SWAN/SIOC is an alignment of two Web ontologies that, taken together, represent Scientific Discourse in online communities at different levels of granularity (content items and discourse elements). The goal of this alignment is to make the discourse structure and component relationships much more accessible to computation, so that information can be navigated, compared and understood in a context far better than is currently possible, both across and within domains. This paper describes these two models and their alignment to support research in Health Care and Life Sciences, as well as an overview of projected future work on the topic.
Semantic Web technologies allow us to provide interoperable and structured data
on the Web, enabling a paradigm shift from the current Web of Documents towards a
Web of Data1. An increasing number of Semantic Web applications have been
deployed in various environments and one of the most popular examples is related to
the Social Web context, or what is termed “Web 2.0” [
Moreover, various research efforts have been carried out on representing
argumentative discussions and scientific discourse using Semantic Web technologies.
A working example of the latter is represented by the SWAN (Semantic Web
Applications in Neuromedicine) project [
However, so far, there has not been much joint work involving the Scientific Discourse Representation and Social Semantic Web communities, while there are obviously strong ties between both, as scientific argumentation often happens within communities of interest, on online platforms such as blogs, wikis, or in online scientific publishing.
In this paper, we present the SWAN/SIOC project that aims to bridge the gap between Scientific Discourse Representation and the Social Semantic Web, by defining a coherent ontology capable of representing both high-level descriptions of communities (thanks to SIOC) and argumentative discussions (using SWAN).
In the next section, we will introduce both the SWAN and SIOC ontologies. Then, we will describe the various alignments that we have defined between both, in terms of new classes and mappings between classes and properties from these two models, leading to the SWAN/SIOC ontology. We will also present one example of data querying focusing on the relevance of such an alignment. Finally, we will present related work on the topic before concluding the paper with an overview of future work. 2
We provide an overview of SWAN and SIOC in this section, with motivating use cases. We will focus especially on their relevant features in the context of the SWAN/SIOC integration, described in the following section, which is targeted for use within the Health Care and Life Sciences domain.
The SWAN project2 attempts to model scientific discourse about Alzheimer disease and its supporting evidence in a rich and extensible way that is compatible with the way the domain of Alzheimer Disease (AD) research functions as a technology-mediated knowledge ecosystem. The SWAN knowledge base, for which the SWAN ontology functions as a schema, consists of a semantically-structured network of hypotheses, claims, dialogue, evidence, publications and digital repositories, incorporating and extending such knowledge. Curators of the SWAN knowledge base have catalogued and annotated dozens of etiopathological models of AD, in collaboration with many of the leading researchers in the field. Interestingly, SWAN can not only show the evidentiary support (if any) for each claim in such models, but also a claim’s relationships (support, conflict, alternative interpretation,
neutral) with claims in other models. AD researchers can access the knowledge base online and they can use it orient themselves to new discoveries in the field and how they are related to current models, and to discuss new claims in the literature.
The SWAN ontology3 was created and continues to evolve in the context of
building actual applications for biomedical researchers, as well as through extensive
discussions and collaborations within the larger bio-ontologies community, including
the NeuroCommons effort [
In the Health Care and Life Sciences domain, many researchers are now using Web 2.0 tools or services to share their knowledge in addition to providing traditional publications (research papers). For example, scientists and researchers use blogs to post about their experiments or recent publications that they have read; they use wikis to build information collaboratively (from encyclopedias to project proposals); and they may even participate in scientific social networks, such as Nature Networks.
However, while these services help in the process of publishing information, they generally function as independent and isolated data silos. Therefore, it is difficult to retrieve and to browse information spread across various platforms. A researcher
4 http://swan.mindinformatics.org/spec/1.2/discourserelationships.html 5 http://swan.mindinformatics.org/spec/1.2/scientificdiscourse.html 6 http://swan.mindinformatics.org/spec/1.2/collections.html interested, for example, in AD will have to discover and browse various services on his or her own to find relevant information (if any exists).
The aim of the SIOC project [
For example, imagine that ACME Pharma uses various blogs, wikis and microblogging applications to enable communication and knowledge sharing between its different research teams. By providing SIOC exports of all this data, and through the use of existing applications, APIs and a central RDF repository to store this data, it is then possible to query it from a single place using uniform SPARQL queries. Moreover, these queries can take advantage of the SIOC Types module, for example, so as to retrieve only instances of sioct:WikiArticle or sioct:BlogPost depending on the requested sources of information.
As described in the previous sections, SWAN and SIOC function in a 7 http://rdfs.org/sioc/spec 8 http://rdfs.org/sioc/types complementary way: SWAN provides fine-grained modeling primitives for scientific discourse elements while SIOC can represent the more generic contributions found in online communities. Bridging both would therefore help one to browse these communities and their related discussions using various levels of granularity, e.g. at the item level (thanks to SIOC) and then zooming in to the discussion level (using SWAN). For example, considering the previous ACME use case, the items could be connected to each other using SIOC (related posts, replies, etc.), but also the kind of relationship that they have to each other could be specified using SWAN (agreement, disagreement, supporting hypothesis, etc.). Then, users would be able to browse information from the various ACME Social Web applications using different layers, depending on their query and the kind of information they want to retrieve. Moreover, combining these two levels also provides advanced querying patterns. When browsing a wiki (represented using SIOC), one could identify all elements that support or contradict the claims of that wiki page (using SWAN) and then filter by content types, i.e. blog posts (using SIOC).
In order to bridge the SWAN and SIOC ontologies, alignments between these two
models have been provided, as we will now detail. These different mappings have
been defined in a SIOC module available at http://rdfs.org/sioc/swan, an overview of
which is given in Figure 3. This module imports the SIOC Core Ontology and its
Types module, as well as the SWAN Ontology, via its OWL definition file9. It has
been validated as OWL-DL (using Pellet10 version 1 [
Previously, the SIOC Core ontology was designed in RDFS, whilst also being an OWL-Full ontology. However, one of the requirements for the SWAN project and related services is to be able to reason on SWAN data to, for example, use OWL cardinality constraints defined in the Scientific Discourse module to verify that each instance of swanscidis:DiscourseElement has at maximum one swanscidis:title. Using the SIOC Ontology with SWAN would not ensure that such reasoning could be achieved in a finite time, because of the OWL-Fullness of SIOC. Therefore, and as we needed the computability of OWL-DL, we adapted the existing RDFS SIOC Core Ontology to OWL-DL by: ! Declaring the value of rdf:type as being owl:Class for some classes defined in external ontologies and used in the SIOC Core Ontology, such as foaf:Person, since we do not use owl:imports to include these external ontologies in SIOC but require that typing to make the ontology OWL-DL; ! Adapting some disjointness statements in the SIOC ontology to make them compliant with OWL-DL axioms, using owl:disjointWith properties.
In addition to the aforementioned changes to the SIOC Core ontology, various classes from the SWAN ontology have been mapped to classes in the SIOC Core ontology. From SWAN Scientific Discourse, the following classes have been defined as subclasses (via rdfs:subClassOf) of sioc:Item: ! swanscidis:DiscourseElement; ! swanscidis:ResearchStatement; ! swanscidis:ResearchQuestion; ! swanscidis:ResearchComment.
In addition, from SWAN Citations, the following mappings have been defined: ! swancit:Citation and swancit:JournalArticle are subclasses of sioc:item; ! swancit:WebArticle and swancit:WebNews are subclasses of sioc:Post; ! swancit:WebComment are subclass of sioc:Comment.
Consequently, most of the SWAN elements became subclasses of the sioc:Item class, since sioc:Post is also defined as a subclass of that resource.
However, as one can see when observing these mappings, some of them are redundant. For example, we explicitly assert that swancit:JournalArticle is a subclass of sioc:item, though this could be inferred from the assertions that swancit:JournalArticle is a subclass of swancit:Citation and swancit:Citation is in turn a subclass of sioc:Item.
In addition, a new class has been introduced in the SWAN/SIOC module for online journals (these are websites where immutable articles are published and comments are allowed on them). swansioc:OnlineJournal is defined as a subclass of sioc:Container, and can be used to represent online publication venues such as StemBook11.
Finally, there may be a need to state that a particular swanscidis:DiscourseElement is a part of a sioc:Item, for example, to represent that a particular hypothesis is part of a blog post, and then to identify in which forums this blog post is contained. This item-to-item inclusion is not specific to the SWAN use case and can already be achieved thanks to the dcterms:hasPart property from Dublin Core, as suggested in the SIOC specification document.
In addition to the previous classes, mappings have been defined between various properties of the SWAN Scientific Discourse Relationship and the sioc:related_to property of the SIOC Core ontology. The following properties use this mapping, and this permits us to infer that two items are related to each other as soon as there is a particular discourse relationship between both: ! swandisrel:agreesWith; ! swandisrel:alternativeTo; ! swandisrel:arisesFrom; ! swandisrel:cites; ! swandisrel:consistentWith; ! swandisrel:disagreesWith; ! swandisrel:discusses; ! swandisrel:inconsistentWith; ! swandisrel:inResponseTo; ! swandisrel:motivatedBy; ! swandisrel:refersTo; ! swandisrel:relatedTo.
Once again, some of these mappings may be redundant, since they can inherit from the swandisrel:relatedTo property, but we provide these for the same reasons as specified earlier for the class mappings.
In order to give an overview of the advantages achieved using these alignments, we ran an initial experiment by querying SWAN data using SPARQL queries based on SIOC, hence benefiting from the various mappings between classes and properties that we have already described.
We generated a set of N random instances of swanscidis:DiscourseElement, linked to each other using each of the 13 relationships in the Scientific Discourse Module, hence providing a dataset of N+13*N*(N-1) triples12. Then, we ran a simple SPARQL query using SIOC patterns, identifying all distinct couples of related items within the dataset (this kind of query 11 http://www.stembook.org/ 12 N triples for instances generation and 13*N*(N-1) for the relationships often being used in SIOC applications to identify related posts on the Web): PREFIX sioc: <http://rdfs.org/sioc/ns#> SELECT DISTINCT ?s ?o WHERE { ?s sioc:related_to ?o .
?s a sioc:Item . ?o a sioc:Item . }
The query was run using Pellet 2 (making use of its OWL-DL SPARQL capabilities) on a 2.53 MHz MacBook Pro with 4 GB RAM. As expected, we retrieved a list of N(N-1) answers each time, hence being able to simply express queries over SWAN data using SIOC patterns.
In addition, we tried each query using both the full property mappings and with a single mapping between swandisrel:related_to and sioc:related_to in order to evaluate the influence of our choice of mappings’ redundancy over computation time, as we expressed previously. The results for various values of N are described below (times are given in milliseconds). As one can see, while the full mappings are not a good choice when dealing with a small number of statements, it becomes interesting when the number of statements grows. Hence, since SWAN knowledge bases generally contain millions of triples, we believe our choice was accurate and enables faster computation of SPARQL queries using SIOC patterns over SWAN data.
Related work includes IBIS [
An argumentation module extension to SIOC has been provided to allow one to
formulate agreement and disagreement between SIOC content items13 [
Another recent effort that may align well with the SWAN/SIOC project is aTags
[
In this paper, we introduced the motivations for the SWAN/SIOC initiative and detailed the mappings that have been created between the SWAN and SIOC ontologies in order to enable better computation and understanding of Scientific Discourse in online communities. We also demonstrated how these mappings could be used for data querying in order to provide both high-level and more fine-grained descriptions of relations between statements.
Future work will consist of building applications on the top of these new alignments, especially within the Science Collaboration Framework16. In addition, we will also investigate how a similar process of mappings could be applied to other ontologies relevant to Scientific Discourse Representation, hence providing a complete and integrated framework for machine-readable discourse in online scientific communities.
We hope that SWAN/SIOC will be a first step towards a more comprehensive work on aligning different frameworks for discourse representation in online communities.
The work presented in this paper has been funded in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Líon 2), and by a generous gift from an anonymous foundation. We would like to thank members of the Scientific Discourse task force in the W3C Semantic Web for Health Care and Life Sciences Interest Group for their valuable discussion. Special thanks are due to Susie Stephens and Scott Marshall for their careful critical review of draft material on the SWAN-SIOC integration, and to Susie Stephens both for suggesting the project and for scribing careful notes during our conference calls. Thanks are also due to Eric Prud’hommeaux of the W3C for his excellent liaison and technical support during this project. 14 http://salt.semanticauthoring.org/ 15 http://www.w3.org/2001/12/replyType 16 http://sciencecollaboration.org/