закладки (спрятать)130
- This is the report of the W3C Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) as specified in the Deliverables section of its charter. In this report we present requirements for better defining the challenge of reasoning with and representing uncertain information available through the World Wide Web and related WWW technologies. Specifically the report: * identifies and describes situations on the scale of the World Wide Web for which uncertainty reasoning would significantly increase the potential for extracting useful information, * identifies methodologies that can be applied to these situations and the fundamentals of a standardized representation that could serve as the basis for information exchange necessary for these methodologies to be effectively used, * includes a set of use cases illustrating conditions under which uncertainty reasoning is important, * provides an overview and discusses the applicability to the World Wide Web of prominent uncertainty reasoning techniques and the information that needs to be represented for effective uncertainty reasoning to be possible, * includes a bibliography of work relevant to the challenge of developing standardized representations for uncertainty and exploiting them in Web-based services and applications. The report identifies various areas which require further investigation and debate.
- Two-way latent grouping model for user preference prediction Eerika Savia, Kai Puolamäki, Janne Sinkkonen and Samuel Kaski In: UAI 2005, 26-29 July 2005, Edinburgh, Scotland.
- Abstract. The envisioned Semantic Web aims to provide richly annotated and explicitly structured Web pages in XML, RDF, or description logics, based upon underlying ontologies and thesauri. Ideally, this should enable a wealth of query processing and semantic reasoning capabilities using XQuery and logical inference engines. However, we believe that the diversity and uncertainty of terminologies and schema-like annotations will make precise querying on a Web scale extremely elusive if not hopeless, and the same argument holds for large-scale dynamic federations of Deep Web sources. Therefore, ontology-based reasoning and querying needs to be enhanced by statistical means, leading to relevanceranked lists as query results. This paper presents steps towards such a “statistically semantic”Web and outlines technical challenges.We discuss how statistically quantified ontological relations can be exploited in XML retrieval, how statistics can help in making Web-scale search efficient, and how statistical information extracted from users’ query logs and click streams can be leveraged for better search result ranking. We believe these are decisive issues for improving the quality of next-generation search engines for intranets, digital libraries, and the Web, and they are crucial also for peer-to-peer collaborative Web search.
- "Google, Microsoft, Yahoo and others are developing next-generation technologies that automate and personalize information search."
- SUGGEST is a Top-N recommendation engine that implements a variety of recommendation algorithms. Top-N recommender systems, a personalized information filtering technology, are used to identify a set of N items that will be of interest to a certain user.
публикации (спрятать)739
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- BMC Bioinformatics, 11 (1): 267 (2010)
- SAC '04: Proceedings of the 2004 ACM symposium on Applied computing, стр. 1033--1039. New York, NY, USA, ACM Press, (2004)
- Knowledge and Information Systems, 10 (3): 357--381 (октября 2006)
- SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, стр. 329--336. New York, NY, USA, ACM Press, (2004)
- Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, стр. 1346- 1349. (2005)
- Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, стр. 1346-1349. (2005)
- SIGMOD Record, 31 (2): 84--93 (2002)
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