Named the "Best Online Reference Service" by the CODiE Awards, HighBeam is a premiere online library where you can find research, facts, and articles. We collect millions of articles from newspapers like The Washington Post and The Boston Globe, magazines like The Economist and Newsweek, and journals like JOPERD and Journal of Research in Childhood Education. We deliver all of this in a single research Web site.
HighBeam also provides an in-depth online library of reference works. Research online dictionaries, including Webster's New World Dictionary and The Oxford American College Dictionary as well as encyclopedias from Britannica and Columbia.
New articles are added to HighBeam daily. Plus we have an extensive article archive that includes newspapers, journals, and magazine back issues dating back more than 20 years!
Verified facts, information, and biographies from trusted sources
Encyclopedia.com gives you credible answers from published reference works – all in one place:
* 49 encyclopedias from sources like Oxford University Press, Britannica, and Columbia University Press
* 73 dictionaries and thesauruses with definitions, synonyms, pronunciation keys, word origins, and abbreviations
Jigsaw provides an online business directory of company information and more than 10 million business contacts. If your success depends on reaching out to others, Jigsaw is essential. And that's why over 500 corporations and more than 600,000 members turn to Jigsaw.
Every contact in Jigsaw is complete with full name, title, postal address, hard-to-find email address and telephone number.
Model, organize and leverage content and knowledge. Manage reference schemas, taxonomies, thesaurus and ontology. Classify content. Power vertical search portals
25,000+ Educational Resources & Subject Guides with Degree Information
Academic Info is an independent online subject directory of over 25,000 hand-picked educational resources for high school and college students. Academic Info also features subject guides with information about distance education and online degree options, as well as admissions test preparation resources (SAT, GRE, LSAT, MCAT, GMAT, USMLE, TOEFL).
We are an information science research group developing software and methodologies to exploit Internet-based data sources for social sciences research, in addition to scientometrics, link analysis, cybermetrics and webometrics.
Qubit is an open-source software toolkit that will allow institutions such as archives, libraries, museums, and art galleries to manage and host web-based collections of information resources. Qubit supports multi-lingual and multi-repository collections.
The goal is to provide an easy-to-use, flexible toolkit that complies with open standards (e.g. Dublin Core, ICA-ISAD, MODS, EAD, METS) and is developed using an open architecture that takes advantage of emerging web-based tools and practices.
Who?
Qubit is the collaborative effort of two seperate open-source software projects that have decided to work together to leverage time, knowledge and skills. Each of these projects is using and contributing to Qubit as the underlying toolkit to build their own applications.
The Economics of the Internet, Information Goods,
Intellectual Property and Related Issues
Compiled by Hal R. Varian
Tools For Viewing Files
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Accounting & Measuring Traffic
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Announcements
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Background and Reference
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Electronic Commerce
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Electronic Publishing
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Government Resources
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Intellectual Property
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International
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Intranets
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Miscellaneous Resources
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Network Economics
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Policy and Law
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Pricing
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Security, Privacy and Encryption
What Topic Maps Do
When XML is introduced into an organization it is usually used for one of two purposes: either to structure the organization's documents or to make that organization's applications talk to other applications. These are both useful ways of using XML, but they will not help anyone find the information they are looking for. What changes with the introduction of XML is that the document processes become more controllable and can be automated to a greater degree than before, while applications can now communicate internally and externally. But the big picture, something that collects the key concepts in the organization's information and ties it all together, is nowhere to be found.
[Diagram]
SemLink is a project whose aim is to link together different lexical resources via a set of mappings. These mappings will make it possible to combine the different information provided by these different lexical resources for tasks such as inferencing. We also plan to use the mappings to aid in semi-automatic extension of each resources coverage, to increase the overall overlap in coverage. Currently, we are creating mappings between the following resources:
* PropBank: A corpus of one million words of English text, annotated with argument role labels for verbs; and a lexicon defining those argument roles on a per-verb basis.
* VerbNet: A lexicon that groups verbs based on their semantic/syntactic linking behavior.
* FrameNet: A lexicon based on frame semantics.
* WordNet: A lexicon that describes semantic relationships (such as synonymy and hyperonymy) between individual words.
The content of all four of these resources can be browsed on-line using the Unified Verb Index. A presentation giving an introduction to SemLink, and two of the resources it combines (PropBank and VerbNet), was given at the SIGSEM/ISO workshop held jointly with the IWCS-7 conference.
Finding important information in unstructured text
From Language and Information Technologies
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A vast majority of the information we deal with in everyday life consists of raw, unstructured text, where the most important facts or concepts are not always readily available, but hidden in the myriad of details that accompany them. To handle and digest the sheer amount of information we are exposed to in this information age, more sophisticated procedures are required to unveil the important parts of a text, and to allow us to process more information in less time. The goal of this project is to develop robust and accurate techniques to automatically extract important information from unstructured text, in the form of keyphrases (keyphrase extraction) or entire sentences (extractive summarization).
Funded by Google
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Graph-based NLP
From Language and Information Technologies
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The goal of this research project is to investigate efficient graph-based representations of text, and explore the application of ranking models based on such graph structures to natural language processing tasks. We bring together methods from computational linguistics and graph-theory, and combine them into a suite of innovative approaches that will improve and ultimately solve difficult problems in natural language processing. Specifically, we are currently working on the application of graph centrality algorithms to problems such as word sense disambiguation, text summarization and keyword extraction.
?. published on the web by University of Pittsburgh, School of Information Sciences, (2003)Report on the NSF Workshop on Research Directions for Digital Libraries, June 15-17, 2003, Chatham, MA.