Documentary about the Semantic Web





Networks - Semantic Web

Data structured in ontologies can be visualized as networks of nodes and edges representing subjects, objects, and predicates. A combination of metadata and content could thus represent the complex structure of the university’s scholarly work. To get an impression of the use of interactive visualization of network graphs for navigating data, you can try the links in the figures included in this section. A somewhat futuristic example of an interactive 3D network graph visualization user interface is Yose Widjaja’s Skyrails. The visuals are very impressive and it looks like a fun interface. However, it is not very clear how to get an overview of everything that is in the graph, and how exactly the graph makes it easier or more interesting to navigate and explore the data. How do we know what the colors and symbols stand for? We still need to read the labels of the nodes as text. The project is part of a doctoral dissertation and might still be under development. The latest information available at the time of this report was from Fall 2009.


Screen_shot_2010-06-21_at_4.18.23_PM.png
Visualization of URIs in RDF using W3C’s online RDF validator, which has a URI graph visualization tool (11) (try, for example, to enter the BibApp VIVO RDF output created with the BibApp team in the Bibapp section of this report



[Extract from report created by Klaar De Schepper for Columbia LIbrary research project, Figures from materials describing http://www.vivoweb.org]
VIVO_Horizontal_Detail.png
Figure 4. Protege OWL Viz Visualization of classes in VIVO, detail of "person" class and subclasses

One of the proposed ways of making data on the Internet more easily accessible is by the use of Semantic Web technology. Semantic data are structured according to ontologies, which are “formal, explicit specification[s] of a shared conceptualization” [6]. Formal ontology has emerged as a knowledge representation infrastructure for the provision of shared semantics to metadata, targeted to machine-readability [7]. Ontologies are particularly well-suited for research in areas with vast amounts of available data, where the relationships to be explored are not hierarchical. Because the relationships used in ontologies are formally defined, it is possible to use a reasoner to perform automated reasoning—for example, a reasoner can determine if one class is a subclass of another class . Information is represented according to the ontology scheme using the Resource Description Framework (RDF); the base element of the RDF model is the “triple” subject - predicate - object structure (Fig. 5).

VIVO_national_blue_050510_Person_Predicate_Objects.png
Figure 5.This diagram shows the way in which data is structured in Semantic Web applications like VIVO see (9)

Using RDF, a metadata data model that has an XML syntax and is thus human and machine readable, instances of classes of “things” can be created. RDF subject-object-predicate statements can then be mapped to an ontology. In the diagram in Figure 4, an undergraduate student is automatically known to be a Student and a Person according to the class hierarchy of the ontology. In RDF, “things” can be assigned properties that describe relationships between and among them. For instance, a faculty member has research areas, is a staff member of an organization within the university, and is featured in news articles. A resource (the subject) is linked to another resource (the object) through an arc labeled with a third resource (the predicate). The <subject> has a property <predicate> valued by <object> [8] .
WebSci10_VIVO_final_VIVO_VIVO_Links_Between_Two_Persons.png
Figure 6. Semantic Web applications such as VIVO express relationships between people and things. For instance, in this example we see that Andrew McDonald is a staff member and Susan Riha is head of NYS WRI, and both are researching crop management and have co-authored a paper (9)

The four linked data principles as proposed by Tim Berners-Lee in 2006 are as follows [10]:
  1. All items should be identified using URIs (Uniform Resource Identifiers);
  2. All URIs should be dereferenceable, that is, using HTTP URIs that allow looking up the item identified through the URI;
  3. When looking up an URI—that is, an RDF property is interpreted as a hyperlink—it leads to more data, which is usually referred to as the follow-your-nose principle;
  4. Links to other URIs should be included to enable the discovery of more data.

By using standardized vocabularies, linked data initiatives will eventually connect to form the Semantic Web, one vast network of information, stored in the “Linked Data Cloud” (see Fig. 7).
WebSci10_VIVO_final_VIVO_LInkedDataCloud.png

The Semantic Web and Library Metadata

In a recent library technology report titled “Library Data in a Modern Context,” library technology specialist Karen Coyle writes:

Today, we face another significant time of change that is being prompted by today’s library user. This user no longer visits the physical library as his primary source of information, but seeks and creates information while connected to the global computer network. The change that libraries will need to make in response must include the transformation of the library’s public catalog from a stand-alone database of bibliographic records to a highly hyperlinked data set that can interact with information resources on the World Wide Web. The library data can then be integrated into the virtual working spaces of the users served by the library. ... This is a radical change in the context for library metadata, yet it is a logical extension of the design for sharing that has been a principle of library cataloging.[11-13]

This summer’s release of RDA (Resource Description and Access), a new standard for metadata describing resources held in the collections of libraries, archives, museums, and other information management organizations, is paralleled by an effort to build Semantic Web-enabled vocabularies. RDA was designed while keeping in mind its translation to RDF[7, 12-14].

The Semantic Web and Scholarly Work
Scientists could share their research and data with other scientists more easily through real-time publishing and sharing of experimental data and by publishing work using semantic markup. Using semantics to encode scientific work will increase the potential of online collaboration tools for researchers and enable, for example, online “journal clubs” collected by semantic similarity of scholarly work [15, 16] and enriched querying of knowledge databases through question-answering systems [17].

Potential uses of Semantic Web technology for scholarly work are under active development by universities. One suggestion has been made for a community-wide annotation of texts by which “links [could] be established between atomic components of papers (domain terminologies, concepts, words, pieces of images or segments of video) and resources over the Web that are capable of processing and/or adding meaning to them”[18]. Tufts University has developed the Visual Understanding Environment (VUE), an open-source project focused on creating flexible tools for managing and integrating digital resources in support of teaching, learning, and research. “Nodes” in the VUE view maps can be assigned elements from Web Ontology Language (OWL) ontologies. Find more information about this software in the Interface Design section.



Semantic Web Resources


  • “Just What Is an Ontology, Anyway?” is a great 6-page tutorial that describes some definitions of “ontology” as it relates to computer applications and gives an overview of common ontology-based applications [19].
  • For techies, there is also a thorough overview/introduction by the World Wide Web Consortium’s Semantic Web Activity Lead Ivan Herman [20]
  • Videos with "Intro to semantic Web/ ontology construction" by Cody Burleson



6. Gruber, T.R., A translation approach to portable ontology specifications. Knowledge acquisition, 1993. 5: p. 199-199
7. Sicilia, M.A. and M.D. Lytras, Metadata and semantics. 2008: Springer-Verlag New York Inc.
8. Triples and graph. Available from: http://www710.univ-lyon1.fr/~champin/rdf-tutorial/node4.html.
9. Team, V.P. A Close Look into VIVO. 2010 May 6; Available from: https://confluence.cornell.edu/download/attachments/118475794/VIVO_national_blue_050510.pptx?version=1.
10. Hausenblas, M., Linked Data Applications. First Community Draft, DERI, 2009
11. Coyle, K., Library Data in a Modern Context. Library Technology Reports, 2010. 46(1): p. 5-5
12. Coyle, K., Meaning, Technology, and the Semantic Web. The Journal of Academic Librarianship, 2008. 34(3): p. 263-264.http://www.sciencedirect.com/science/article/B6W50-4SBHF0F-2/2/b80dc588ae5081021412cffcb1b43ab8
13. Coyle, K., Changing the Nature of Library Data. Library Technology Reports, 2010. 46(1): p. 14-14
14. Hillmann, D., et al., RDA Vocabularies. D-Lib Magazine, 2010. 16(1/2).http://www.dlib.org/dlib/january10/hillmann/01hillmann.html
15. Can New XML Technologies and the Semantic Web Deliver on Their Promises? « The Scholarly Kitchen %U http://scholarlykitchen.sspnet.org/2010/05/10/can-new-xml-technologies-and-the-semantic-web-deliver-on-their-promises,
16. Zhao, J., et al., The implications of Semantic Web technologies for support of the e-Science process.
17. Lopez, V., et al., AquaLog: An ontology-driven question answering system for organizational semantic intranets. Web Semant., 2007. 5(2): p. 72-105.http://portal.acm.org/citation.cfm?id=1265608.1265747&coll=GUIDE&dl=GUIDE&CFID=94450842&CFTOKEN=42525121
18. Garcia-Castro, A., et al., Semantic Web and Social Web heading towards Living Documents in the Life Sciences. Web Semantics: Science, Services and Agents on the World Wide Web, 2010. In Press, Corrected Proof.http://www.sciencedirect.com/science/article/B758F-4YRXD5B-2/2/4cb95e52479feaa463752e614288fefb
19. Jepsen, T.C. Just What Is an Ontology, Anyway? IT Professional. 2009. 22-27 http://www.computer.org/portal/web/computingnow/1009/whatsnew/itpro
20. Tutorial on Semantic Web. Available from: http://www.w3.org/People/Ivan/CorePresentations/SWTutorial/.