Performing live time-traversal queries on RDF datasets

Guest post by Arcangelo Massari, University of Bologna

In this post, Arcangelo Massari, who recently graduated in Digital Humanities and Digital Knowledge under Professor Silvio Peroni at the University of Bologna, shares the results of his master thesis.

A particular problem in information retrieval is that of obtaining data from an evolving dataset, independent of the time at which that item of data was added, changed or removed. To permit such time-independent queries to be performed over evolving RDF datasets, I have developed two new pieces of open source software, time-agnostic-library [1] and time-agnostic-browser [2], that are now available from the OpenCitations GitHub repository.

The time-agnostic-library is a Python library to perform live time-traversal queries on RDF datasets. Time-traversal means being agnostic about time: a SPARQL query that is not run on the current state of the collection but over its entire history or over a specified timespan of that history [3]. This tool allows materializations – obtaining all versions of an entity over time, or its status at a given time. Furthermore, SPARQL queries can be performed to get the delta between two or more versions of one or more resources. Thereby, the time-agnostic-library realizes all the retrieval functionalities described in the taxonomy by Fernández et al. [3].

To complement this query software, the time-agnostic-browser is a web application built on top of the time-agnostic-library to achieve the same results via a graphical user interface.

The primary purpose of these developments is to offer a system for browsing the provenance [4] of RDF statements across time: who produced them, when, where the information was taken from, and what changes were made compared to the previous state of the resource. Knowledge of such information is essential because data changes over time, either because of the natural evolution of concepts or due to the correction of mistakes. Indeed, the latest version of knowledge may not be the most accurate. Such phenomena are particularly tangible in the Web of Data, as highlighted in a study by the Dynamic Linked Data Observatory, which noted the modification of about 38% of the nearly 90,000 RDF documents monitored for 29 weeks, and the permanent disappearance of 5% of them [5] (Figure 1).

Figure 1. Donut chart showing the results of the study conducted by the Dynamic Linked Data Observatory on the evolution of RDF documents [5].

Additionally, the truthfulness of data cannot be assessed without provenance records and a system to query them. In fact, the truth value of an assertion on the Web is never absolute, as demonstrated by Wikipedia, which in its official policy on the subject states: “The threshold for inclusion in Wikipedia is verifiability, not truth.” [6]. The Semantic Web does not alter that condition, and trustworthiness has to be evaluated by each application by probing the context of the statements [7]. It is a challenging task and thus, in the Semantic Web Stack, trust is the highest and most complex level to satisfy, subsuming all the previous ones (Figure 2).

Figure 2.The Semantic Web layers [7]. Trust is the uppermost level of the stack, subsuming all the others.

Notwithstanding these premises, at present the most extensive RDF datasets – DBPedia [8], Wikidata [9], Yago [10], and the Dynamic Linked Data Observatory [11] – do not use RDF to track changes and record the provenance of such changes. Instead, they all adopt backup-based archiving policies. Some of them, such as Yago 4, record provenance but not changes. As far as citation databases are concerned, OpenCitations is the only infrastructure to implement change-tracking mechanisms and to record full RDF provenance records for each data entity. Among the leading players in this field, neither Web of Science nor Scopus have adopted similar solutions.

In accordance with the OpenCitations Data Model (OCDM) [12], a provenance snapshot is generated by OpenCitations every time a bibliographical entity is created or modified. Each snapshot (prov:Entity) records the responsible agent (prov:wasAttributedTo), the generation time (prov:generatedAtTime), the invalidation time (prov:invalidatedAtTime), the primary source (prov:hadPrimarySource), and a link to the previous snapshot (prov:wasDerivedFrom), using terms from the Provenance Ontology. In addition, OCDM introduced a system to simplify restoring an entity’s status at a given time, by saving the delta between two versions as a SPARQL update query (prov:hasUpdateQuery) [13] (Figure 3). This approach enables one to restore an entity to a specific timepoint (snapshot) in a straightforward way by applying the inverse operations, i.e., deletions instead of additions, etc.

Figure 3. Provenance in the OpenCitations Data Model.

This solution is concretely used in all the datasets related to the OpenCitations infrastructure, such as COCI, an open index containing almost 1.2 billion DOI-to-DOI citation links derived from the open reference data available in Crossref [14]. It is important to note that this OpenCitations provenance model is generic and reusable in any other context. Since the time-agnostic-library leverages OCDM, it too is generic and can be used for any RDF dataset that tracks changes and provenance as OpenCitations does.

The time-agnostic-library is released under the ISC license and is downloadable through pip [1]. Test-driven development was adopted as a software development process during its creation [15]. It makes three main classes available to the user: AgnosticEntity, VersionQuery, and DeltaQuery, for materializations, version queries, and delta queries, respectively (Listing 1).

Listing 1. Code template to achieve materializations, time-traversal queries, and delta queries.

All three operations can be performed over the entire available history of the dataset, or by specifying a time interval via a tuple in the form (START, END).

The time-agnostic-browser [2] is also released under the ISC license and can be run as a Flask application. It is organized into two macro-sections: “Explore” and “Query”. In the former, a text input accepts a URI. By submitting it, the entire history of the corresponding resource is displayed. In the latter, a text area receives a SPARQL query, which is resolved on all dataset states. Its main added value is hiding the triples and the complexity of the underlying RDF model: predicate URIs, as well as subjects and objects, appear in a human-readable format. Moreover, all the entities are displayed as links, providing shortcuts to reconstruct the history of the related resources (Figure 4).

Figure 4. Graphical user interface of an entity history reconstruction through the time-agnostic-browser.

The efficiency of time-agnostic-library was measured with two types of benchmarks [16], one on execution times and the other on the amount of computer memory (RAM) required by ten different use cases, each repeated ten times to produce significant results and avoid outliers. In light of these benchmarks, time-agnostic-library has proven effective for any materialization. Regarding structured queries, they are swift if all subjects are known or deductible. On the other hand, the presence of unknown subjects in the user’s SPARQL query involves the identification of all present and past entities that satisfy that pattern, and so requires a more significant amount of time and resources. Specifically, all materializations and the cross-version structured query with known subjects required about half a second and about 50 MB of RAM; conversely, with unknown subjects, 581 seconds and 519 MB of RAM are required. It can be concluded that the proposed software can be used effectively in all cases where the subject is known, that is, for any materialization or formulated SPARQL queries without isolated triple patterns containing unknown subjects.

Other software solutions for such problems have been proposed. Table 1 shows the list of available software to perform materializations and time-traversal queries on RDF datasets. As can be observed, time-agnostic-library is the only one to support all retrieval functionalities without requiring pre-indexing processes. This feature makes it particularly suitable for use in scenarios with large amounts of data that often change over time. Moreover, compared to the approach of Im, Lee and Kim [17] and OSTRICH [18], the OpenCitations Data Model only requires storing the current state of the dataset, rather than the original one, allowing one to query the latest version, without additional computational effort to first re-create the original version.

SoftwareVersion materializationDelta materializationSingle-version structured queryCross-version structured querySingle-delta structured queryCross-delta structured queryLive
PromptDiff [19]+++
SemVersion [20]+++
Im, Lee, & Kim, 2012 [17]+++++
R&Wbase [21]++++
x-RDF-3X [22]+++
v-RDFCSA [23]++++++
OSTRICH [18]+++
Tanon & Suchanek, 2019 [24]++++++
Table 1. Comparative between time-agnostic-library and preexisting software to achieve materializations and time traversal queries on RDF datasets. (Scroll right to see Columns 6-8).

The OpenCitations Data Model and the time-agnostic-library software are the pre-requisites that will allow OpenCitations to involve third parties, for example members of staff in academic libraries, in the submission, curation and updating of OpenCitations bibliographic and citation data. At this stage, all entities in COCI have a single snapshot — the one made at the time of creation. However, since these entities may become modified, corrected or enriched over time, it is imperative to have appropriate software tools available for use by curators. With the time-agnostic-library software and its associated time-agnostic-browser, it will be possible for a curator to explore the entire history of the changes within an RDF dataset, to know when they were made, based on which source, and by which responsible agent, thus ensuring the reliability and verifiability of data, and facilitating any necessary further changes.


[1] A. Massari, time-agnostic-library. 2021. Available:

[2] A. Massari, time-agnostic-browser. 2021. Available:

[3] J. D. Fernández, A. Polleres, and J. Umbrich, ‘Towards Efficient Archiving of Dynamic Linked’, in DIACRON@ESWC, Portorož, Slovenia: Computer Science, 2015, pp. 34–49.

[4] December, ‘Provenance XG Final Report’. 2010. Available:

[5] T. Käfer, A. Abdelrahman, J. Umbrich, P. O’Byrne, and A. Hogan, ‘Observing Linked Data Dynamics’, in The Semantic Web: Semantics and Big Data, vol. 7882, P. Cimiano, O. Corcho, V. Presutti, L. Hollink, and S. Rudolph, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 213–227. doi: 10.1007/978-3-642-38288-8_15

[6] S. L. Garfinkel, ‘Wikipedia and the Meaning of Truth’, MIT Technology Review, 2008, [Online]. Available:

[7] M.-R. Koivunen and E. Miller, ‘Semantic Web Activity’, W3C, Nov. 02, 2001.

[8] F. Orlandi and A. Passant, ‘Modelling provenance of DBpedia resources using Wikipedia contributions’, Journal of Web Semantics, vol. 9, no. 2, pp. 149–164, Jul. 2011, doi: 10.1016/j.websem.2011.03.002.

[9] P. Dooley and B. Božić, ‘Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags’, in Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, Munich Germany, Dec. 2019, pp. 166–175. doi: 10.1145/3366030.3366048.

[10] Yago Project, ‘Download data, code, and logo of Yago projects’, Yago, 2021. (accessed Sep. 24, 2021).

[11] J. Umbrich, M. Hausenblas, A. Hogan, A. Polleres, and S. Decker, ‘Towards Dataset Dynamics: Change Frequency of Linked Open Data Sources’, in Proceedings of the WWW2010 Workshop on Linked Data on the Web, Raleigh, USA, 2010. Available:

[12] M. Daquino, S. Peroni, and D. Shotton, ‘The OpenCitations Data Model’, p. 836876 Bytes, 2020, doi: 10.6084/M9.FIGSHARE.3443876.V7.

[13] S. Peroni, D. Shotton, and F. Vitali, ‘A Document-inspired Way for Tracking Changes of RDF Data’, in Detection, Representation and Management of Concept Drift in Linked Open Data, Bologna, 2016, pp. 26–33. Available:

[14] I. Heibi, S. Peroni, and D. Shotton, ‘Software review: COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations’, Scientometrics, vol. 121, no. 2, pp. 1213–1228, Nov. 2019, doi: 10.1007/s11192-019-03217-6.

[15] K. Beck, Test-driven development: by example. Boston: Addison-Wesley, 2003.

[16] A. Massari, ‘time-agnostic-library: benchmark results on execution times and RAM’. Zenodo, Oct. 05, 2021. doi: 10.5281/ZENODO.5549648.

[17] D.-H. Im, S.-W. Lee, and H.-J. Kim, ‘A Version Management Framework for RDF Triple Stores’, Int. J. Softw. Eng. Knowl. Eng., vol. 22, pp. 85–106, 2012.

[18] R. Taelman, M. V. Sande, and R. Verborgh, ‘OSTRICH: Versioned Random-Access Triple Store’, in Companion Proceedings of the Web Conference 2018, 2018, pp. 127–130. Available:

[19] N. F. Noy and M. A. Musen, ‘Promptdiff: A Fixed-Point Algorithm for Comparing Ontology Versions’, in Proc. of IAAI, 2002, pp. 744–750.

[20] M. Völkel, W. Winkler, Y. Sure, S. Kruk, and M. Synak, ‘SemVersion: A Versioning System for RDF and Ontologies’, 2005.

[21] M. V. Sande, P. Colpaert, R. Verborgh, S. Coppens, E. Mannens, and R. V. Walle, ‘R&Wbase: Git for triples’, 2013.

[22] T. Neumann and G. Weikum, ‘x-RDF-3X: Fast Querying, High Update Rates, and Consistency for RDF Databases’, Proceedings of the VLDB Endowment, vol. 3, pp. 256–263, 2010.

[23] A. Cerdeira-Pena, A. Farina, J. D. Fernandez, and M. A. Martinez-Prieto, ‘Self-Indexing RDF Archives’, in 2016 Data Compression Conference (DCC), Snowbird, UT, USA, Mar. 2016, pp. 526–535. doi: 10.1109/DCC.2016.40.

[24] T. Pellissier Tanon and F. Suchanek, ‘Querying the Edit History of Wikidata’, in The Semantic Web: ESWC 2019 Satellite Events, vol. 11762, P. Hitzler, S. Kirrane, O. Hartig, V. de Boer, M.-E. Vidal, M. Maleshkova, S. Schlobach, K. Hammar, N. Lasierra, S. Stadtmüller, K. Hose, and R. Verborgh, Eds. Cham: Springer International Publishing, 2019, pp. 161–166. doi:

Cite this article as: Chiara Di Giambattista, "Performing live time-traversal queries on RDF datasets," in OpenCitations blog, 29/11/2021,

OpenCitations described

OpenCitations is an infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data. We at OpenCitations are proud to announce the publication, in the first issue of Quantitative Science Studies, of a canonical paper in which we introduce and describe OpenCitations and outline its achievements and goals [1].

Here, I outline the contents of our paper, and provide definitive links on the topics described. Many of these topics have been the subjects of earlier blog posts.

This paper appears in the first Special Issue of QSS, dedicated to the description of the bibliometric data sources that lie at the heart of scientometric research, which aims to characterize the most important data sources currently available and to show how they differ in various dimensions, for instance in the data they provide, their level of openness, and their support for making research reproducible. The first three papers in this special issue cover the most important commercial bibliographic data sources: Web of Science (Clarivate Analytics), Scopus (Elsevier), and Dimensions (Digital Science), while the remaining three articles describe open data sources: Microsoft Academic, Crossref and OpenCitations.

In the introduction to our own paper, we describe the origins of OpenCitations, discuss the growth and benefits of open science, and introduce the Semantic Web techniques used at OpenCitations for recording and publishing our data. We then go on to describe OpenCitations’ services and data, namely Open Citation Identifiers, the OpenCitations Data Model, the SPAR (Semantic Publishing and Referencing) Ontologies, the OpenCitations Corpus, and the OpenCitations Indexes of citation data, of which the first and largest is COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, that currently holds information on over 624 million citations. We conclude our survey of OpenCitations’ services and data by outlining the generic open source software developed at OpenCitations, including OSCAR, the OpenCitations RDF Search Application for searching over RDF datasets, LUCINDA, OSCAR’s associated OpenCitations RDF Resource Browser, and RAMOSE, OpenCitations’ application for creating REST APIs over SPARQL endpoints, thus opening Semantic Web datasets to those not familiar with SPARQL, the RDF query language.

In the second half of the paper, we describe OpenCitations as an organization in terms of its compliance with the principles for the sustainability of open infrastructures proposed by Bilder, Lin and Neylon (2015) [2], and report the selection of OpenCitations by the Global Sustainability Coalition for Open Science Services (SCOSS) as an open infrastructure organization worthy of crowd-funding support by the stakeholder community. We then provide usage statistics for our datasets and web site, and describe the adoption of OpenCitations data and services by the community, before concluding with a forward look at our proposed developments of OpenCitations activities.


[1] Silvio Peroni and David Shotton (2020). OpenCitations, an infrastructure organization for open scholarship. Quantitative Science Studies 1 (1): 428-444.

[2] Geoffrey Bilder, Jennifer Lin and Cameron Neylon (2015). Principles for open scholarly infrastructures. Figshare.

Cite this article as: davidshotton, "OpenCitations described," in OpenCitations blog, 26/02/2020,

Introducing InTRePIDs – In-Text Reference Pointer Identifiers


Readers of this blog will be familiar with Open Citation Identifiers (OCIs), described in an earlier post and formally defined in [1]. OCIs enable bibliographic citations, treated as first class information entities, to be uniquely identified and referenced, and are used to identify the >624 million individual citations indexed in the latest release of COCI, the OpenCitations Index of Crossref open DOI-to-DOI citations, as described in a recent post.

However, COCI and similar citation indexes do not provide any information about where within the citing paper a citation is generated, the textual contexts of the in-text reference pointers, or the reasons for including different in-text reference pointers denoting the same reference at different points within the text.

As explained in the preceding post describing the Open Biomedical Citations in Context Corpus funded by the Wellcome Trust and under development by OpenCitations, deep citation analysis requires a more nuanced approach to citations, which acknowledges that each in-text reference pointer that denotes a bibliographic reference in the reference list of a citing publication instantiates its own citation, as shown in Figure 1.

Figure 1. Citations between a citing paper and a cited paper instantiated both by the inclusion of a bibliographic reference within the reference list of the citing paper and by the inclusion within the text of the citing paper of one or more in-text reference pointers denoting that reference.

The pointer citations clearly involve the same cited publication as does the reference citation itself, but each has its own unique characteristics: the location and textual context of its in-text reference pointer within the text of the citing publication, and its particular rhetorical function which is determined by that context.

If the reference citation is open (as defined in [2]) and identified by an OCI, each in-text reference pointer related to that citation can be identified uniquely using an In-Text Reference Pointer Identifier (InTRePID).

InTRePIDs facilitate in-depth scholarship on in-text reference pointer locations and citation functions, and fine-grained analysis of the relationships between publications, by making it possible

  • to identify each in-text reference pointer with a unique PID,
  • to distinguish references that are cited only once from those that are cited multiple times,
  • to see which references are cited together (e.g. in the same sentence or within an in-text reference pointer list),
  • to determine from which section(s) of the article references are cited (e.g. Introduction, Methods, Discussion), and, potentially,
  • to determine the rhetorical function of the citations from analysis of their textual contexts, by the application of natural language processing, machine learning and artificial intelligence techniques to conduct sentiment analysis on the citation contexts.

Definition of an InTRePID

An InTRePID is composed of two parts separated by an oblique stroke



  • <oci-numerals> is the numerical part of the OCI uniquely identifying the particular open citation to which the in-text reference pointer and its denoted bibliographic reference relate. Thus an InTRePID can be assigned for any in-text reference pointer that relates to an open citation for which a valid OCI has been assigned;
  • <ordinal> identifies the nth occurrence of an in-text reference pointer within the text of the citing paper relating to that citation; and
  • <total> defines the total number of in-text reference pointers denoting that bibliographic reference within the citing paper.

For example, intrepid:070433-070475/46 is a valid InTRePID for an in-text reference pointer defined within the OpenCitations Citations in Context Corpus.

A formal definition document for the InTRePID is given in [3].

Exemplar in-text reference pointers

Consider the following citing paper:

Zou, J. et al. (2020). Phenotypic and genotypic correlates of penicillin susceptibility in nontoxigenic Corynebacterium diphtheriae, British Columbia, Canada, 2015–2018. Emerging Infectious Diseases, 26: 97-103.

This paper contains six in-text reference pointers denoting Reference 13 in the reference list:

13. Lowe, C. et al. (2011). Cutaneous diphtheria in the urban poor population of Vancouver, British Columbia, Canada: a 10-year review. J. Clinical Microbiology 49: 2664-2666.

The InTRePIDs for these pointers are recorded within the OpenCitations Biomedical Citations in Context Corpus, together with the corpus identifiers and DOIs of the citing and cited papers, as shown in the excerpt presented in Figure 2.

Figure 2. An excerpt from the OpenCitations Biomedical Citations in Context Corpus, showing highlighted the InTRePIDs for the six in-text reference pointers within Zou, J. et al. (2020) denoting Reference 13, the reference to Lowe, C. et al. (2011), together with the internal corpus identifiers for each in-text reference pointer, and the corpus identifiers and DOIs for the citing and cited papers.

Of these six in-text reference pointers, having InTRePIDs intrepid:070433-070475/1-6 to intrepid:070433-070475/6-6, the first and the fourth of these, together with their document locations, their embedding sentences, their in-text reference pointer lists, and their InTRePIDs, chosen as examples, are as follows:

Introduction. “Nontoxigenic strains have been shown to have epidemic potential, causing infections in persons afflicted by homelessness, alcohol abuse, and injection drug use (9,13–15).” (intrepid:070433-070475/1-6)

Discussion. “We also noted ST5 and ST32 in our review from downtown Vancouver during 1998–2007 (13).” (intrepid:070433-070475/4-6)

The first of these discusses those people most susceptible to diphtheria infection, while the other discusses which multilocus sequence types (STs) of C. diphtheriae were found, thus relating to the organism causing the infection rather than to the infected individuals. The rhetorical function of these two in-text reference pointers is quite distinct.

To permit this information to be recorded within the OpenCitations Citations in Context Corpus, extensions were required to the OpenCitations Data Model, a new extended version of which was recently published [4], as described in a related blog post.

The OpenCitations InTRePID Resolution Service

To support the use of InTRePIDs to identify in-text reference pointers, OpenCitations has recently developed an InTRePID Resolution Service (currently in ‘beta’ in its development cycle), which is running at A screenshot of this service is shown in Figure 3.

Figure 3. A screenshot of the user interface of the InTRePID Resolution Service.

In addition to using the Web user interface shown in Figure 3, InTRePIDs can be entered into this resolution service in the form of resolvable URIs, e.g.

As shown in Figure 4, the OpenCitations InTRePID Resolution service returns metadata concerning the in-text reference pointer identified by the InTRePID, and the bibliographic reference that it denotes, from which further information about the citation and the citing and cited publications may be obtained by following the links provided.

Figure 4. A screenshot of the Web page displaying metadata returned by the InTRePID Resolution Service.

Note that as well as rendering this information in HTML on a web page, the resolution service can also provide it in a variety of machine-readable formats.


InTRePIDs, which enable the identification of individual in-text reference pointers, and the InTRePID Resolution Service, are new services from OpenCitations that will facilitate scholarship on the textual contexts and rhetorical functions of such in-text reference pointers, and of the citations that they instantiate.

InTRePIDs were first announced on 30th January 2020 at PIDapalooza 2020 in Lisbon, the Open Festival of Persistent Identifiers.


[1] Silvio Peroni and David Shotton (2019): Open Citation Identifier: Definition. Figshare.

[2] Silvio Peroni and David Shotton (2018). Open Citation: Definition. Figshare.

[3] David Shotton, Marilena Daquino and Silvio Peroni (2020). In-Text Reference Pointer Identifier: Definition. Figshare.

[4] Marilena Daquino, Silvio Peroni and David Shotton (2019). The OpenCitations Data Model. Version 2.0. Figshare.

Cite this article as: davidshotton, "Introducing InTRePIDs – In-Text Reference Pointer Identifiers," in OpenCitations blog, 30/01/2020,

‘Likes’ joins the semantic web: cito:likes

A ‘like’ button is a well-known feature in communication software such as social networking services, Internet forums, news websites and blogs that permits a user to indicate that he/she likes, enjoys or supports certain content.  Internet services that feature ‘like’ buttons usually also display the number of users who have expressed that they ‘like’ a particular item of content, providing a quantitative estimate of the strength of support for it.

In particular, the ‘Like’ button is one of Facebook’s social plug-ins, which can be use on websites outside Facebook as part of Facebook’s Open Graph.  It is valued by advertisers who wish to attract ‘likes’ for their products (and who pay Facebook for the privilege), but its use has aroused privacy concerns because it permits Facebook to track visitors to participating sites, even if they not Facebook users, giving Facebook a vast amount of information about who visits which sites.

Like it or not, however, this form of social communication has now become an integral feature of online social interactions.  For this reason, we thought it would be worthwhile to enable encoding of such ‘likes’ as open linked data, in the form of a new object property in CiTO, the Citation Typing Ontology.

This new property, cito:likes, has the following definition:

“A property that permits you to express appreciation of or interest in something, or to express that it is worth thinking about even if you do not agree with its content, enabling social media ‘likes’ statements to be encoded in RDF.  Use of this property does NOT imply the existence of a formal citation of the entity that is ‘liked’.”

An exemplar usage of cito:likes (in Turtle format) is:

sioc: <> .
foaf: <> .
today: <> .

today:cito-likes a sioc:Post ;
	sioc:has_creator [ 
		a sioc:UserAccount ; 
		sioc:account_of [ 
			a foaf:Person ;
			foaf:givenName "David" ;
			foaf:familyName "Shotton" ] ] .

<> a sioc:UserAccount ;
	sioc:account_of <>;
	cito:likes today:cito-likes .

To our surprise, we found that existing ontologies did not include such a property – a search in the excellent new LOV (Linked Open Vocabularies) service revealed that no other open ontology contains the same concept as is now represented by cito:likes.

The Trait Ontology has trait:likes, but this object property has a gender-related domain, and its definition indicates that its usage is designed for expressing sexual fetish preferences. at first sight appears to have something resembling cito:likes, but inspection of schema:UserLike reveals this use to be specific for events.

Even the SIOC ontology, a product of the SIOC initiative (Semantically-Interlinked Online Communities) aimed at enabling the integration of online community information, which is described in an award-winning paper from DERI [1], lacks the concept ‘likes’.

So here we offer cito:likes, a property (like all other cito properties) without domain or range constraints, permitting it to be used in a wide variety of situations.

Like it? Click the Like button below!

David Shotton
Silvio Peroni


[1]     John G. Breslin, Andreas Harth, Uldis Bojars, and Stefan Decker (2005). Towards Semantically-Interlinked Online Communities.  In Proc. ESWC 2005 (A. Gómez-Pérez and J. Euzenat, Eds.); Lecture Notes in Computer Science 3532, pp. 500–514.  doi:10.1007/11431053_34.  Available from

Cite this article as: Silvio Peroni, "‘Likes’ joins the semantic web: cito:likes," in OpenCitations blog, 13/07/2012,

Science joins Nature in opening reference citations

Hot on the heels of my announcement on Monday that Nature Publishing Group has agreed to open its articles’ reference lists, I am delighted to announce that Science Magazine, the American Association for the Advancement of Science’s premier global science weekly, together with the other AAAS journals Science Signalling and Science Translational Medicine, will also open their articles’ reference lists, and contribute the bibliographic citations contained within these lists as open linked data to an expanded Open Citations Corpus, where they will be freely available for everyone to use in whatever manner they choose.

Preparations to expand the corpus in this manner, by integration with the reference processing pipeline of the CrossRef Cited-By Linking service, will be undertaken over the last six months of this year, and incorporation of the references from the AAAS journals into the expanded Open Citations Corpus is planned to commence in the first half of 2013.

Now that the world’s two most presigeous scientific journals have declared their hands, it is time for other subscription-access publishers to step up and join them, for the reasons given in my earlier post.  I am presently in discussion with a small number of other major STM publishers, and hope to be able to make further announcements in the near future.  However, there are many other publishers with whom I have not yet been able to make personal contact.  Those who would also like to become ‘early adopters’ of the Open Citations publishing model should contact me at <>.

Cite this article as: davidshotton, "Science joins Nature in opening reference citations," in OpenCitations blog, 16/06/2012,

Access to Citation Data

The JISC, in response to its invitation to tender, has recently funded Curtis+Cartwright Consulting Ltd, a research and strategy consultancy, to undertake an independent study entitled Access to Citation Data: A Cost-Benefit and Risk Review and Forward Look.  Evidence gathering for the study has just started, and the consultants are due to produce a report on this subject by next February.  This report will be one of a series of high profile reports that the JISC is commissioning to inform the UK FE/HE sector on key issues relating to digital infrastructure.

The JISC has invited me to serve on the steering group for this study, which will in particular be reviewing the Open Citations Corpus, both to provide intelligence and to act as a case study and as a potential recommended corpus of citation data.

I look forward to working with the review team, starting later this month, and anticipate that we will benefit from this investigation as well as contribute to it, since we anticipate that Curtis+Cartwright will be able to advise on sustainable business models for such open access citation data corpora.

Cite this article as: davidshotton, "Access to Citation Data," in OpenCitations blog, 12/06/2012,

Nature to open its reference list data

Bibliographic references are the links that knit together independent scholarly endeavours.  I am thus delighted to announce that Nature Publishing Group, publisher of Nature, Nature Genetics and many other leading journals, has agreed to open its articles’ reference lists, initially for a selected number of NPG journals, and contribute the bibliographic citations contained in these lists as open linked data to an expanded Open Citations Corpus, where they will be freely available for everyone to use in whatever manner they choose.  Preparations to expand the corpus in this manner, by integration with the reference processing pipeline of the CrossRef Cited-By Linking service, will be undertaken over the next six months of this year, and incorporation of the references from the selected NPG journals into the expanded Open Citations Corpus is planned to commence in the first half of 2013.

As the first subscription-access publisher to opening its reference lists in this way, Nature Publishing Group is further demonstrating its commitment to ‘lead from the front’ in its embrace of new semantic publishing technologies.  Only two months ago, this publisher announced its decision to open up the bibliographic records of its journal articles as open linked data.  On 4th April, NPG’s Linked Data Platform press release read:

“Nature Publishing Group (NPG) today is pleased to join the linked data community by opening up access to its publication data via a linked data platform. NPG’s Linked Data Platform is available at      The platform includes more than 20 million Resource Description Framework (RDF) statements, including primary metadata for more than 450,000 articles published by NPG since 1869. In this first release, the datasets include basic bibliographic information (title, author, publication date, etc) as well as NPG-specific ontology terms. These datasets are being released under an open metadata license, Creative Commons Zero (CC0), which permits maximal use/re-use of this data.      NPG’s platform allows for easy querying, exploration and extraction of data and relationships about articles, contributors, publications, and subjects. Users can run web-standard SPARQL Protocol and RDF Query Language (SPARQL) queries to obtain and manipulate data stored as RDF. The platform uses standard vocabularies such as Dublin Core, FOAF, PRISM, BIBO and OWL, and the data is integrated with existing public datasets including CrossRef and PubMed.      More information about NPG’s Linked Data Platform is available at Sample queries can be found at ”

We very much hope that NPG’s example will encourage other subscription-access publishers to open their own journal article reference lists, and become early adopters of Open Citations.  Reasons why subscription-access publishers should willingly join NPG and open their citation data are given in my previous blog post.  While at first coverage among subscription-access publishers will be incomplete, this expanded Open Citation Corpus will, I am sure, draw in increasing numbers of publishers, and in true Web 2.0 style will become more useful the more publishers participate, resulting in value-added bibliographic and bibliometic services being created over the open data. Other subscription-access publishers who would like to contribute their journal article references to the Open Citations Corpus should contact me at <>.

Cite this article as: davidshotton, "Nature to open its reference list data," in OpenCitations blog, 11/06/2012,

Why publishers should open their references

Why should the publishers of subscription-access journals, who presently generate income from the sale of access to peer-reviewed full text scholarly articles, be willingly open the reference lists of these articles, and contribute these to the Open Citations Corpus for publication as open linked data? I would like to suggest the following reasons:

1. There is a general move towards open data, which is widely regarded as a common good. This includes citation data, i.e. bibliographic references from one article to another (in RDF Turtle format: A cito:cites B . ).
2. The reference lists at the end of journal articles are works of scholarship by the authors, who have chosen to include certain references and exclude other potentially citable papers from the reference list. However, the references themselves are simply items of bibliographic data, formatted according to the journal style, and do not benefit from the author’s creative input.
3. The reference list, together with the front matter (including the bibliographic information about the article itself) and the abstract, has traditionally been included within the copyright protection enjoyed by the article as a whole. However, the bibliographic information about the article and the article’s abstract are commonly made freely available, for example through PubMed. This same openness should now be afforded to the reference list within each article.
4. There is a home for such reference citation data: the Open Citations Corpus has been specifically created to house and publish scholarly bibliographic citation data, and is now preparing to welcome article reference lists from subscription-access journals, to supplement those already contributed from open-access journals.
5. For those publishers who already contribute their reference information to CrossRef as part of its Cited-By Linking service, this can be accomplished without any change to the publisher’s own publishing workflows, just by giving permission for CrossRef to flag the articles of certain journals as having open references. Open Citations intends to collaborate with CrossRef by harvesting the reference lists from such flagged articles, parsing them into RDF, and adding them to the Open Citations Corpus. Provided that the references are already being submitted to CrossRef, no work will have to be done by the publisher, and no changes in publishing procedure will be involved.
6. Open Citations will publish each reference list as an independent RDF Named Graph, with a unique URI, thereby protecting the integrity of the article reference list as a unit of scholarship, the source of which will be explicitly acknowledged.
7. The open citations data will then be offered back to publishers to use as they wish, e.g. for visualization of citation networks, calculation of metrics, etc., providing easier and more usable access to their own citation data than is currently afforded by commercial providers, who do not provide such data in linked data format.
8. Publishers will also be free to host their own open citations data, should they wish to do so.
9. For the majority of publishers, who would still receive subscriptions on the full articles themselves, opening their article reference lists in this way will cost nothing in terms of lost revenue.
10. Indeed, participation in the Open Citation Corpus will bring the following benefits to subscription-access publishers:
– Access to services to be built over the aggregated open citations data, for example an automated reference correction service available to editors upon receipt of a manuscript, for the automated pre-publication correction of errors in reference lists prior to article publication.
– Increased exposure to users of the references to the publisher’s own journal articles – a form of advertising. While at first coverage among subscription-access publishers will be incomplete, this expanding Open Citation Corpus will, in true Web 2.0 style, become more useful the more publishers participate.
– Even while coverage is incomplete, the Open Citations Corpus by its very nature contains reference citations to all the key papers published in every field covered – currently to all the key papers published in every biomedical field, enabling readers more easily to identify and find the most highly cited papers of each contributing publisher.
– Opening citations data will result in white-listing and general good-will from funding agencies, government and other advocates of open data, who might otherwise mandate publication by grantees in alternative open-access journals.
– Opening citations data will lead to support from scholars and researchers themselves, who wil be more inclined to publish in that publisher’s journals, feeling that at last the publishers would be giving back to them some of their own data, rather than selling it back to them as at present.

As my next blog post shows, one leading subscription-access publisher is now willing to open its journal article references in the way I have suggested.  Others who would like to so the same should contact me at <>.

JISC Open Citations: Wider Benefits to Sector & Achievements for Host Institution

One of the biggest challenges faced by modern scientists is information overload. The life sciences are probably the area most affected by it, with almost a million new entries being added to PubMed each year. While on-line publishing and bibliographic search engines have made the problem of finding individual research articles considerably easier, the present scholarly citation system inadequately exposes the knowledge networks that exist within the scientific literature, linking papers, authors and research projects.  Much of the problem stems from the lack of freely available citation data in machine-readable form.

In this Open Access age, it is a scandal that reference lists from journal articles, the core elements of the academic data cycle, are not freely available for use by scholars.   Current citation services are largely restricted to a small number of commercial companies whose valuable products are still insufficiently developed to satisfy all the needs of the academic community.

Google Scholar offers navigation through the citation network, but only in one direction – backwards. Thomson Reuter guards citation data in ISI Citation Index and Web of Knowledge as commercial assets, as does Elsevier for citation data in Scopus, with limited subscription-access search and display capabilities, and no methods for extracting citation data in bulk. Furthermore, they do not characterise the nature of citations between publications.

The value of citation data to the research community has grown as research evaluation has increased in importance. Citation metrics are increasingly used by institutions to establish their research quality, and by funding agencies to determine the effectiveness of their grant spending.

Citation data now need to be recognized as a part of the Commons – those works that are freely and legally available for sharing and reuse – extending the Science Commons / Open Knowledge Foundation philosophy to the world of scientific citation.

If machine-readable citation data for all scholarly publications were to be published freely on the Web, the construction and interrogation of citation networks would become trivially simple, with enormous advantages to scholarship. Thanks to CiteSeerX, citation data in computer science have been freely available for several years,

Similar access is now coming for other fields of scholarship, particularly for the biological sciences through CiteXplore. However, in none of these cases are the citation data available as Linked Open Data, and there are no convenient free tools, accessible to working research biologists, that permit them to visualize and navigate the literature by means of its citation network, or that permit knowledge analysts to pose generic questions over the whole corpus, such as determining whether those who publish in Open Access journals are more prone to cite other Open Access articles, in comparison with those who do not.

This project will produce citation information as Open, Linked Data which will address the short-comings of the current situation, aiming to provide researchers with the tools to explore the citation network.

Cite this article as: benosteen, "JISC Open Citations: Wider Benefits to Sector & Achievements for Host Institution," in OpenCitations blog, 15/07/2010,

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