How to cite data

As an approach towards developing best practice for data citation, I recently wrote a Data Citation Best Practice Discussion Document that is available on Google Docs, and that I have now slightly revised to Version 2 [1].

In that document, I first compared what is recommended by DataCite [2] and by Altman and King [3] with what currently practised by the Dryad Data Repository and what presently occurs ‘in the wild’ in a handful of journal articles that reference Dryad datasets.  I then proposed some ‘internal’ recommendations for Dryad to adopt, and concluded with draft Data Citation Best Practice Recommendations.  As I say in the preface to the document:

“Since Dryad is pioneering data management in terms of data resources that are linked to journal articles, it is to be hoped that by first developing citation best practice in the Dryad context we can thereby catalyse its wider spread.  If we can thus agree what such best practice should be among the Dryad community and implement such best practice proposals, we can then promote such practices within the wider scholarly community.”

I realized that much of the confusion and disagreement concerning the best method of citing data resources within earlier e-mail threads resulted from a conflation of ideas about two entities which in the conventional citation of journal articles are quite distinct:

  • the in-text citation containing an in-text reference pointer, e.g. “this paper builds upon the work of Jones et al. [15].”     and
  • the actual reference to Jones et al. within the article’s reference list, e.g. “[15] Jones A, Bloggs B and Smith C (2008). Title. JournalName
    14:132-134. doi:*****.”

Thus, in an e-mail I wrote on 27 April, where I said

“Excellent, but what we really want is for the data citations to be included in the reference list along with the bibliographic citations, following the DataCite model: Creator (PublicationYear): Title. Version. Publisher. ResourceType. Identifier “

. . . I should also have stressed the need for explicit in-text citations that denote such references.

All that is explained within the Google Docs paper.  In that paper I also proposed having a separate Data Resources section within the body text of a journal article, in which data resource citations can be gathered.  That does not preclude these resources also being cited, where appropriate, within the Methods and Materials or Results sections of the paper, but is designed to put data resource citations “on the map”, so to speak, as important new publication performative acts.

It is not appropriate, in my mind, for data citations to be included in the Acknowledgements section of a paper, which is designed for acknowledging contributions to the work from people and funding agencies, even if Thomson Reuters has developed methods to parse such entries, since they also have well-established mechanisms for harvesting proper (data) references from the reference list.

All the ontological terms required to mark up in-text reference pointers and their textual contexts, references, reference lists, etc., to permit automated detection and harvesting of data citations and references, are available as RDF within the SPAR (Semantic Publishing and Referencing) Ontologies (http://purl.org/spar/), which were designed precisely to facilitate such work.

Since writing my Data Citation Best Practice Discussion Document, I was invited (on a purely voluntary non-commercial basis, I should add!) to work with Pensoft Journals, a publisher that specialises in publishing biodiversity and biological systematics papers, and that has taken the lead in promoting the publication of datasets with DOIs, to contribute to and help revise their now-published Data Publishing Policies and Guidelines for Biodiversity Data [4].  This 34-page paper has a three-page section on how to cite data in Pensoft Journals, which I discuss in the next blog post, and which I am pleased to say includes all the recommendations discussed above.

[1]     David Shotton (2011) Data Citation Best Practice Discussion Document. Google Docs. https://docs.google.com/document/d/1kF8-faB72l4dKTLEyx6Z5cIabk68GrJ9GraCtWnK0qQ/edit?hl=en_GB&authkey=CPPW46wL#.

[2]    The DataCite Metadata Kernel version 2.0 (2011). http://datacite.org/schema/DataCite-MetadataKernel_v2.0.pdf.

[3]    Micah Altman and Gary King (2007). A proposed standard for the scholarly citation of quantitative data. D-Lib Magazine. 13. http://www.dlib.org/dlib/march07/altman/03altman.html.

[4]     Penev L, Mietchen D, Chavan V, Hagedorn G, Remsen D, Smith V, Shotton D (2011). Pensoft Data Publishing Policies and Guidelines for Biodiversity Data. Pensoft Publishers, http://www.pensoft.net/J_FILES/Pensoft_Data_Publishing_Policies_and_Guidelines.pdf.

Cite this article as: davidshotton, "How to cite data," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/153.

Nomenclature for data publications and citations

The meaning of the word “dataset” is ambiguous, changing with context.  In FaBiO , the FRBR-aligned bibliographic ontology, we define “dataset” at the conceptual level (i.e. as a frbr:Work) thus:

fabio:Dataset: “A collection of related facts, often expressed in numerical form and encoded in a defined structure.”

In situations demanding precision in terminology, such as data repository metadata and data citation, I think it is wiser to avoid the use of the term “dataset”, and instead to use the terms “data file” and “data package“, as the entities that data repositories handle and to which identifiers pertain.

In FaBiO, a data file is defined as follows:

fabio:DataFile: “A realisation of a fabio:Dataset (a frbr:Work) containing a defined collection of data with specific content and possibly with a specific version number, that can be embodied as a fabio:Digital Manifestation (a frbr:Manifestation with a specific format) and represented by a specific fabio:ComputerFile (a frbr:Item) on someone’s hard drive.”

FaBiO also has the class

fabio:MetadataDocument “A document that contains metadata information describing one or more characteristics of an entity”, which is a realization of fabio:Metadata
“A separate work that provides information describing one or more characteristics of a resource or entity”.

Using ORE, a data package containing data files and a metadata manifest can be defined as type of ore:Aggregation.  However, ORE is not sufficiently expressive to meet the need of repositories who may wish to refer specifically to data packages, to manifests and to the landing pages displaying metadata relating to data packages and individual data files in their RDF metadata. For this reason, we have developed two tiny new ontologies, DaPO, the Data Package Ontology, and LaPO, the Landing Page Ontology, that provide the missing nomenclature, as will be detailed in a future blog post.

Cite this article as: davidshotton, "Nomenclature for data publications and citations," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/144.

DataCite2RDF – Mapping DataCite Metadata Scheme Terms to ontologies

The DataCite Metadata Kernel version 2.0 [1] specifies the minimal metadata, and optional metadata, that should accompany a DataCite DOI for the identification of a published data entity. Within the Metadata Kernel document there is an XML mapping of these metadata terms, using DCMI Metadata Terms, and an example encoded in XML.

Silvio Peroni and I recently published a mapping of the DataCite metadata elements to RDF using ontology terms [2], in order to enable data repositories to publish DataCite metadata in RDF as Open Linked Data, enabling these metadata to be understood programmatically and integrated automatically with similar data from elsewhere.

Our mapping covers all the main terms, and the Relation Type sub-properties that describe the relationship of the related resource to the resource being registered, but does not address DataCite sub-terms, e.g. 2.2.1 nameIdentifierScheme.

Wherever possible, commonly used Dublin Core Elements, DCMI (Dublin Core Metadata Initiative) Metadata Terms, FOAF (Friend of a Friend Vocabulary) and PRISM (Publishing Requirements for Industry Standard Metadata) terms have been used.

These have been supplemented, as appropriate, by terms:

from FRBR (Functional Requirements for Bibliographic Records),

from the following SPAR (Semantic Publishing and Referencing) Ontologies:

                CiTO, Citation Typing Ontology

                FaBiO, FRBR-aligned Bibliographic Ontology, and

CiTO4Data, an extension of CiTO for datasets that provides the properties cito4data:compiles and cito4data:isCompiledBy that the DataCite Metadata Kernel requires;

and from a new DataCite Ontology (http://purl.org/spar/datacite/) that we created to provide the following four object properties lacking in other Ontologies:

                    datacite:hasPrimaryIdentifier

                    datacite:hasAlternateIdentifier

                    datacite:hasRelatedIdentifier

                    datacite:hasPersonalIdentifier

Use of DCMI Metadata Terms in RDF

An object property has a class or a URI as its object, while a data property has a literal (e.g. text, number, date) as its object, and may have a W3C XML Schema Definition Language (XSD) datatype qualifier, e.g. ^^xsd:date. (See http://www.w3.org/TR/xmlschema11-2/).

Many Dublin Core properties are not formally specified to be one or another, leading to potential confusion. In the following mapping, Dublin Core Elements are always used as data properties, while Dublin Core Metadata Initiative Metadata Terms are used either as data properties or as object properties, as helpfully specified by the Max Planck Digital Library in their document entitled How to use DCMI Metadata as linked data.  

In our DataCite2RDF mapping document [2], alternative mappings are given where appropriate, separated by semi-colons.  Both dc: and dcterms: properties are listed.  Preferred terms are shown bold.  RDF statements are given in Turtle notation.

Accompanying this DataCite2RDF mapping document, we published as Google docs both an RDF mapping of the DataCite XML example, and an RDF mapping of the metadata for a Dryad repository holding, showing how DataCite2RDF can be used for real data.

We welcome feedback on these documents: <david.shotton@zoo.ox.ac.uk> and <speroni@cs.unibo.it>.

[1]    The DataCite Metadata Kernel version 2.0 (2011). http://datacite.org/schema/DataCite-MetadataKernel_v2.0.pdf.

[2]     David Shotton and Silvio Peroni (2011). Mapping DataCite Metadata Scheme Terms (v2.0) to ontologies (DataCite2RDF). Google docs. https://docs.google.com/document/d/1paJgvmCMu3pbM4in6PjWAKO0gP-6ultii3DWQslygq4/edit?authkey=CMeV3tgF&hl=en_GB.

Cite this article as: davidshotton, "DataCite2RDF – Mapping DataCite Metadata Scheme Terms to ontologies," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/139.

 

CiTO4Data – a new data-centric citation typing ontology

This is the first of a series of blog posts on the Open Citations blog that address the problem of citing data entities, for example a data package in a data repository, rather than bibliographic entities such as journal articles. For these purposes, the existence of DataCite to assign DOIs to datasets, and extensions to the SPAR (Semantic Publishing and Referencing) Ontologies to handle data items, are both important. This post addresses the second of these two issues.

As described in the previous blog post, CiTO, the Citation Typing Ontology, focuses on providing ontological properties to characterize citations of bibliographic entities. However, several of its relationships can also be used in relation to data entities:

Sub-classes of cito:cites

    cito:citesAsDataSource

    cito:citesAsEvidence

    cito:corrects

    cito:extends

    cito:updates

    cito:usesDataFrom

When attempting to map the DataCite metadata elements to ontology terms, to be described in a subsequent blog post, we encountered the DataCite requirement for the properties compiles and isCompiledBy, which are absent from CiTO and other ontologies that we know about and which cannot be conceived as relating to bibliographic entities.

Conscious of a growing need to specify metadata for data entities as well as bibliographic entities, and reluctant to include in CiTO relationships that cannot be applied to bibliographic entities, Silvio Peroni and I have thus created CiTO4Data, a new ontology within the SPAR ontology suite, to be found at http://purl.org/spar/cito4data/.

CiTO4Data, the Citation Typing Ontology for Data, is an extension ontology written in OWL 2 DL that imports CiTO, the Citation Typing Ontology, and adds extra object properties that are relevant only for datasets, computer programs and other similar digital objects, but not for conventional bibliographic entities.

To date, CiTO4Data adds just a single new object property

    cito4data:compiles

and its inverse

    cito4data:isCompiledBy

created to meet the need of encoding the DataCite Metadata Kernel v2.0 into RDF. We intend to expand CiTO4Data to include other properties relevant only for citing data entities, as the need arises.

NOTE: In the summer of 2012, we decided for simplicity to include these two properties within CiTO itself (as cito:compiles and cito:isCompiledBy), and to deprecate the CiTO4Data ontology.

Cite this article as: davidshotton, "CiTO4Data – a new data-centric citation typing ontology," in OpenCitations blog, 30/06/2011, https://opencitations.hypotheses.org/128.

BIBO2SPAR, an RDF Mapping of BIBO to the SPAR Ontologies

In the previous blog post, I made a textual comparison between BIBO v1.3, the Bibliographic Ontology developed by Bruce D’Arcus and Frédérick Giasson, and FaBiO, the FRBR-aligned Bibliographic Ontology.

In this post, I wish to introduce our RDF mapping of BIBO (version 1.3, undated, downloaded from http://purl.org/ontology/bibo/) to the SPAR (Semantic Publishing and Referencing) Ontologies, particularly to FaBiO (version 1.1, dated 23-06-2011, downloadable from http://purl.org/spar/fabio/), to permit cross-walk between these two systems.

The mapping takes the form of an alphabetical listing of all BIBO classes, object properties and data properties, for each of which is provided a SKOS mapping to equivalent classes, object properties or data properties in FaBiO or one of the other SPAR ontologies, or to relevant terms in FRBR, PRISM, FOAF, SKOS or Dublin Core Metadata Initiative Terms, where such a mapping is possible. Where it is not, a note is included to explain why.

Note that most of the classes in FaBiO are arranged according to the FRBR data model into fabio:Works (69 sub-classes), fabio:Expressions (92 sub-classes), fabio:Manifestations (10 sub-classes) and fabio:Items (4 sub-classes). 38 of the BIBO classes are sub-classes of bibo:Document, which roughly corresponds to fabio:Expression.

Note also that, because of the ambiguity and multiplicity of meanings of the word “document”, FaBiO intentionally lacks the class fabio:Document, using instead the generic term fabio:Expression and more specific sub-classes. In contrast, BIBO classifies things as diverse as films, slideshows and web pages as sub-classes of bibo:Document.

Note further that BIBO contains classes that describe social events, such as bibo:Interview and bibo:Performance, and that describe specific legal documents such as bibo:LegalDecision and bibo:Legislation. These have no equivalents in the SPAR Ontologies.

The mapping document, BIBO2SPAR, is written as validated RDF in Turtle format, and is available at https://sempublishing.svn.sourceforge.net/svnroot/sempublishing/SPAR/BIBO2SPAR.ttl

In the mappings, the BIBO class or property name is given on the left of each line, followed by the SKOS mapping relationship and the equivalent class or property name used in the SPAR ontologies, e.g.

bibo:AudioDocument skos:exactMatch fabio:AudioDocument .

This mapping document has been prepared by and is copyright © David Shotton, University of Oxford, and Silvio Peroni, University of Bologna, 23 June 2011, and is published as open data under a Creative Commons 2.5 attribution license.

Please contact <david.shotton@zoo.ox.ac.uk> or <speroni@cs.unibo.it> for further information.

Cite this article as: davidshotton, "BIBO2SPAR, an RDF Mapping of BIBO to the SPAR Ontologies," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/113.

Comparison of BIBO and FaBiO

BIBO v1.3, the Bibliographic Ontology developed by Bruce D’Arcus and Frédérick Giasson [1], was the first OWL ontology dedicated to describing bibliographic entities, and has attracted a wide group of users. It provided the much-needed ability to describe the nature of cited works in RDF to a high degree of granularity, in terms of Title, Abstract, Journal, Volume, Pages, ISSN, DOI, dataCopyrighted, editor, etc.

FaBiO, the FRBR-aligned Bibliographic Ontology, which was created more recently and was introduced in a previous blog post [2], bears many similarities with BIBO, including its overall scope and intention, and the inclusion of PRISM and DC Terms data properties. A clear comparison of these two ontologies, and the provision of a mapping between them, is long overdue.

The primary differences between FaBiO and BIBO are as follows:

  1. While BIBO is a ‘flat’ ontology, FaBiO is structured according to the FRBR conceptual model, in which publication entities are described from four different and correlated points of view, those of Work, Expression, Manifestation and Item, each of which is a FRBR Endeavour, as defined in a previous blog post. This structure has been introduced to provide greater expressivity. For example, using FaBiO one can describe a fabio:ResearchPaper that can be published as a fabio:JournalArticle, or alternatively as a fabio:ConferencePaper or a fabio:BookChapter. BIBO has the class bibo:AcademicArticle, which conflated the two concepts ResearchPaper (a conceptual Work) and JournalArticle (an Expression of that Work).
  2. Great care has been taken to make the textual definitions of FaBiO classes accurate and expressive. Thus, while BIBO’s definition of bibo:Standard is “A document describing a standard”, FaBiO describes fabio:TechnicalStandard, which is a subclass of fabio:Specification, as “An official or public specification of, or requirement for, a technical method, practice, process or protocol that is involved in, for example, manufacturing, computation, electronic communication, or digital media.”
  3. FaBiO is larger than BIBO (211 rather than 69 classes, 69 rather than 52 object properties, 45 rather than 54 data properties, and 16 rather than 15 individuals), permitting greater expressivity.
  4. BIBO has a slight subject specialism in the area of legal documents (including bibo:Bill, bibo:Brief, bibo:CourtReporter, bibo:LegalCaseDocument, bibo:Statue). In addition to covering conventional scholarly works, BIBO also covers a few things outside the immediate realm of bibliographic entities, including social events (e.g. bibo:Hearing, bibo:Interview, bibo:Performance) and personal roles (e.g. bibo:interviewer, bibo:performer, bibo:producer). In contrast, FaBiO was developed to describe anything a research scientist might need to reference. Thus it lacks legal classes but includes classes that betray a preoccupation with academia and publications (e.g. fabio:ExaminationPaper, fabio:Preprint, fabio:SupplementaryInformationFile), funding and IPR (e.g. fabio:GrantApplication, fabio:PatentApplication), biomedical research (e.g. fabio:ClinicalTrialDesign, fabio:SystematicReview), research data (e.g. fabio:Dataset, fabio:Metadata, fabio:DataBase, fabio:DataRepository), computing (e.g. fabio:Algorithm, fabio:ComputerProgram), web communication (e.g. fabio:BlogPost, fabio:WikiEntry) and ontologies (e.g. fabio:ControlledVocabulary, fabio:Ontology, fabio:Thesaurus, fabio:Taxonomy), none of which are present in BIBO.
  5. FaBiO is not a stand-alone ontology, but is one of the SPAR (Semantic Publishing and Referencing) Ontologies, a suite of complementary and orthogonal ontologies outlined in two previous blog posts [2, 3], that cover all aspects of publishing, referencing and bibliographic endeavour.
  6. FaBiO and the other SPAR ontologies are written in OWL 2 DL, enabling them to be used in the Semantic Web with appropriate reasoners. In contrast, BIBO is in OWL Full, since it includes the RDF resources rdf:List, rdf:Seq and rdfs:Resource and the property rdf:value that cannot be included in proper OWL2 DL ontologies.
  7. BIBO contains a few elements that are not present within FaBiO, but that have equivalent elements within some of the other SPAR ontologies:
    1. the object properties bibo:cites and bibo:isCitedBy have equivalent properties in CiTO, the Citation Typing Ontology, where they have 32 sub-properties lacking in BIBO;
    2. classes to describe component parts of documents (e.g. bibo:DocumentPart) that are covered in DoCO, the Document Components Ontology;
    3. classes and properties to describe the status of documents (e.g. bibo:DocumentStatus, bibo:status), that are covered in PSO, the Publication Status Ontology.
  8. FaBiO and CiTO have been harmonized [4] with the SWAN (Semantic Web Applications in Neuromedicine) ontology ecosystem developed by Tim Clark and Paolo Ciccarese of Harvard University, which covers the domain of scientific discourse in general, with particular application to neuromedicine, and which is used particularly useful for characterizing rhetorical structures between documents. Since SWAN had been previously harmonized with the SIOC (Semantically-Interlinked Online Communities) ontology for describing blogs, wikis and discussion groups, this provides the basis of a powerful new web framework for scientific communications.
  9. For categorising bibliographic resources, FaBiO has been integrated with and extends SKOS, the Simple Knowledge Organization System [5] that supports the use of thesauri, classification schemes, subject heading lists, taxonomies and other knowledge organization systems within the framework of the Semantic Web, as described in a previous blog post.

The choice of whether to use BIBO or FaBiO depends upon whether BIBO possesses sufficient expressiveness for the task in hand, whether the user is prepared to put in the small degree of extra effort required to engage with the layered FRBR structure of FaBiO rather than the flat structure of BIBO, whether one requires the semantic reasoning support of an OWL 2 DL ontology, and whether the user requires integration with other ontologies such as CiTO and SWAN, that FaBiO provides.

To assist existing users of BIBO and the SPAR Ontologies to map between these two systems, we have used SKOS to prepare an RDF mapping document, BIBO2SPAR, that we describe in the following blog post.

[1] Bruce D’Arcus and Frédérick Giasson (2009). Bibliographic Ontology Specification. Specification Document, 4 November 2009. http://bibliontology.com/specification.

[2] David Shotton (2010) Introducing the Semantic Publishing and Referencing (SPAR) Ontologies. JISC Open Citations Blog, 14 October 2010. https://opencitations.hypotheses.org/16.

[3] David Shotton (2011) New web site for the SPAR ontologies. JISC Open Citations Blog, 25 February 2011. https://opencitations.hypotheses.org/55.

[4] Ciccarese P, Shotton D, Peroni S and Clark T (2011) CiTO + SWAN: The Web Semantics of Bibliographic Records, Citations, Evidence and Discourse Relationships. (Submitted for publication).

[5] Miles, A., Bechhofer, S. (2009). SKOS Simple Knowledge Organization System Reference. W3C Recommendation, 18 August 2009. World Wide Web Consortium. http://www.w3.org/TR/2009/REC-skos-reference-20090818/.

Cite this article as: davidshotton, "Comparison of BIBO and FaBiO," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/109.

Categorising bibliographic resources with FaBiO and SKOS

One of the most important need for a publisher is to categorising each bibliographic entity it produces by adding free-text keywords and/or specific terms structured according to recognised classification systems and/or thesauri specific for certain academic disciplines. Academics have the same need when annotating bibliographic references.

SKOS, the Simple Knowledge Organization System [1], is an RDFS model to support the use of knowledge organization systems (KOS) such as thesauri, classification schemes, subject heading lists and taxonomies within the framework of the Semantic Web. The reception of this language has been particularly positive: a large number of well-known thesauri and classification systems, including the Medical Subject Headings (MeSH) and the Library of Congress Subject Headings (LCSH), have started to convert their respective specifications into SKOS documents. This makes SKOS the de facto standard for encoding controlled vocabularies for the Semantic Web, and it is in this role that we have employed it within FaBiO.

While, through FaBiO, the FRBR-aligned Bibliographic Ontology, the definition of keywords is possible using the PRISM property prism:keyword, terms from thesauri, structured vocabularies and classification systems are best described using SKOS. To facilitate this, FaBiO extends some classes and properties of SKOS as shown in the following Graffoo diagram.

The extensions to the common SKOS classes and relations implemented in FaBiO

As shown, any FRBR endeavour can be associated with one or more descriptive terms (fabio:hasSubjectTerm fabio:SubjectTerm, a sub-class of skos:Concept) found in a specific dictionary (fabio:TermDictionary, a sub-class of skos:ConceptScheme) that is relevant to one or more particular disciplines (fabio:hasDiscipline fabio:SubjectDiscipline, also a sub-class of skos:Concept) describing a field of knowledge or human activity such as Computer Science, Biology, Economics, Cookery or Swimming. These discipline names (fabio:SubjectDiscipline) are recorded within a discipline dictionary (fabio:DisciplineDictionary, also a sub-class of skos:ConceptScheme).

For example, “cell”, “mitochondrion” and “Cell Biology” are members (fabio:SubjectTerms) of the Cell Biology Term Dictionary, a particular fabio:TermDictionary. This Cell Biology Term Dictionary has a scientific subject discipline (fabio:hasSubjectDiscipline), revealed by its name, of “Cell Biology”. This subject discipline is a member of the class fabio:SubjectDiscipline, and is contained within a fabio:DisciplineDictionary entitled “Science Subject Dictionary”.

Of course, the granularity of application of such an information model is up to the user, since from the point of view of the cell biology specialist, “cell biology” is his subject discipline, whereas from the point of view of a librarian cataloguing books, “cell biology” might be a subject term.

The following RDF example (in Turtle) shows how these concepts can be used to enrich a bibliographic record:

@prefix skos: <http://www.w3.oeg/2004/02/skos/core#>
@prefix acm: <http://www.acm.org/class/1998/> .
@prefix dbpedia: <http://dbpedia.org/resource/> .
@prefix wiley: <http://onlinelibrary.wiley.com/doi/> .

:intertextual-semantics a fabio:ResearchPaper
; dcterms:title “Intertextual semantics: A semantics for information design”
; fabio:hasPortrayal wiley:10.1002/asi.21134/full
; prism:keywords “semantics of markup” , “semiotic application” , “xml”
; fabio:hasSubjectTerm acm:markup-languages , acm:semantics .

acm:markup-languages a fabio:SubjectTerm
; skos:prefLabel “Markup languages”
; skos:inScheme <http://www.acm.org/class/1998>
; skos:broader acm:document-preparation .


acm:semantics a fabio:SubjectTerm
; skos:prefLabel “Semantics”
; skos:inScheme <http://www.acm.org/class/1998>
; skos:broader acm:formal-definitions-and-theory .


<http://www.acm.org/class/1998> a fabio:TermDictionary
; skos:prefLabel “The 1998 ACM Computing Classification System”
; fabio:hasDiscipline dbpedia:Computer_science .

[1] Miles, A., Bechhofer, S. (2009). SKOS Simple Knowledge Organization System Reference. W3C Recommendation, 18 August 2009. World Wide Web Consortium. http://www.w3.org/TR/2009/REC-skos-reference-20090818/.

Cite this article as: davidshotton, "Categorising bibliographic resources with FaBiO and SKOS," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/101.

Extending FRBR within FaBiO

FRBR, the Functional Requirements for Bibliographic Records [1] is a general model, proposed by the International Federation of Library Association (IFLA), for describing bibliographic documents. It works for both physical and digital resources and has proved to be very flexible and powerful. One of the most important aspect of FRBR is the fact that it is not associated with a particular metadata schema or implementation. Its defining characteristic is that FRBR describes all documents from four different and correlated points of view, those of Work, Expression, Manifestation and Item, each of which is a FRBR Endeavour.

A Work is a distinct intellectual or artistic creation, an abstract concept recognised through its various expressions. An example of a Work is your latest research paper.

An Expression is the specific form that a Work takes each time it is ‘realized’ in physical or electronic form. For your latest research paper, Draft 5, the preprint, and the published version to which the publisher assigned a unique DOI (Digital Object Identifier), are all Expressions of the same work.

A Manifestation of an expression of a scholarly work defines its particular physical or electronic embodiment. If your latest research paper appeared as an article in a print journal, in the on-line version of that journal as an HTML page, and also as a downloadable PDF file, these are three separate manifestations of the same ‘version of record’ Expression of your work, all bearing the same DOI.

In FRBR, an Item is one single exemplar copy of a Manifestation, i.e. a physical or electronic object that can be owned by a person, for example a particular HTML file on a web server, a printed copy of a journal article on your desk, or a PDF file of that article that you purchased from a publisher and that now resides in digital form on your computer hard drive.

There now exists two different implementations of the core concepts of FRBR using standards that permit the encoding of proper semantics: the first, authored in 2005 by Richard Newman and Ian Davis, is FRBR Core in RDFS, while the second, developed from the first in 2010 by Paolo Ciccarese and Silvio Peroni, is FRBR Core in OWL 2 DL.

FaBiO, the FRBR-aligned Bibliographic Ontology, previously described in this blog post, is an ontology for recording and publishing on the Semantic Web bibliographic records of scholarly endeavours that is structured according to the FRBR model of Works, Expressions, Manifestations and Items.

One of the explicit requests from publishers and end-users of FaBiO was to create shortcuts between particular combinations of FRBR endeavours C:\Documents and Settings\David Shotton\My Documents\Papers and books\Peroni paper CiTO and FaBiO\citofabio_01.odt\iml: – IFLAStudyGroupFRBR(between Work and Manifestation, between Work and Item, and between Expression and Item), since these were not part of the original FRBR model.

Let us introduce a bibliographic reference to illustrate this need:

Yves Marcoux, Élias Rizkallah (2009). Intertextual semantics: A semantics for information design. http://onlinelibrary.wiley.com/doi/10.1002/asi.21134/full.

In this reference, we have a FRBR work – the paper by Marcoux and Rizkallah – and the URL for a specific FRBR item that portrays that work, an HTML file containing the paper on the publisher’s web site. If we wished to link these concepts simply using the FRBR OWL ontology terms, we would be obliged to specify each intermediate FRBR endeavour, namely the expression and manifestation of that paper, even if we were not interested in doing that:

 @prefix wiley: <http://onlinelibrary.wiley.com/doi/> .

:intertextual-semantics a frbr:Work
; frbr:creator :marcoux , :rizkallah
; dcterms:title “Intertextual semantics: A semantics for information design”
; frbr:realization [ a frbr:Expression
; frbr:embodiment [ a frbr:Manifestation
; frbr:exemplar wiley:10.1002/asi.21134/full ] ] .

 In order to avoid this kind of verbosity, we have supplemented the existing FRBR object properties with three new FaBiO properties shown in the following figure: fabio:hasManifestation that links a Work directly to its Manifestations, fabio:hasPortrayal that links a Work directly to its Items), and fabio:hasRepresentation that links an Expression directly to its Items.


A Graffoo diagram showing the FRBR Core data model, supplemented with three new FaBiO properties.

Note: In Manchester Syntax, the “o” in each sub-property chain is the syntax mechanism for chaining properties.

These added properties allow us to treat the previous example in a less verbose way:

@prefix wiley: <http://onlinelibrary.wiley.com/doi/> .

:intertextual-semantics a frbr:Work
; frbr:creator :marcoux , :rizkallah
; dcterms:title “Intertextual semantics: A semantics for information design”
; fabio:hasPortrayal wiley:10.1002/asi.21134/full .

[1] IFLA Study Group on the Functional Requirements for Bibliographic Records (2009). Functional Requirements for Bibliographic Records Final Report. International Federation of Library Associations and Institutions. http://www.ifla.org/en/publications/functional-requirements-for-bibliographic-records.

Cite this article as: davidshotton, "Extending FRBR within FaBiO," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/95.

Functional clustering of CiTO properties

CiTO v2.0 contains just two main object properties, cito:cites and its inverse cito:isCitedBy, each of which as thirty-two sub-properties. Intentionally, these properties are not constrained as to domain or range, thereby maximising their applicability in a wide range of citation contexts. Additionally, CiTO contains one additional generic object property, cito:shareAuthorsWith, that may be used even outside a citation context.

Some have criticised CiTO for having too many properties, making it confusing for potential users, and Martin Fenner chose to use only the ten ‘most popular’ properties in his CiTO plug-in for WordPress, previously mentioned in this blog post. In response, I would point out that each property has a distinct and clearly defined meaning, and that together they provide an appropriate level of expressivity for effective use. Nevertheless, it is clearly difficult to conceptualize all the CiTO properties at one time, if they are being viewed using an ontology editor such as Protégé. I have thus created the following figure that groups CiTO properties by similarity, in the hope that this will facilitate choice of the most appropriate one.

Clustering of CiTO relationships by similarity

As shown in the figure, the CiTO properties and sub-properties (and, consequently, their inverses) may be classified as rhetorical (upper oval) and/or factual (lower oval, dark blue text), with the rhetorical properties being grouped in three sets depending on their connotation: positive (green), neutral or informative (blue) and negative (red). Five properties (in purple, within the overlap of the ovals) have both factual and rhetorical characteristics. The inverse properties are not shown.

Cite this article as: davidshotton, "Functional clustering of CiTO properties," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/91.

Advantages of Ontological Standards in Scholarly Publishing

Scholarly authoring and publishing is in the throes of a revolution, as the full potential of on-line publishing is explored. Semantic publishing is one aspect of this, namely
the use of web and semantic web technologies to enhance a published document such as a journal article so as to enrich its meaning, facilitate its automatic discovery, enable its linking to semantically related articles, provide access to data within the article in actionable form, and allow integration of data between papers [1].

As confirmed by a number of recent initiatives – including the Elsevier Grand Challenge and its Executable Paper Challenge, the Beyond The PDF Workshop, the Beyond Impact Workshop, the First International Workshop about Semantic Publication (SePublica 2011), the upcoming Schloss Dagstuhl Perspectives Workshop entitled The Future of Research Communication, and the Special Issue of the Semantic Web Journal on New Models of Semantic Publishing in Science, which is open for submissions until 15 September 2011 – semantic publishing and scholarly citation using web standards are presently two of the most interesting topics within the scientific publishing domain.

Yet, to date, publishers have not adopted web standards for their day-to-day work, but rather employ a variety of proprietary XML-based informational models and document type definitions (DTDs). While such independence was reasonable in the pre-web world of paper publishing, it now appears anachronistic, since publications and their metadata are incompatible, requiring hand-crafted mappings to convert from one to another. For a large community such as publishers, this lack of standard definitions that could be adopted and reused across the entire industry represents losses in terms of money, time and effort.

In contrast, modern web information management techniques employ standards such as RDF and OWL2 to encode information in ways that permit computers to query metadata and integrate web-based information from multiple resources in an automated manner. Since the processes of scholarly communication are central to the practice of science, it is essential that publishers now adopt such standards to permit inference over the entire corpus of scholarly communication represented in journals, books and conference proceedings.

The SPAR (Semantic Publishing and Referencing) Ontologies (http://purl.org/spar/), introduced in previous blog posts [2, 3], are specially tailored to the requirements of authors, publishers and their readers, and form key components of this semantic publishing revolution, as do the tools LODE [4] and Graffoo [5] that permit these (and other) ontologies to be documented and visualized in complementary ways. We commend their use to the scholarly publishing community.

[1] David Shotton, Katie Portwin, Graham Klyne, Alistair Miles David Shotton (2009) Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article. PLoS Computational Biology 5(4): e1000361. doi:10.1371/journal.pcbi.1000361.

[2] David Shotton (2010) Introducing the Semantic Publishing and Referencing (SPAR) Ontologies. JISC Open Citations Blog, 14 October 2010. http://opencitations.wordpress.com/2010/10/14/introducing-the-semantic-publishing-and-referencing-spar-ontologies/.

[3] David Shotton (2011) New web site for the SPAR ontologies. JISC Open Citations Blog, 25 February 2011. http://opencitations.wordpress.com/2011/02/25/new-web-site-for-the-spar-ontologies/.

[4] David Shotton (2011) Using LODE for ontology visualization. JISC Open Citations Blog, 25 February 2011. http://opencitations.wordpress.com/2011/02/25/using-lode-for-ontology-visualization/

[5] David Shotton (2011) Graffoo, a Graphical Framework for OWL Ontologies. JISC Open Citations Blog, 29 June 2011. http://opencitations.wordpress.com/2011/06/29/graffoo-a-graphical-framework-for-owl-ontologies/.

Cite this article as: davidshotton, "Advantages of Ontological Standards in Scholarly Publishing," in OpenCitations blog, 29/06/2011, https://opencitations.hypotheses.org/87.
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