Communications - December 2009

ions. Typical research-related processes could be augmented or even completely supplanted. For example, researchers could automatically get recommendations of papers and contacts based on what they are currently doing; experts might be automatically identified in a domain based on discussions around their papers and blog entries; peer reviewing could evolve to take into consideration the new social media and Web-based interactions; and even ‘impact factors’ for institutions might incorporate electronic analysis of all types of information and not just citations to publications and research grants.

Our support of the myExperiment ( http://www.myexperiment.org/) project is a demonstration of our belief that scientific collaboration and information sharing can be supported through social networking. The myExperiment project brings together social networks and workflows in a single information graph—a data mesh—that can be browsed, analyzed, and searched.

Conclusion

As researchers and scientific instruments can now produce and publish large amounts of data and information more easily than at any other point in history, there is an increasing requirement for automation tools to help manage and navigate the deluge of research data. For example, projects like Pan-STARRS (http://pan-starrs.ifa. hawaii.edu/) and the HLC (http://lhc. web.cern.ch/) will generate many peta-bytes of data. The emergence of folk-sonomies on the Web is one example of how user-driven categorization can help with information discovery. The

there is an increasing
requirement for
automation tools
to help manage and
navigate the deluge
of research data.
We need to
invest significant
resources to
making the semantic
computing vision
a reality.

need to deal with meaningful and rel-
evant information within the context
of one’s actions is growing. There is an
immense opportunity for the research
community to bring its expertise and
experience together in accelerating
the development of semantic comput-
ing technologies. We need to invest
significant resources to making the se-
mantic computing vision a reality by:
investing in semantics-aware in- ˲
frastructure;
increasing awareness of the po- ˲
tential of semantics-based computing;
and
training more researchers on se- ˲
mantics-based computing and related
technologies.

The discussion on data meshes shows the potential value of aggregating information in a (semi-)structured, machine-interpretable manner. We believe an ecosystem of desktop tools, cloud services, and data formats will emerge to support “information and knowledge management,” namely, the (automatic) acquisition, representation, aggregation, indexing, discovery, consumption, correlation, management, and inference of information. Doing so at scale would significantly improve the way we discover and share information and how we collaborate.

We have described a representative set of investments we are making to ease the transition of researchers toward a world where information is produced and consumed in a structured and semantics-rich manner (more information about the work and research tools offered by Microsoft Research for scientists can be found at http://research.microsoft.com/en-us/ collaboration/about/). However, this will not happen instantly. There is a lot

of unstructured data out there already. Data-mining technologies are necessary to automatically extract as much semantically rich information as possible. For example, Microsoft’s Live Labs has worked on machine learn-ing-based technologies to extract entities from the unstructured Web (see http://livelabs.com/projects/entity-extraction/). The research world needs similar technologies to be deployed at scale that can aggregate, index, and mine research-related information.

We believe such an ecosystem of semantics-aware tools and services will ultimately become the norm in our day-to-day interactions with computers, constituting a global “smart cyberinfrastructure.” However, if the big companies are to invest in implementing these ideas and technologies in their offerings (products and services), the research community must test and demonstrate their potential as part of the community’s attempt to build a smart cyberinfrastructure for research. Ultimately, this vision of a data mesh and smart cyberinfrastructure will go some way toward realizing the visions of the early pioneers like Vannevar Bush3 and J.C.R. Licklider. 8

References

1. Berners-Lee, T., Hendler, J.A., and Lasilla, O. The Semantic Web. Scientific American (May 2001).

2. Borgman, C.L. Scholariship in the Digital Age: Information, Infrastructure, and the Internet. Mi T Press, 2007.

3. Bush, V. As we may think. The Atlantic Monthly (1945); www.theatlantic.com/doc/194507/bush

4. Cycorp. Cyc Knowledge Base; www.cyc.com/

5. Dirks, L. and Hey, T., eds. CT Watch Quarterly: The Coming Revolution in Scholarly Communications & Cyberinfrastructure 3, 3 (Aug. 2007).

6. Fellbaum, C., ed. WordNet. The Mi T Press, 1998.

7. Helbig, H. Knowledge Representation and the Semantics of Natural Language. Springer, Berlin, 2006.

8. Licklider, J. C. R. Libraries of the Future. Mi T Press, 1965.

9. Shadbolt, N., Berners-Lee, T., and Hall, W. The Semantic Web revisited. IEEE Intelligent Systems 21, 3 (Mar. 2006), 96–101.

10. Wroe, C. et al. A suite of DAML+ OiL ontologies to describe bioinformatics Web services and data. International Journal of Cooperative Information Systems 12 (2003), 197–224.

Savas Parastatidis ( Savas.Parastatidis@microsoft. com) is a principal developer with Microsoft Technical Computing and a visiting fellow at the University of Newcastle upon Tyne, U.K.

Evelyne Viegas ( evelynev@microsoft.com) is a senior program manager responsible for the Online Technologies and Web Cultures initiative at Microsoft Research in Redmond, WA.