Re: [ontolog-forum] Combining Machine Learning, Ontologies, Semantic Net

[Ali SH <asaegyn+out@xxxxxxxxx>]

And in case anyone is interested in slightly meatier descriptions:

http://research.microsoft.com/en-us/um/people/horvitz/www_2012_behavioral_dynamics.pdf

Modeling and Predicting Behavioral Dynamics on the Web

Kira Radinsky, Krysta Svore, Susan Dumais, Jaime Teevan, Alex Bocharov, Eric Horvitz

ABSTRACT User behavior on the Web changes over time. For example, the queries that people issue to search engines, and the underlying informational goals behind the queries vary over time. In this paper, we examine how to model and predict user behavior over time. We develop a temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends. We also explore other dynamics of Web behaviors, such as the detection of periodicities and surprises. We develop a learning procedure that can be used to construct models of users' activities based on features of current and historical behaviors. The results of experiments indicate that by using our framework to predict user behavior, we can achieve signicant improvements in predictio compared to baseline models that weight historical evidence the same for all queries. We also develop a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models. Our improved temporal modeling of user behavior can be used to enhance query suggestions, crawling policies, and result ranking

and

http://www.technion.ac.il/~kirar/files/radinsky12a_causality_jair.pdf

Learning to Predict from Textual Data

Kira Radinsky, Sagie Davidovich, Shaul Markovitch 

Abstract Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predened causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans

On Tue, Feb 5, 2013 at 1:35 PM, Ali SH <asaegyn+out@xxxxxxxxx> wrote:

This is a cool little project:

http://www.technologyreview.com/news/510191/software-predicts-tomorrows-news-by-analyzing-todays-and-yesterdays/

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...

The system provides striking results when tested on historical data. For example, reports of droughts in Angola in 2006 triggered a warning about possible cholera outbreaks in the country, because previous events had taught the system that cholera outbreaks were more likely in years following droughts. A second warning about cholera in Angola was triggered by news reports of large storms in Africa in early 2007; less than a week later, reports appeared that cholera had become established. In similar tests involving forecasts of disease, violence, and a significant numbers of deaths, the system’s warnings were correct between 70 to 90 percent of the time.

...

The system was built using 22 years of New York Times archives, from 1986 to 2007, but it also draws on data from the Web to learn about what leads up to major news events.

“One source we found useful was DBpedia, which is a structured form of the information inside Wikipedia constructed using crowdsourcing,” says Radinsky. “We can understand, or see, the location of the places in the news articles, how much money people earn there, and even information about politics.” Other sources included WordNet, which helps software understand the meaning of words, and OpenCyc, a database of common knowledge.

...

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Best,

Ali

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