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Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.

The prototype we describe is a biomedical knowledge base, constructed for a demonstration at Banff WWW2007 , that integrates 15 distinct data sources using currently available Semantic Web technologies such as the W3C standard Web Ontology Language [OWL] and Resource Description Framework [RDF]. This report outlines which resources were integrated, how the knowledge base was constructed using free and open source triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL [SPARQL], and what resources and inferences are involved in answering complex queries. While the utility of the knowledge base is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: * Collective classification * Link prediction * Entity resolution * Social network modeling * Information extraction

In order to keep data structures in Semantic CrunchBase close to the source API, I used a 1-to-1 mapping between CrunchBase JSON keys and RDF terms (with only a few exceptions). This was helpful for people knowing the JSON API, but it wasn't easy to interlink the converted information with existing SemWeb data such as FOAF, or the various LOD sources.

Many RDF toolkits provide SPARQL/Update functionality and move from simple query operations to more powerful data manipulation. SPARQLScript goes another step further and enables or simplifies the implementation of * semantic Mashups, * custom, portable rule and inference scripts using a SPARQL-based syntax * Output templates for RDF data and SPARQL query results * RDF/SPARQL-driven Yahoo! Pipes-like systems

PHYLIP is a free package of programs for inferring phylogenies. It is distributed as source code, documentation files, and a number of different types of executables.

Integrate. Relate. Infer. Share. IRIS is a semantic desktop application framework that enables users to create a "personal map" across their office-related information objects. IRIS includes a machine-learning platform to help automate this process. It provides "dashboard" views, contextual navigation, and relationship-based structure across an extensible suite of office applications, including a calendar, web and file browser, e-mail client, and instant messaging client.

Cyberinfrastructure for Phylogenetic Research (CIPRES) project is an open collaboration funded by the National Science Foundation. The group is led by Tandy Warnow and involves researchers (biologists, computer scientists, statisticians, and mathematicians) at sixteen institutions. The goal of the CIPRES project is to enable large-scale phylogenetic reconstructions on a scale that will enable analyses of huge data sets containing hundreds of thousands of bio molecular sequences. To achieve this goal we have brought together a group of researchers involved in phylogeny estimation, statistics, and computer science to create new solutions for the difficult computational problems that arise in inferring evolutionary relationships.

The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced "lark"), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web.