NSF Convergence Accelerator Series Tracks A&B: Natasha Noy

NSF Convergence Accelerator Tracks A&B Speaker Series

 

Google Dataset Search: Building an Open Ecosystem for Dataset Discovery

Natasha Noy

Google Research

Thursday, March 18, 2021. 9:00 a.m. (PT)

The recorded video of this talk can be found here.

 

The National Science Foundation’s (NSF) tracks A and B of the Convergence Accelerator program are proud to present Natasha Noy in its 2021/2022 speaker series on Open Knowledge Networks. The series features researchers and practitioners widely recognized for their contribution to knowledge graphs, knowledge engineering, and FAIR data.

Abstract. There are thousands of data repositories on the Web, providing access to millions of datasets. National and regional governments, scientific publishers and consortia, commercial data providers, and others publish data for fields ranging from social science to life science to high-energy physics to climate science and more. Access to this data is critical to facilitating reproducibility of research results, enabling scientists to build on others’ work, and providing data journalists easier access to information and its provenance. In this talk, we will discuss recently launched Dataset Search by Google, which provides search capabilities over potentially all dataset repositories on the Web. I will talk about the open ecosystem for describing datasets that we hope to encourage.

Bio: Natasha Noy is a senior staff scientist at Google Research where she works on making structured data accessible and useful. She leads the team building Dataset Search, a search engine for all the datasets on the Web. Prior to joining Google, Noy worked at Stanford Center for Biomedical Informatics Research where she made major contributions in the areas of ontology development and alignment, and collaborative ontology engineering. Noy is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI). She served as president of the Semantic Web Science Association from 2011 to 2017.

Please contact us for follow-up questions.

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NSF Convergence Accelerator Series Tracks A&B: Denny Vrandečić

NSF Convergence Accelerator Tracks A&B Speaker Series

 

Knowledge beyond the Graph: Toward a Multilingual Wikipedia

 

Denny Vrandečić

Wikimedia Foundation

Thursday, Feb 11, 2021. 9:00 a.m. (PT)

The recorded video of this talk can be found here.

 

The National Science Foundation’s (NSF) tracks A and B of the Convergence Accelerator program are proud to present the next speaker in its 2021/22 speaker series on Open Knowledge Networks. The series will feature researchers and practitioners widely recognized for their contribution to knowledge graphs, knowledge engineering, and FAIR data

Abstract. Wikipedia’s vision is a world in which everyone can share in the sum of all knowledge. In its first two decades, this vision has been very unevenly achieved. One of the largest hindrances is the sheer number of languages Wikipedia needs to cover in order to achieve that goal. We argue that we need a new approach to tackle this problem more effectively, a multilingual Wikipedia where content can be shared between language editions.

We have started a new project where we separate this goal into two parts: creating and maintaining content in an abstract notation within a project called Abstract Wikipedia, and creating a new project called Wikifunctions that can translate this notation to natural language. Both parts are fully owned and maintained by the community. This architecture will make more encyclopedic content available to more people in their own language, and at the same time allow more people to contribute knowledge and reach more people with their contributions, no matter what their respective language backgrounds.

Bio: Denny Vrandečić has joined Wikimedia Foundation as Head of Special Projects in order to lead the Abstract Wikipedia project. He obtained his Ph.D. from Karlsruhe Institute of Technology (KIT) in 2012 where he co-founded Semantic MediaWiki, and thereafter launched Wikidata at Wikimedia Deutschland. He then joined Google as an ontologist on the Google Knowledge Graph and later worked as a researcher on the topic of knowledge representation.

Please contact us for follow-up questions.

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NSF Convergence Accelerator Series Tracks A&B: Markus Krötzsch

NSF Convergence Accelerator Tracks A&B Speaker Series

 

Knowledge Graphs for AI: Wikidata and Beyond

 

Markus Krötzsch

Technische Universität Dresden

Wednesday, Feb 3, 2021. 9:00 a.m. (PT)

The recorded video of this talk can be found here.

 

The National Science Foundation’s (NSF) tracks A and B of the Convergence Accelerator program are proud to present the first speaker in their 2021/22 speaker series on Open Knowledge Networks. The series will feature researchers and practitioners widely recognized for their contribution to knowledge graphs, knowledge engineering, and FAIR data

Abstract. Wikidata, the knowledge graph of Wikimedia, has successfully grown from an experimental “data wiki” to a well-organized reference knowledge base with a large and active editor community as well as many academic and industrial uses. It is also a key ingredient of popular AI applications, most prominently of intelligent agents such as Apple’s Siri or Amazon’s Alexa. Of course, human knowledge is fully expected to be in high demand in this time of rapidly advancing AI. And yet, the fact that modern AI relies on the manual labor of thousands of human knowledge modelers is in stark contrast to the common narrative of AI in popular media, which tells us that methods of pattern recognition and statistical function approximation can produce intelligent behavior from unstructured data without much human intervention. However, Wikidata is not a singular exception to the trend but rather a specific solution to a general need of AI: the need for knowledge that is understandable to humans and accessible to computers. Almost every major AI application incorporates such knowledge, and organizations long have realized the need to acquire and develop knowledge resources as part of their AI strategy. The next frontier in AI is the ability of systems to explain and justify their behavior. There, too, we can see the need for knowledge-based technologies as a bridge between human understanding and computational mechanisms, but the task goes far beyond the realms of knowledge representation or machine learning, and will require the effort of all of AI and maybe all of computer science. In my talk, I will give an overview of Wikidata and outline some ongoing research efforts that combine knowledge representation with other methods towards the creation of (more) understandable and accountable AI.

Bio: Markus Krötzsch is a full professor at the Faculty of Computer Science of TU Dresden, where he is holding the chair for Knowledge-Based Systems. He obtained his Ph.D. from Karlsruhe Institute of Technology (KIT) in 2010, and thereafter worked at the Department of Computer Science of the University of Oxford until October 2013. He has contributed to the concept and design of Wikidata, as one of the most prominent examples of applied knowledge representation today. His research made many further contributions to the development and analysis of knowledge modeling languages (including the W3C OWL standard), inference methods, and automated reasoners. Krötzsch is a member of the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) and of the Center for Perspicuous Computing (CPEC).

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Spatial Data Science Hangout Series: Call for Speakers

The Center for Spatial Studies will again be hosting a special Spatial Data Hangout on Thursday, December 3 at 10:00 a.m. All grad students are invited to attend! We are fully aware that your schedule is already crowded with  Zoom meetings, but we are hopeful that this will be  an excuse for you to hang out with other geography grads and  learn a bit about the cool projects or ideas they have been working on this year.

This spatial data science hangout will focus on learning and discussing all sorts of graph data (and analytics that involve graphs) used to do research in our department. This includes everything related to representing and reasoning on data. This is  an opportunity for you to: (i) teach others about the tools you use to represent data for spatial reasoning, –this might include some coding, (ii) discuss common graphs for spatial analytics in your research field, (iii) discuss the process you use to create a graph that you are proud of, or (iv) present early-stage ideas, projects that you might not complete yet or learned lessons from “failed” projects. This is meant to be a safe space for people to talk through ideas, and learn from each other.  Some potential topics include spatial networks in their broadest sense, such as: 

  • Social networks
  • Transportation networks 
  • Biological networks
  • Other types of networks in your domain (stream networks)
  • Knowledge graphs

We are looking for folks who would like to lead/co-lead sessions! Depending on the number of speakers, you might be able to participate with a lightning talk or a 15-minute talk. If you are interested in participating in this, please contact Marcela Suárez at amsuarez@ucsb.edu by November 27 (preferably sooner), and otherwise mark your calendar to attend.

Spatial Data Science Hangout Series: November 2019

T next seminar in the Center for Spatial Studies’ Spatial Data Hangouts series will be on Tuesday, 11/19 from 11:30 a.m.12:30 p.m. at 3512 Phelps Hall. All grad students are invited to attend.

Continuing the theme of finding academic employment, where we discuss why and how to apply for a professorship, we will continue last month’s discussion of the academic hiring process and talk about interviews on-site and per teleconference. We will also do at least one test run to give you a chance to practice. Hence, if you would like to volunteer and be interviewed in front of the other students, please let Jano or Karen know.

We will be providing a light lunch after the discussion. Please contact Karen Doehner if you plan to attend.

 

Spatial Data Science Hangout Series: Fall 2019

Spatial Data Science Hangouts Poster

After a successful first run in the last academic year, the Center for Spatial Studies will again be hosting the Spatial Data Hangouts, with the first one on Thursday, 10/17 from 11:30 a.m.12:30 p.m. at 3512 Phelps Hall. All grad students are invited to attend.

With the season for academic jobs starting, the next few spatial data science hangouts will be used to to discuss why and how to apply for a professorship, eg., how to write your cover letters, what makes a good recommendation letter, how to structure your CV, how to score during the on-site interviews and your talk, how to negotiate, and so on.

We will focus on jobs in spatial data science, GIScience, remote sensing, spatial cognition, and so on, but most of what we will discuss applies to academic employment in general. We will do all this in a hands-on, interactive style.

We will be providing a light lunch after the discussion. Please contact Karen Doehner if you plan to attend.

 

Spatial Center Receives NSF Grant

Center for Spatial Studies at the University of California, Santa Barbara participating in NSF C-Accel Pilot

View the complete news release at: https://www.news.ucsb.edu/2019/019651/breaking-data-out-silos

The Center of Spatial Studies at the University of California, Santa Barbara is receiving research funding under the Open Knowledge Network track of the new Convergence Accelerator Pilot (C-Accel) by the National Science Foundation (NSF). Prof. Krzysztof Janowicz leads a diverse team of partners from academia, industry, and federal agencies. The team will develop Artificial Intelligence based models, methods, and services for representing,  retrieving, linking, and predicting spatial and temporal data from a highly diverse set of public knowledge graphs that range across topics such as soil health and the historic slave trade. 

This new NSF Convergence Accelerator Pilot program is set to “bring teams together to focus on grand challenges of national importance that require a convergence approach […] and have a high probability of resulting in deliverables that will benefit society within a fixed term.” NSF is funding several teams under this program in an effort that will lead to the development of public knowledge graphs which in turn have “the potential to drive innovation across all areas of science and engineering, and unleash the power of data and artificial intelligence to achieve scientific discovery and economic growth.” The funding program is highly competitive and had an acceptance rate of only 8.5%.

2019 Spatial Data Science Symposium

Spatial Data Science Symposium

“Setting the Spatial Data Science Agenda”

December 9–11, 2019

Upham Hotel (https://www.uphamhotel.com/)

Santa Barbara, California

Visit the official SDSS site

Motivation

Space and time matter not only for the obvious reason that everything happens somewhere and at some time, but because knowing where and when things happen is critical to understanding why and how they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of spatial data. It generalizes and unifies research from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, and fosters the application of methods developed in these fields to outside disciplines ranging from the social to the physical sciences. In doing so, research on spatial data science must address a variety of new challenges that relate to the diversity of the utilized data and the underlying conceptual models from various domains, the opportunistic reuse of existing data, the scalability of its methods, the support of users not familiar with the language and methods of traditional geographic information systems, the reproducibility of its results that are often generated by complex chains of methods, the uncertainty arising from the use of its methods and data, the visualization of complex spatiotemporal processes and data about them, and, finally, the data collection, analysis, and visualization playing out in near real-time. Spatial data science does not only utilize advanced techniques from fields such as machine learning or big data storage and retrieval, but it also contributes back to them. Recent work, for instance, has shown that spatially-explicit machine learning methods substantially outperform more general data when applied to spatial data even though this spatial component may seem of secondary importance at first glance.

Goals

Instead of being restricted by a historically grown partition into small and overlapping communities that deal with spatial data in one way or the other, the overarching goal of this symposium is to put spatial data science at the forefront of a unified field that explores the current research and application landscape to define an agenda for spatial data science for the next 10 years.

Means

Forty-three experts from academia and industry convened to share and develop visions, insights, and best practices. Plenary presentations and intense exchanges in small breakout discussion groups offered opportunities for knowledge transfer.

2019 Spatial Data Science Symposium

Spatial Data Science Symposium

“Setting the Spatial Data Science Agenda”

December 9–11, 2019

 

Upham Hotel (https://www.uphamhotel.com/)

Santa Barbara, California

Motivation

Space and time matter not only for the obvious reason that everything happens somewhere and at some time, but because knowing where and when things happen is critical to understanding why and how they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of spatial data. It generalizes and unifies research from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, and fosters the application of methods developed in these fields to outside disciplines ranging from the social to the physical sciences. In doing so, research on spatial data science must  address a variety of new challenges that relate to the diversity of the utilized data and the underlying conceptual models from various domains, the opportunistic reuse of existing data, the scalability of its methods, the support of users not familiar with the language and methods of traditional geographic information systems, the reproducibility of its results that are often generated by complex chains of methods, the uncertainty arising from the use of its methods and data, the visualization of complex spatiotemporal processes and data about them, and, finally, the data collection, analysis, and visualization playing out in near real-time. Spatial data science does not only utilize advanced techniques from fields such as machine learning or big data storage and retrieval, but it also contributes back to them. Recent work, for instance, has shown that spatially-explicit machine learning methods substantially outperform more general data when applied to spatial data even though this spatial component may seem of secondary importance at first glance.

Co-sponsored by Esri, the Center for Spatial Studies at the University of California, Santa Barbara is hosting a symposium entitled “Setting the Spatial Data Science Agenda.” The meeting will bring together academic and industry representatives from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, with interest in setting an interdisciplinary research agenda to advance spatial data science methods and practice, both from scientific and engineering viewpoints. We also invite experts from related fields and those that are producers or users of spatial data in the social and physical sciences.

Goals

Instead of being restricted by a historically grown partition into small and overlapping communities that deal with spatial data in one way or the other, the overarching goal of this symposium is to put spatial data science at the forefront of a unified field that explores the current research and application landscape to define an agenda for spatial data science for the next 10 years.

Means 

About 35 invited and funded experts from academia and industry will convene to share and develop visions, insights, and best practices. Plenary presentations and intense exchanges in small breakout discussion groups offer opportunities for knowledge transfer.

Call for Applications 

To apply, please submit a one-page, paragraph-style bio with a photograph and a short two-page position paper (in PDF format), discussing your perspective on the subject by August 23, 2019. Participants will be selected by the organizing committee and notified of their acceptance by September 9. Our goal is to achieve a balance of participants from a variety of disciplines and from different career levels. Hence, we especially encourage early-career (including graduate students) participants from both the industry and academia to apply. We will cover the full expense of accommodations and reimburse travel expenses up to $1,200 for international participants and $700 for domestic. 

The meeting will be held at the Upham Hotel in downtown Santa Barbara on Dec. 9–11; suggested travel days are Dec. 8 and the afternoon of Dec. 11.

Please see http://spatial.ucsb.edu for more information. 

Submit your application directly to Karen Doehner <kdoehner@spatial.ucsb.edu>.

Please feel free to contact Krzysztof Janowicz <janowicz@ucsb.edu> if you have questions about the event or the call for applications.