In this demo we present our approach to the use of Semantic Web technology in scholarly communications; it entails the understanding of research papers as an interface to the Web of Data. We are using the connectivity tissue provided by RDF technologies in order to facilitate semantic retrieval as well as to improve the user-experience when interacting with biomedical literature. Availability: http://biotea.idiginfo.org, http://biotea.idiginfo.org/gene/
As biomedical literature grows exponentially, so do the connections across
documents. Research articles are deeply interconnected to one another, and to resources in
the web –e.g., biomedical databases. Such interconnectedness makes the paper an
ideal interface to the Web of Data (WoD). Connections may be as evident as those
due to bibliographic references; they may also be as complex as those due to
similarities in topics, research questions, materials and methods, etc. Making use of such
connectivity tissue requires a semantically processed dataset, delivering a
selfdescriptive document fully interoperable with the Web. However, a solely linked
dataset may not be enough; semantic search engines, tools, and mash-ups should also
be provided in order to facilitate the access to the data as well as to improve the
enduser experience. RDF4PMC [
RDF4PMC orchestrates ontologies such as DoCO (http://purl.org/spar/doco), BIBO
(http://purl.org/ontology/bibo), DC (http://dublincore.org), and FOAF
(http://xmlns.com/foaf/0.1) to model the metadata and content of the article.
Meaningful terms in the content are represented as annotations modeled with the
Annotation Ontology (AO) [
Our RDF4PMC browser makes it possible for users to search for a human gene, and to retrieve related papers; the retrieval is presented as a list of alphabetically ordered articles: metadata –title, authors, and abstract, as well as links – GeneWiki, PubMed, PMC, and DOI, are displayed. Whenever possible, links to identifiers.org (http://identifiers.org) –a resolvable persistent system for biological related information, and Bio2RDF are also provided. Additionally, a cloud of tags complementing the bibliographic data for each article is presented; this cloud contains the biological relevant terms identified in the article. The weight of each term in the cloud depends on the number of biological entities associated to it.
Fig.2 presents a detailed view for one of the articles retrieved after searching for the human gene “insulin”. As illustrated in Fig.2 a), whenever a term in the cloud is selected, the vocabularies and, subsequently, the biological entities are displayed; different colors are used for different vocabularies. The interactive zone, Fig.2 b), changes depending on the selection in the cloud: (i) when a term is selected, the paragraphs containing that term are displayed; a simple navigation bar allows user to move from one paragraph to the other; similarly (ii) when a biological entity is selected, relevant information is displayed, e.g., sequences and 3D structures for proteins, structures for chemicals, or images for species. Our prototype identifies the type of the selected biological entity, and uses Biojs components (http://code.google.com/p/biojs) in order to display the graphical data.
A graph-based display is also possible; it facilitates navigation and filtering depending on terms contained in the retrieved articles. As articles are connected by shared terms, they are naturally arranged in a graph where articles are represented as nodes while terms are represented as edges. Whenever two articles share a link, there will be an edge connecting them; the number of biological entities associated to a term will determine the weight of the edge. The term-browsing graph is created from a term searched for by the user, for instance catalase. Once the searched term has been provided, the graph is generated with all articles containing that term; edges are added depending on the other shared terms between any pair of articles. As documents and terms are retrieved, the graph is reorganized following a force-based algorithm. Fig. 3 shows the term-browsing graph for the term catalase; it also illustrates some features facilitating the navigation: mouse over events on nodes and edges, as well as filtering options. For nodes, mouse-over activates a close icon on the right upper corner so the article can be removed from the graph; in the figure it is possible to observe the close icon for the article with title “Localization of the Carnation Italian ringspot virus […]”. For arcs, mouse-over displays the actual term, in the figure it is possible to observe the term PROTEASE. Terms can also be filtered out; as multiple terms can be shared amongst articles, an edge-based filtering feature has been defined: depending on the weight, i.e., number of biological entities, terms can be excluded from the graph; in the figure the minimum weight has been set to 30.
The components for the RDF4PMC browser that have been described in this paper illustrate how our browser acts as an interface to the WoD, particularly the data contained in the open subset of PMC. RDF4PMC delivers self-describing content that allows the implementation of semantic search, exploration, and retrieval mechanisms throughout the dataset as well as a new reading experience when interacting with the documents. The former is achieved by making extensive use of the interlinked nature of the RDF dataset; the latter is the consequence of using specialized visualization and manipulation gadgets that make it possible for the reader to browse the document focusing on those aspects he/she considers relevant for the research at hand. Exploring the network of interconnected documents facilitate the formulation of more precise queries; being able to post process queries based on information found in the documents deliver a new pivot from where to build queries. In the near future we are planning to explore the use of ontology mappings and expansion; users should not need to know the exact name of a gene, and should be able to search for related information, e.g., diseases or drugs. We will focus on knowledge that could be easily inferred as a consequence of the interrelated nature of the semantic model.