=Paper=
{{Paper
|id=None
|storemode=property
|title=Using the Annotation Ontology in Semantic Digital Libraries
|pdfUrl=https://ceur-ws.org/Vol-658/paper505.pdf
|volume=Vol-658
|dblpUrl=https://dblp.org/rec/conf/semweb/Garcia-CastroGC10
}}
==Using the Annotation Ontology in Semantic Digital Libraries==
https://ceur-ws.org/Vol-658/paper505.pdf
Using the Annotation Ontology in Semantic Digital
Libraries
L. Jael García Castro1, Olga X. Giraldo2, Alexander García Castro3
1
Universität der Bundeswehr München, Werner-Heinsenberg-Weg 39,
85779 Neubiberg, Germany
w31blega@unibw.de
2 National University of Colombia
Palmira, Valle, Colombia
oxgiraldo@unal.edu.co
3 University of Bremen, Bibliothekstrasse 1,
28359 Bremen, Germany
cagarcia@uni-bremen.de
Abstract. The Living Document Project aims to harness the collective
knowledge within communities in digital libraries, making it possible to
enhance knowledge discovery and dissemination as well as to facilitate
interdisciplinary collaborations amongst readers. Here we present a prototype
that allows users to annotate content within digital libraries; the annotation
schema is built upon the Annotation Ontology; data is available as RDF,
making it possible to publish it as linked data and use SPARQL and SWRL for
querying, reasoning, and processing. Our demo illustrates how a social tagging
system could be used within the context of digital libraries in life sciences so
that users are able to better organize, share, and discover knowledge embedded
in research articles. Availability: http://www.biotea.ws/videos/ld_ao/ld_ao.html
Keywords: Social semantic web, digital libraries, Web 3.0
1 Introduction
Semantic Digital Libraries (SDL) make extensive use of meta-data in order to
support information retrieval and classification tasks. Within the context of SDLs,
ontologies can be used to: (i) organize bibliographic descriptions, (ii) represent and
expose document contents, and (iii) share knowledge amongst users [1]. There have
been some efforts aiming to make use of ontologies and Semantic Web technology in
digital libraries; for instance, JeromeDL (http://www.jeromedl.org) allows users to
semantically annotate books, papers, and resources [2]. The Bricks project
(http://www.brickscommunity.org/) aims to integrate existing digital resources into a
shared digital memory; it relies on OWL-DL in order to support, organize and
manage meta-data [1]. Digital libraries within the biomedical domain store
information related to methods, biomaterial, research statements, hypotheses, results,
etc. Although the information is in the digital library, retrieving papers addressing the
same topic and for which similar biomaterial has been used is not a trivial task [3].
Ontologies have shown to be useful for supporting the semantic annotation of
scientific papers [4] –and thereby facilitating information retrieval tasks. However, as
ontologies are often incomplete users should be able to provide additional metadata
[3, 5]. Collaborative social tagging and annotation systems have recently gained
attention in the research community [6, 7]; partly because of their rapid and
spontaneous growth and partly because of the need for structuring and classifying
information. Collaborative social tagging is considered exemplary of the WEB2.0
phenomena because such sites use the Internet to “harness” the collective intelligence.
It has been observed that several users can tag a resource; tags used for individual
resources tend to stabilize overtime [8]. Our implementation uses the Annotation
Ontology (AO) [9] for supporting the automatic and manual annotation of research
articles. Annotations may be rooted in existing ontologies or provided by users; we
are supporting the tagging of atomic components within papers –e.g. words, tables,
figures. The content of the paper and the corresponding tags are being presented as
linked data, this facilitates the interoperability between the paper and external
resources –e.g. databases, repositories for experimental data, etc. Our approach aims
to facilitate sharing, linking, and integrating knowledge across digital libraries and
online resources. It also aims to support concept-based collaboration.
2 Enhancing Digital Libraries with the Annotation Ontology
The AO is built upon the Annotea Project (http://www.w3.org/2001/Annotea/); it is
also compatible with Newman’s (http://www.holygoat.co.uk/projects/tags/), MOAT
[7] , and SKOS (http://www.w3.org/2004/02/skos/) ontologies. The AO supports free
and semantic annotation over the paper; it facilitates tagging the paper as a whole as
well as portions of it, i.e. atomic annotation. It also provides facilities for curation,
provenance, authoring and versioning. Annotations are not limit to tags but also
include notes, comments, erratum, etc.
Our prototype, the LD, makes it possible for users to annotate papers as well as
specific sections of them, e.g. words, sentences, images, tables, etc. It also
interoperates with automatic annotation tools such as Whatizit (http://www.ebi.ac.uk/
webservices/whatizit). Annotations are used to improve search and retrieval of papers;
it also makes possible to find related papers and researchers. Within the LD, the AO is
used to represent the network of concepts and related resources derived from the
annotations; in this sense, the AO applied to papers plays a similar role to that played
by FOAF in human-centric social networks. The LD facilitates discovering links and
improving interaction across papers and researchers.
An atomic annotation is shown in Fig. 1. The document is internally represented by
an XML as it is the format used by the publisher; however RDF is also possible. The
annotated elements are identified by using XPointer technology (http://www.w3.org/
TR/WD-xptr). The provenance is based on FOAF ontology while tagging reuses
Newman’s and MOAT ontology. The annotation states a related meaning for the term
“partial sequence on psy promoter” to the GeneBank
(http://www.ncbi.nlm.nih.gov/genbank/) term AB005238, since the meaning is linked
to a well established ontology, the type of the annotation is Qualifier.
Fig. 1. LD and AO in action
The search & retrieval module is based on that one usually provided by digital
libraries; it uses clouds of annotations and annotators to facilitate navigation and
filtering. Once a paper is selected, the annotation module allows users to identify
annotations on the paper, using different colors for different types of annotations, i.e.
manual and automatic annotations, and also to distinguish amongst categories, i.e.
species, proteins, genes, etc. It also allows users to manage their annotations and to
link them to external resources. Additional information on automatic annotations is
provided: links to specialized sources such as UniProt (http://www.uniprot.org). The
contextual reading module allows easily navigating across the paper by jumping
from one annotation to other. The linked open data module allows exporting
annotations as RDF, making it possible to use query and reasoning languages such as
SPARQL and SWRL. An overview of the LD modules is showed in Fig. 2.
3 Final Remarks
“Less is more” illustrates the collaboration dynamic that embodies the Long Tail
(http://en.wikipedia.org/wiki/Long_Tail) principle within the Social Web; a huge
number of people providing relatively small contributions that collectively are
substantial and significant. Current available metadata in digital libraries is not
enough as to support quires such as “retrieve papers for which microarrays have been
used in liver mice”. By making it possible for ontologies and free-provided terms to
live together within the scaffold granted by the AO executing such complex queries is
possible. It also facilitates the enrichment of the available metadata. In addition,
presenting the paper as RDF allows going beyond the PDF without compromising the
business model most publishers have –selling access to the full content of the
document. The LD approach offers an environment in which researchers harness the
collective intelligence as they are building networks based on similar reading
practices. Our future work includes: i) enhancing meta-data on authors and co-
authors, ii) allowing users to organize networks, use social consensus mechanisms,
and create relationships between annotations, and iii) better orchestrating the LD with
existing biomedical ontologies, e.g. improving the user interface for large ontologies.
Fig. 2. LD: Modules and Characteristics
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