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.
"In Semantic Web languages, such as RDF and OWL, a property is a binary relation: it is used to link two individuals or an individual and a value. However, in some cases, the natural and convenient way to represent certain concepts is to use relations to link an individual to more than just one individual or value. These relations are called n-ary relations. For example, we may want to represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on. Another example is representing relations among multiple individuals, such as a buyer, a seller, and an object that was bought when describing a purchase of a book. This document presents ontology patterns for representing n-ary relations in RDF and OWL and discusses what users must consider when choosing these patterns."
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.
The OWL API is a Java API and reference implmentation for creating, manipulating and serialising OWL Ontologies. The latest version of the API is focused towards OWL 2
Welcome to the OpenMath website. OpenMath is an extensible standard for representing the semantics of mathematical objects. If you haven't heard about it before you might want to consult the overview.
Proceedings of KDD Cup and Workshop 2007
The Workshop was Co-organized by ACM SIGKDD and Netflix
Held during KDD-2007, San Jose, California, Aug 12, 2007
"This page is an ongoing attempt to survey current efforts directed at understanding and improving interfaces for Personal Information Management (PIM for short), defined as: the collecting and handling of information (such as files, email and contacts) by an individual, for that individual's own use."
Prem Melville and Raymond J. Mooney and Ramadass Nagarajan. Content-Boosted Collaborative Filtering for Improved Recommendations. Proceedings of the Eighteenth National Conference on Artificial Intelligence(AAAI-2002),
pp. 187-192, Edmonton, Canada, July 2002
Bayesian Networks are probabilistic structured representations of domains which have been applied to monitoring and manipulating cause and effects for modelled systems as disparate as the weather, disease and mobile telecommunications networks. Although useful, Bayesian Networks are notoriously difficult to build accurately and efficiently which has somewhat limited their application to real world problems. Ontologies are also a structured representation of knowledge, encoding facts and rules about a given domain. This paper outlines an approach to harness the knowledge and inference capabilities inherent in an ontology model to automate the construction of Bayesian Networks to accurately represent a domain of interest. The approach was implemented in the context of an adaptive, self-configuring network management system in the telecommunications domain. In this system, the ontology model has the dual function of knowledge repository and facilitator of automated workflows and the generated BN serves to monitor effects of management activity, forming part of a feedback look for self-configuration decisions and tasks.
Our in intention is to construct a repository that will allow us empirical research within our community by facilitating (1)better reproducibility of results, and (2) better comparisons among competing approach. Both of these are required to measure progress on problems that are commonly agreed upon, such as inference and learning
Incorporating Evidence in Bayesian Networks with the Select Operator - all 4 versions »
CJ Butz, F Fang - Advances in Artificial Intelligence: 18th Conference of the …, 2005 - books.google.com
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.