Cirrus Shakeri, SAP Labs
In this talk we will report on using semantic technologies in implementing tools for enterprise information management and decision support systems. We have built proof-of-concept software tools that assist knowledge workers in making sense of information within the context of some business processes.

Larry Lefkowitz, Cycorp, Inc.
Two major causes for optimism in the Semantic Technologies community are: 1) the rapid growth in the availability of open (linkable) structured datasets and associated ontologies, and 2) the belief that shared representation schemata will enable ontology integration and data sharing. Increasing pairwise linkages, as reflected especially at the dataset (instance) level by the growth of the Linked Open Data Cloud, offers evidence to support this optimism.

Paul Miller, The Cloud of Data
Nova Spivack, Radar Networks (Twine.com)
Daniela Barbosa, DataPortability Project/ Dow Jones
Fraser Kelton, AdaptiveBlue Inc
Stephen A. Leicht, Collexis Holdings Inc.
Amit Sheth, Kno.e.sis Center, Wright State U
Social networking continues to grow and evolve, expanding beyond the early adopters to attract usage in a wide variety of social, business and educational contexts. Community innovations such as the Twitter ‘hash tag’ demonstrate users’ desire to bring structure to their interactions with these networks, and a plethora of mashups make use of APIs to demonstrate the value of this structure. As yet more structure is added, and as the semantic richness grows, do these networks continue to become more powerful or does increased complexity run counter to the ways in which they are actually used?

Wen Ruan, TextWise
Ronald M Kaplan, Powerset division of bing
Christian F. Hempelmann, RiverGlass, Inc.
Riza Berkan, hakia
Semantic Search technology in the Semantic Web community is often understood as retrieval of knowledge from tagged data such as RDF sources, which require substantial formatting and markup to realize. Understanding unstructured query and document text and conducting searches according to their meaning is another approach, exemplified by linguistically rooted semantic matching, ontological knowledge-based semantic interpretation, and statistically based semantic similarity search.