Group items tagged

Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the "logic of science"; it is the instrument of risk-taking; it is the applied tools of epistemology; you can't be a modern intellectual and not think probabilistically-but... let's not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let's face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).

The present research analyses the 'social visualization' tool Sense.us, a commercial interactive Web application in which U.S. Census data are visualized. Sense.us was developed as a tool for social data exploration and interaction, in which it would be worthwhile to pay attention to the socio-cultural values that have driven the collection and categorization of the underlying U.S. Census datasets. It is argued that closer attention to value driven U.S. Census statistics would greatly enhance the social appeal of Sense.us, and would be a logical next step in the development of online social visualization tools. In order to allow for explicit socio-cultural values of statistics in online visualizations, three strategies are offered: pro-active annotation; more attention to visual aesthetics; and, a tighter integration of user profiles and represented data.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

A tag cloud is a list of words in different sizes and colors, with or without a sense of depth (3D), meant to represent the statistical importance of keywords mentioned in a particular document base (a blog, a website, twitter,…). It serves as an indicator of the relative importance of the use of certain ideas in the document base at hand. It is a bottom-up, very fuzzy method for the synthesis of knowledge from an arbitrarily big aggregate of (text) data. Because it rests entirely on statistics, very often there is absolutely no relationships between the keywords of a tag cloud. Worse even, if they existed (by pure chance), there is absolutely no way of finding out about the meaning of those relationships.

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

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

BioJava is an open-source project dedicated to providing a Java framework for processing biological data. It includes objects for manipulating biological sequences, file parsers, DAS client and server support, access to BioSQL and Ensembl databases, tools for making sequence analysis GUIs and powerful analysis and statistical routines including a dynamic programming toolkit.

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 YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation. We have already harvested knowledge about millions of entities and facts about their relationships, from Wikipedia and WordNet with careful integration of these two sources. The resulting knowledge base, coined YAGO, has very high precision and is freely available. The facts are represented as RDF triples, and we have developed methods and prototype systems for querying, ranking, and exploring knowledge. Our search engine NAGA provides ranked answers to queries based on statistical models.

The goal of this project is to develop a Python tool to make an in-depth quantitative analysis about Wikipedia, generating graphics and statistical results for each language version of Wikipedia.