-physics.net/pr_acp_poschl_ifla_
journal_2010_interactive_open_access_publishing.pdf.
17. Schaffert, S., Eder, J., Grünwald, S., Kurz, T., Radulescu, M., Sint, R.,
and Stroka, S. KiWi – A platform for semantic social software. In Proceedings of
the 4th Workshop on Semantic Wikis, European Semantic Web Conference (2009).
18. Schickler, M. A., Mazer, M. S., and Brooks, C. Pan-browser support for
annotations and other meta-information on the world wide web. In Proceedings
of the fifth international World Wide Web conference on Computer networks and
ISDN systems (Amsterdam, The Netherlands, The Netherlands, 1996), Elsevier
Science Publishers B. V., pp. 1063–1074.
19. Schmidt, M., and Smrz, P. Annotation component for a semantic wiki. In
Proceedings of the 5th Workshop on Semantic Wikis, European Semantic Web
Conference (2010).
20. Smrz, P., and Schmidt, M. Information extraction in semantic wikis. In Proceed-
ings of the 4th Workshop on Semantic Wikis, European Semantic Web Conference
(2009).
21. Waltham, M. Why does one size not fit all in journal publishing?, 2010. Presen-
tation at the June 2010 Society for Scholarly Publishing meeting, slides available
online: http://www.marywaltham.com/SSP_Seminar_2010.pdf.
22. Wimalasuriya, D. C., and Dou, D. Ontology-based information extraction: An
introduction and a survey of current approaches. Journal of Information Science
36, 3 (2010), 306–323.
23. Zheng, Q. Structured annotations to support collaborative writing workflow. Mas-
ter’s thesis, The Faculty of Graduate Studies (Computer Science), The University
of British Columbia, Dec. 2005. (accessed online March 14, 2011).
24. Zheng, Q., Booth, K., and McGrenere, J. Co-authoring with structured
annotations. Department of Computer Science, University of British Columbia,
2006.