=Paper=
{{Paper
|id=Vol-1155/paper-07
|storemode=property
|title=A review of ontologies for describing scholarly and scientific documents
|pdfUrl=https://ceur-ws.org/Vol-1155/paper-07.pdf
|volume=Vol-1155
|dblpUrl=https://dblp.org/rec/conf/esws/Ruiz-IniestaC14
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==A review of ontologies for describing scholarly and scientific documents==
https://ceur-ws.org/Vol-1155/paper-07.pdf
A review of ontologies for describing scholarly
and scientific documents
Almudena Ruiz-Iniesta and Oscar Corcho
Ontology Engineering Group, Facultad de Informática
Universidad Politécnica de Madrid, Madrid, Spain
{almudenari, ocorcho}@fi.upm.es
Abstract. Several ontologies have been created in the last years for
the semantic annotation of scholarly publications and scientific docu-
ments. This rich variety of ontologies makes it difficult for those willing
to annotate their documents to know which ones they should select for
such activity. This paper presents a classification and description of these
state-of-the-art ontologies, together with the rationale behind the diffe-
rent approaches. Finally, we provide an example of how some of these
ontologies can be used for the annotation of a scientific document.
Keywords: ontology, document semantics, semantic publishing
1 Introduction
Semantic publishing [1,2] can be defined as the activity of enhancing a docu-
ment (e.g. a journal article) with semantic annotations, providing a way to un-
derstand the meaning of the published information and enabling the linking
to related documents. Semantic publications offer a better access to both their
content and metadata describing the entire documents, their structure, their
rhetorical elements and related information. Several ontologies have been cre-
ated to support this activity in different scholarly domains, e.g., EXPO [3] in
the scientific experiments domain, OMDoc as a markup format and data model
for Open Mathematical Documents [4], the SWAN ontology for modelling the
scientific discourse, developed in the context of building a series of applications
for biomedical research [5]. However, the variety of works that describe docu-
ments in different domains makes it difficult to choose the best ontology for the
annotation of scientific papers, besides the obvious use of Dublin Core Terms1 .
Moreover, there are no general conventions or rules on how to use the exist-
ing ontologies in semantic publishing. In order to shed light on this variety of
works, in this paper we review the most relevant ontologies for describing schol-
arly publications and we also present other vocabularies that allow embedding
formal metadata in documents using markup languages.
1
Dublin Core Metadata Element Set, Version 1.1 http://dublincore.org/
documents/2008/01/14/dces/
Therefore, the result of this work is a classification of the most important on-
tologies for describing scholarly documents. The proposed classification divides
ontologies into three main groups: ontologies for describing the document struc-
ture (sections, paragraphs, etc.), ontologies for describing the rhetorical elements
(introduction, results, etc.) and ontologies for describing bibliographies and ci-
tations. In what follows, we expand on these ontologies, and show how they
can be employed to describe a scientific paper. We also illustrate how some of
them could be applied to a published article from the journal Future Generation
Computer Systems, which is already encapsulated as a Research Object [6]2 .
The rest of the paper is organized as follows: Section 2 explains the main
ontologies that describe documents; Section 3 focuses on describing ontologies
for the scientific discourse; Section 4 presents the works that attempt to annotate
the references of a document; Section 5 introduces other vocabularies that allow
annotating documents. Finally, Section 6 concludes the paper and depicts some
recommendations to annotate a scientific document.
2 Ontologies for describing documents
In this section we describe ontologies that allow describing the structure of a
scholarly article or, more generally, of a document. Each ontology is presented
with its main characteristics (classes and properties) and an example of use.
One of the earliest works in this direction was the no-longer maintained
Document ontology3 , implemented in the SHOE language. This ontology focuses
only on the document type. Some of the documents types defined in this ontology
are: Abstract, Letter, Form, Lecture, etc.
The Ontology of Rhetorical Blocks (ORB)4 captures a coarse-grained rhetori-
cal structure of scientific publications, independently of their domain. The onto-
logy models a publication by means of three artefacts: the header, the body and
the tail. The header is the part of the publication that models meta-information
about the publication, including title, authors, affiliations, publishing venue and
abstract. The body is composed by four rhetorical blocks: introduction, methods,
results and discussion, according to the IMRAD [7] structure. Finally the tail
provides additional meta-information about the paper, related to external refer-
ences. The tail is represented by two ontological entities: acknowledgments and
references.
A recent work that attempts to annotate the entire characteristics of a docu-
ment is the Semantic Publishing and Referencing Ontologies5 a set of ontologies
that allow describing books and journal articles, citations, bibliographic records,
the component parts of documents, and various aspects of the scholarly publi-
cation process. This set of ontologies is composed by:
2
http://rohub.linkeddata.es/motifs_bundle_page-FGCS/
3
http://www.cs.umd.edu/projects/plus/SHOE/onts/docmnt1.0.html
4
http://www.w3.org/2001/sw/hcls/notes/orb/#ontology
5
SPAR, namespace http://purl.org/spar
– FaBiO6 , the FRBR-aligned Bibliographic Ontology, which allows recording
and publishing bibliographic records of scholarly documents.
– CiTO7 [8], the Citation Typing Ontology, which allows characterising cita-
tions, both factually and rhetorically.
– BiRO8 , the Bibliographic Reference Ontology, which allows describing bib-
liographic records and references, and their compilation into bibliographic
collections and reference lists.
– C4O9 , the Citation Counting and Context Characterization Ontology, which
allows the characterization of bibliographic citations in terms of their number
and their context.
– DoCO10 , Document Components Ontology, which allows describing the com-
ponent parts of a document. DoCO imports the Discourse Elements Onto-
logy11 and the Document Structural Patterns Ontology12 .
– PRO13 , the Publishing Roles Ontology, which allows characterising the roles
of agents in the publication process.
– PSO14 , the Publishing Status Ontology, which allows characterising the pu-
blication status of a document at each of the various stages in the publishing
process.
– PWO15 , the Publishing Workflow Ontology, which allows describing the
steps in the workflow associated with the publication of a document.
In this work we analyse those focused on describing the document content. Hence
we will describe in detail DoCO (see Section 2.1) and CiTO (see Section 4).
2.1 DoCO, Documents Components Ontology
The DoCO ontology provides a broad number of classes and relationships that
allow describing a document based on its structure and content. DoCO imports
two ontologies: Deo and the Document Structural Patterns Ontology. Deo is an
OWL2 ontology that describes the major rhetorical elements of a document.
It also provides a structured vocabulary for rhetorical elements within docu-
ments and it uses all the rhetorical block elements from the SALT Rhetorical
Ontology [9]. The pattern ontology defines formally patterns for segmenting a
document into atomic components, in order to be manipulated independently
and re-flowed in different contexts.
DoCO describes the vast majority of document components such as chapter,
preface, glossary, etc. Table 1 shows some of the classes from this ontology.
6
Namespace http://purl.org/spar/fabio/
7
Namespace http://purl.org/spar/cito
8
Namespace http://purl.org/spar/biro
9
Namespace http://purl.org/spar/c4o
10
Namespace http://purl.org/spar/doco
11
Namespace, http://purl.org/spar/deo
12
Namespace, http://www.essepuntato.it/2008/12/pattern
13
Namespace http://purl.org/spar/pro
14
Namespace http://purl.org/spar/pso
15
Namespace http://purl.org/spar/pwo
Table 1. List of some of the classes in DoCO
table section list
chapter figure glossary
front matter body matter preface
As far as Deo is concerned, Deo supports the main rhetorical elements in a
document, e.g., Introduction, Methods, Results and Conclusions. These elements
give a defined rhetorical structure to the paper, which assists readers to identify
the important aspects of the paper. Notice that the rhetorical organization of a
paper does not necessarily correspond neatly to its structural components (sec-
tions, paragraphs, etc.). In this sense, Deo and DoCO complement one another.
Table 2 shows some of the most relevant classes from Deo.
Table 2. List of some of classes in Deo
acknowledgements background conclusion
introduction future work methods
related work results discussion
motivation problem statement biography
3 Scholarly and scientific discourse ontologies
The scientific discourse has particular characteristics that are not covered by the
aforementioned ontologies. Particularly, a scientific discourse has goals, claims,
experiments, evaluations and so on. Indeed, the reasoning of the assertion of the
scientific document is crucial for scholarly and scientific publishing, in proposing
hypotheses and advancing evidence in their support. Several works have been
proposed to model the discourse argumentation normally present in scientific
articles.
One of the first works to address the modelling of the scholarly discourse
was ScholOnto[10]. The ScholOnto ontology provided a small set of conceptual
and relational types. The main class of the ontology is the Claim. All claims
are owned by an agent, and have some form of justification. Claims assert new
relationships with other claims, or between concepts.
The work proposed by [11,12] identifies the main components of scientific in-
vestigations and construct the Core Information about Scientific Papers (CISP)
metadata about the content of papers. The main classes proposed in CISP are:
Goal of investigation, Motivation, Object of investigation, Research method, Ex-
periment, Observation, Result and Conclusion. CISP metadata makes use of the
ontology of experiments EXPO16 [3] as a core ontology. CISP includes eight key
16
http://expo.sourceforge.net/
classes that are presented in Table 3. Many of these key classes have additional
subclasses and properties.
Table 3. List of the CISP key classes.
goal of investigation motivation object of investigation research method
experiment observation result conclusion
As aformentioned, CISP makes use of EXPO, a very complete ontology about
scientific experiments. The aim of this ontology is to provide a controlled vo-
cabulary of scientific experiments. For this purpose EXPO defines over 200 con-
cepts to allow providing a formal description of experiments for efficient analysis,
annotation and sharing of results. EXPO is able to describe computational and
physical experiments, experiments with explicit and implicit hypothesis. EXPO
defines general classes including ScientificExperiment, ExperimentGoal, Exper-
imentTechnology, ExperimentResult, etc. (see Table 4).
Table 4. Some of EXPO classes.
experimental design strategy fact field of study
procedure variable experimental technology
scientific activity hypothesis forming interpreting results
Inspired by EXPO and CISP metadata, the work described in [13] proposes
the Core Scientific Concepts, CoreSCs. The CoreSCs is a scheme built upon
eleven categories at the sentence level that allows the automatic recognition of
each one of the categories in scientific articles. The CoreSC includes: hypothesis,
motivation, goal, object, background, method, experiment, model, observation,
result and conclusion. The authors argued that these categories describe the main
components of a scientific investigation. The first application of this scheme has
been used to automatically annotate papers in Biochemistry and Chemistry.
Other works, such as The Argument Model Ontology17 , use the ‘Toulmin
Model of Argument’ [14]. Toulmin proposed a layout containing six interrelated
components for analysing arguments: claim, evidence, warrant, backing, qualifier
and rebuttal. The Argument Model Ontology models this components through
a set of 8 classes and 21 properties (see Table 5). The following snippet shows
an example of use of this ontology:
17
http://www.essepuntato.it/2011/02/argumentmodel
: sentence1 dcterms : description " We propose a catalog of
domain independent conceptual abstractions for workflow
steps that we call scientific workflow motifs " .
: sentence2 dcterms : description " We present an empirical
analysis performed over 260 scientific workflow
descriptions . " .
: argument1 a amo : Argument ;
amo : hasClaim : sentence1 ;
amo : hasEvidence : sentence2 .
Table 5. List of the Argument Model Ontology classes, together with some properties.
Classes Properties
argument argumentation entity backing claim backs forces has evidence has claim
evidence qualifier rebuttal warrant involves relates to support proves
A very recent work is the one proposed in [15] MicroPublications18 . In this
work the authors employ the Toulmin’s model updated by Bart Verheij [16] and
then they propose a semantic model of scientific argument and evidence designed
for representing the key arguments and evidence in scientific articles. MicroP-
ublications proposes a model to construct an argumentation network linking
textual statements and data as evidence for claims.
Beyond the works that employ a linguistic model there are other works that
are focused on describing the scientific discourse itself and the relations among
the claims and hypotheses made by the author of the document. That is the case
of the last two works that we present here.
The SWAN19 ontology [5] models the scientific discourse. The SWAN project
is part of the Annotation Ontology [17] and it has evolved into the Domeo anno-
tation toolkit20 (a web application enabling users to create and share ontology-
based annotations on HTML and XML documents). The core of the SWAN
ontology models the discourse elements providing a model of assertions, ques-
tions and hypotheses. The SWAN discourse elements are:
– Research statements: a claim or an hypothesis.
– Research questions: topics under investigation.
– Structured comments: the structured representation of a comment published
in a digital resource.
On the other hand the SWAN ontology also provides discourse relationships,
which are a set of relationships that can be used to build scientific discourse.
Some of the discourse relationships are in Table 6.
18
http://purl.org/mp
19
Semantic Web Applications in Neuromedicine http://www.w3.org/TR/hcls-swan/
20
http://swan.mindinformatics.org/
Table 6. The discourse relationships properties proposed by the SWAN ontology.
refers to inconsistent with alternative to
relevant to arises from motivates
4 Ontologies for describing bibliography and citations
The references of a document play an important role in the paper. One of the
most widely used ontologies for describing bibliographic entities is BIBO, The
Bibliographic Ontology Specification [18]. BIBO defines a set of classes to identify
the type of document based on its origin (journal, book, webpage, etc.), where
bibo:Document is the key class of this model. BIBO includes Dublin Core terms
to cover common needs, uses FOAF (Friend of a Friend)21 to describe authors,
and adds other classes and properties, as shown in Table 7.
Table 7. List of some of the classes from BIBO, together with some properties.
Classes Properties
academic article journal collection homepage publisher rights
book chapter issue status subject time
Let us suppose that we want to annotate our journal paper with BIBO. In
this case we would include the following information:
@ prefix bibo : < http :// purl . org / ontology / bibo > .
@ prefix dc : < http :// purl . org / dc / terms > .
[ a bibo : Article ;
dc : title " Common motifs in scientific workflows ... " ;
dc : date " 2013 -09 -21 "
bibo : volume " In press " ;
dc : creator " Daniel Garijo "
bibo : authorList ( " Daniel Garijo " " ... " " ... " )
...
]
Although the BIBO ontology identifies in a unique way each paper, BIBO is
unable to express the history of a paper. In this sense, the work proposed by [19]
extends the BIBO ontology in order to include the internal workflow of journals,
conferences, and so forth. The result of this work allows tracking the history of
a scientific paper.
As mentioned in Section 2, there are other ontologies focused on describing
bibliographic entities, such as FaBiO22 [8]. FaBiO describes bibliographic entities
(e.g. books and journal articles) and their grouping (e.g. into book series and
21
http://www.foaf-project.org/
22
FaBiO http://purl.org/spar/fabio
journal issues). FaBiO classes are structured according to the FRBR schema of
Works, Expressions, Manifestations and Items [20]. FaBiO has additional proper-
ties to extend the FRBR data model by linking the different parts of the FRBR
schema. The FaBiO classes are divided into four main groups: Works with 69
subclasses, Expressions with 92 subclasses, Manifestations with 10 subclasses
and Items with 4 subclasses.
Finally, the Citation Typing Ontology23 enables the characterization of the
nature or type of citations, both factually and rhetorically. CiTO contains 41
object properties that add more information to the cite (e.g., agrees with, cor-
rects, likes, uses method in). CiTO allows characterising citations in three ways:
explicit citations (e.g. the reference list of a journal article), indirect citations
(e.g. a citation to a more recent paper by the same research group on the same
topic), or implicit citations (e.g. as in artistic quotations or parodies, or in cases
of plagiarism). Some of the CiTO properties are enumerated in Table 8.
Table 8. List of some of CiTO properties
agrees with cites as authority cites as evidence confirms corrects
describes disagrees with extends includes excerpt from discusses
supports updates uses conclusions from uses data from uses method in
Let us see an example of using CiTO for defining the citations of the journal
paper.
@ prefix cito : < http :// purl . org / spar / cito / > .
: journalPaper cito : agrees_with < http :// dx . doi . org /10.1016/ j .
future .2008.06.012 > ;
cito : extends < http :// dx . doi . org /10.1109/ eScience
.2012.6404427 > ;
5 Other vocabularies for describing documents
There are also other general ontologies that have been used for the annotation
of any type of documents, not just scientific ones.
The most obvious and extended one is Dublin Core Metadata Terms (DCT),
which contains fifteen properties to specify the characteristics of electronic docu-
ments (creator, date, contributor, description, format, etc). The terms in DCT
are intended to be used in combination with terms from other vocabularies, as
we saw above in the ORB vocabulary.
Friend of a Friend (FOAF) is a stable ontology that contains some classes
such as Agent, Person, Organization, Group, Project, Document, Image, etc.,
and some properties to describe the instances of these classes. This vocabulary
23
CiTO, namespace http://purl.org/spar/cito
allows describing the authors of documents, their affiliations and other relevant
information about them.
Finally, the Semantic Web Conference Ontology24 is an ontology for des-
cribing academic conferences. This ontology establishes how to use classes and
properties from other ontologies (FOAF, Dublin Core, SIOC25 and iCal/RDF
Calendar26 ) and provides some classes for things relative to conferences. Some
of these classes are: AcademicEvent, ConferenceVenuePlace, Proceedings, etc.
6 Conclusions and recommendations
We conclude this review paper by doing some recommendations about how to
annotate scholarly documents with the aforementioned ontologies. These recom-
mendations are part of our approach to describe scientific documents, which we
are applying in the context of the DrInventor project in the domain of computer
graphics.
First, we propose to use the DoCO ontology for describing the document
structure. In Figure 1 (annotations with doco prefix) we can see how the title,
section, table and figure classes are employed to annotate the different parts
of the document. Due to space restrictions in the figure, all the classes do not
appear. The annotation of the document structure allows comparing two or more
papers according to their structure. It will also be possible to detect frequent
document structures according to the domain or the kind of document (e.g.
journal paper, book).
The next step is to annotate the rhetorical elements of the document. In this
sense, Deo seems to be the most appropriate ontology. In Figure 1(annotations
with deo prefix) we use the background class to identify what is the essential
knowledge for understanding the problem. The class contribution annotates a
description of the part that this publication plays in the overall field. Notice
that Deo covers the most relevant rhetorical elements, but we should extend Deo
with some specific classes according to the concrete domain. Particularly we are
interested in the scientific domain, and we can see in Figure 1(annotations with
scientific prefix) two new classes that are necessary for this domain: hypothesis
and goal. We have defined a hypothesis as an “educated guess about how things
work”. A hypothesis should be “something that you can test”. On the other
hand, a goal of an investigation, according to the definition provided by [21],
is the “target state of the investigation where intended discoveries are made,
approaches are tested, problems are demonstrated, tasks formulated etc.”
For describing the bibliography and citations we have employed BIBO and
CiTO. BIBO describes in a detailed way each one of the references. In this sense
we can establish a document classification. The second ontology, CiTO, has been
employed for characterising the nature of the bibliographic citation linking the
citing paper to the cited paper. Figure 1 shows that the citing paper extends
24
http://data.semanticweb.org/ns/swc/ontology
25
http://sioc-project.org/
26
http://www.w3.org/TR/rdfcal/
the cited paper, because the author of the citing paper says that this document
extends their previous work.
Finally, we point out how to use the FOAF ontology to annotate each one of
the authors of the document. The use of this ontology allows describing people
and their relations.
In brief, we propose to use the DoCO ontology for describing the document
structure, the Deo ontology gives way to describe the vast majority of rhetorical
elements, but we believe that it is necessary to extend this ontology in order
to cover the concrete elements of the application domain. The BIBO ontology
describes references according the kind of document and all the characteristics
involved in the publication process. We propose to use the CiTO ontology for
describing the rhetoric of the citations (in this way we can establish a network
with other works). In order to describe the scientific discourse it is necessary to
describe the most important elements of the domain (e.g. the biological domain
has probably different discourse elements than the computer graphics domain).
An important part in the scientific discourse are the claims done by the author of
the paper and how they are contextualized. For this purpose we propose to use
the discourse model proposed by [11]. This model can be annotated with most
of the CiTO properties. In this sense, our nearly future work is to provide an
ontology for describing the scientific discourse according to the aforementioned
model by doing an extension of the proposed ontologies.
In this paper we have described the most relevant ontologies used for des-
cribing scientific documents. We have classified these ontologies into three main
groups, those that describe the discourse of a document (either scientific or
generic), those that allow describing the document structure and those ded-
icated to describe references and citations. At last, we have presented other
well-known vocabularies that provide generic information about any type of
document. Moreover, we have also sketched out a model for the semantic en-
hancement of documents based on some of these ontologies, mainly those that
describe the document structure, their rhetorical elements and their references.
Acknowledgments
This work is supported by the FP7 European project Dr Inventor FP7-611383.
References
1. Shotton, D.: Semantic publishing: the coming revolution in scientific journal pub-
lishing. Learned Publishing 22(2) (April 2009) 85–94
2. Waard, A.d.: From proteins to fairytales: Directions in semantic publishing. IEEE
Intelligent Systems 25(2) (March 2010) 83–88
3. Soldatova, L.N., King, R.D.: An ontology of scientific experiments. Journal of The
Royal Society Interface 3(11) (December 2006) 795–803
4. Kohlhase, M.: OMDoc: Open mathematical documents. In: OMDoc An Open
Markup Format for Mathematical Documents. Number 4180 in Lecture Notes in
Computer Science. Springer Berlin Heidelberg (January 2006) 25–32
D.Garijo, et al., Common motifs in scientific worksflows: An empirical analysis, Future Generation Computer
Systems (2013), In press.
doco:title Common motifs in scientific workflows: An empirical analysis
doco:list of authors Daniel Garijo, Pinar Alper, Khalid Belhajjame, Oscar Corcho , Yolanda Gil ,Carole Goble foaf:person
Ontology Engineering Group, Universidad Politécnica de Madrid, Spain
School of Computer Science, University of Manchester, United Kingdom
Information Sciences Institute, Department of Computer Science, University of Southern California, United States
Abstract. Workflow technology continues to play an important
role as a means for specifying and enacting
computational experiments in modern science……… 4. Scientific workflow motif catalog for abstracting
workflows
……………………………………………………………....
Keywords. Workflow motif, Workflow pattern, Taverna,
Wings...
1 Introduction
A scientific workflow is a template defining the set of tasks doco:figure
deo:background needed to carry out a computational experiment
[1].……………………………………………………
..In this paper we present the result of an empirical analysis
scientific:goal
performed over 260 workflow descriptions from Taverna [2],
Wings [3], Galaxy [4] and Vistrails [5]. Based on this analysis, we
propose a catalog of domain independent conceptual Fig. 1. Sample motifs in a Wings workflow fragment for drug discovery. A
scientific:hypothesis comparison analysis is performed on two different input datasets
abstractions for workflow steps that we call scientific workflow (SMAPV2). The results are then sorted (SMAPResultSorter) and finally
motifs…………………………………………………………………………. merged (Merger, SMAPAlignementResultMerger).
The new contributions reported on in this paper are an
deo:contribution extension of the related work in Section 2, the addition and 7 Conclusions
extension of scientific domains from Wings and Taverna
…………………………………………………………....
workflows (Social Network Analysis, Astronomy and Domain In doing so, we have defined a catalog of motifs distinguishing Data-
Independent) in Sections 3 and 5; and the analysis of workflows Operation motifs, which describe the tasks carried out by the workflow
from the Galaxy and Vistrails systems among different domains deo:conclusion
steps, from Workflow- Oriented motifs, which describe the way those
(Genomics, Text Mining, Domain Independent and Medical tasks are implemented within the workflow…………………………………………
Informatics). Finally, we have also revisited the motif catalog …………………………………………………………
(Section 4), our previous results (Section 5) and conclusions As part of our future work, we envisage deriving best practices that can
(Section 7) according to our new findings. be used in workflow design and providing tools that assist users in deo:future
This paper extends our previous work [12], which performed automatic workflow annotation using our Motif work
cito:extends an analysis of 177 workflows from Wings and Taverna Ontology…………………………………………………………
Acknowledgments
2 Related work This research was supported by the…
doco:section …………………………………………………………………… doco:bibliography
References
……………………………………………… [1] Ewa Deelman, Dennis Gannon, Matthew Shields, Ian Taylor ….
[2] Katherine Wolstencroft, Robert Haines, Donal Fellows, Alan Williams,
…
3 Analysis setup [3] Yolanda Gil, Varun Ratnakar, Jihie Kim, Pedro A. González-Calero, Paul
………………………………..………………………………… T. Groth,….
……………………………………………… [6] Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat
3.2 Approach for workflow analysis Jaeger,
Our analysis has been performed based on the documentation, Matthew Jones, Edward A. Lee, Jing Tao, Yang …
metadata and traces available for each of the workflows within the [7] Thomas Fahringer, Radu Prodan, Rubing Duan, Jürgen Hofer, Farrukh
cohort Nadeem…
studied……………………………………………………………………………………………… [8] Jia Zhang, Wei Tan, J. Alexander, I. Foster, R. Madduri, Recommend-as-
…… yougo…
………………………………………
[12] Daniel Garijo, Pinar Alper, Khalid Belhajjame, Oscar Corcho, Yolanda
doco:table Gil, Carole Goble, Common motifs in scientific workflows: an empirical
analysis, in: 8th IEEE International Conference on eScience 2012, Chicago,bibo:article
IEEE Computer Society Press, USA, 2012.
Fig. 1. The proposed model for describing a scientific document taking together some
of the described ontologies.
5. Ciccarese, P., Wu, E., Wong, G., Ocana, M., Kinoshita, J., Ruttenberg, A., Clark,
T.: The SWAN biomedical discourse ontology. Journal of Biomedical Informatics
41(5) (October 2008) 739–751
6. Bechhofer, S., Buchan, I., De Roure, D., Missier, P., Ainsworth, J., Bhagat, J.,
Couch, P., Cruickshank, D., Delderfield, M., Dunlop, I., Gamble, M., Michaelides,
D., Owen, S., Newman, D., Sufi, S., Goble, C.: Why linked data is not enough for
scientists. Future Generation Computer Systems 29(2) (February 2013) 599–611
7. Sollaci, L.B., Pereira, M.G.: The introduction, methods, results, and discussion
(IMRAD) structure: a fifty-year survey. Journal of the Medical Library Association
92(3) (July 2004) 364–371
8. Peroni, S., Shotton, D.: FaBiO and CiTO: ontologies for describing bibliographic
resources and citations. Web Semantics: Science, Services and Agents on the World
Wide Web 17 (December 2012) 33–43
9. Groza, T., Handschuh, S., Möller, K., Decker, S.: SALT - semantically annotated
LaTeX for scientific publications. In Franconi, E., Kifer, M., May, W., eds.: The
Semantic Web: Research and Applications. Number 4519 in Lecture Notes in Com-
puter Science. Springer Berlin Heidelberg (January 2007) 518–532
10. Shum, S.B., Motta, E., Domingue, J.: ScholOnto: an ontology-based digital library
server for research documents and discourse. International Journal on Digital
Libraries 3(3) (October 2000) 237–248
11. Liakata, M., Teufel, S., Siddharthan, A., Batchelor, C.R.: Corpora for the con-
ceptualisation and zoning of scientific papers. In: Proceedings of the International
Conference on Language Resources and Evaluation. (2010)
12. Soldatova, L., Liakata, M.: An ontology methodology and CISP - the proposed
core information about scientific papers. Programme/Project deposit (December
2007)
13. Liakata, M., Saha, S., Dobnik, S., Batchelor, C., Rebholz-Schuhmann, D.: Au-
tomatic recognition of conceptualization zones in scientific articles and two life
science applications. Bioinformatics 28(7) (January 2012) 991–1000
14. Toulmin, S.E.: The Uses of Argument. Cambridge University Press (July 2003)
15. Clark, T., Ciccarese, P.N., Goble, C.A.: Micropublications: a semantic model for
claims, evidence, arguments and annotations in biomedical communications. Sub-
mitted to the Journal of Biomedical Semantics (May 2013)
16. Verheij, B.: The toulmin argument model in artificial intelligence. In Simari, G.,
Rahwan, I., eds.: Argumentation in Artificial Intelligence. Springer US (January
2009) 219–238
17. Ciccarese, P., Ocana, M., Garcia Castro, L., Das, S., Clark, T.: An open annotation
ontology for science on web 3.0. Journal of Biomedical Semantics 2(2) (July 2011)
1–24
18. D’Arcus, B., Giasson, F.: Bibliographic ontology specification (November 2009)
19. Hu, Y., Janowicz, K., McKenzie, G., Sengupta, K., Hitzler, P.: A linked-data-
driven and semantically-enabled journal portal for scientometrics. In Alani, H.,
Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N.,
Welty, C., Janowicz, K., eds.: The Semantic Web. Number 8219 in Lecture Notes
in Computer Science. Springer Berlin Heidelberg (January 2013) 114–129
20. Tillett, B.: What is FRBR? a conceptual model for the bibliographic universe.
The Australian Library Journal 54(1) (2005) 24–30
21. Liakata, M., Soldatova, L.: Guidelines for the annotation of general scientific con-
cepts. Aberystwyth University, JISC Project Report (November 2008)