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Events for October 23, 2015
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AI SEMINAR
Fri, Oct 23, 2015 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Mohsen Taheriyan, Ph.D at USC
Talk Title: Learning the Semantics of Structured Data Sources
Series: AI Seminar
Abstract: Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data, however, they rarely provide a semantic model to describe their contents. Semantic models of data sources capture the intended meaning of data sources by mapping them to the concepts and relationships defined by a domain ontology. Such models are the key ingredients to automate many tasks such as source discovery, data integration, and publishing semantic content on the Web. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the effort to automatically build semantic models is focused on labeling the data fields (source attributes) with ontology classes and/or properties, e.g., annotating the first column of a table with the class Person and the second one with the class Movie. However, a precise semantic model needs to explicitly represent the relationships between the attributes in addition to their semantic types, e.g., stating that the person is the director of the movie. Automatically constructing such precise models is a difficult task. In this talk, I present a novel approach that exploits the knowledge from a domain ontology, the semantic models of previously modeled sources, and the vast amount of data available in the Linked Open Data (LOD) cloud to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input.
Biography: Mohsen Taheriyan is a newly graduated PhD from the University of Southern California. He worked at Information Integration Group at ISI on learning the semantics of structured data sources. His research focus is applying Semantic Web technologies and AI techniques to understand the meaning of data. He received his B.S. in Computer Engineering from University of Tehran and his M.S. in Software Engineering from Sharif University of Technology.
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=500df65b10044d08837b95ecc188eecf1dLocation: Information Science Institute (ISI) - 1135 - 11th fl Large CR
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=500df65b10044d08837b95ecc188eecf1d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar: Fine Grained Temporal Patterns of Online Content Consumption
Fri, Oct 23, 2015 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Farshad Kooti, USC/ISI
Talk Title: Fine Grained Temporal Patterns of Online Content Consumption
Series: Natural Language Seminar
Abstract: Online activity is characterized by diurnal and weekly patterns, reflecting human circadian rhythms, sleep cycles, and social patterns of work and leisure. Using data from online social networking site Facebook, we uncover temporal patterns that take place at far shorter time scales. Specifically, we demonstrate fine-grained, within-session behavioral changes, where a session is defined as a period of time a user engages with Facebook before choosing to take a break. We show that over the course of a session, users spend less time consuming some types of content, such as textual posts, and preferentially consume more photos and videos. Moreover, users who spend more time engaging with Facebook have different patterns of session activity than the less-engaged users, a distinction that is already visible at the start of the session. We study activity patterns with respect to users demographic characteristics, such as age and gender, and show that age has a strong impact on within-session behavioral changes. Finally, we show that the temporal patterns we uncover help us more accurately predict the length of sessions on Facebook.
Biography: I am a third-year Computer Science PhD student at the University of Southern California USC, Information Sciences Institute ISI working under the supervision of Kristina Lerman. My main research interest is the study of large and complex datasets, especially data from online social networks, which includes the measurement and analysis of users' behavior in OSNs. I'm currently a Data Science intern at Facebook in Menlo Park. Before joining USC, I got my master's from Max Planck Institute for Software Systems MPI SWS, Germany. I worked with Krishna Gummadi as my advisor and also with Meeyoung Cha KAIST and Winter Mason Facebook during my master's. Before MPI, I got my bachelor's in Computer Engineering Software from University of Tehran, Iran.
Host: Nima Pourdamghani and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
