Director’s End of the Academic Year Talk

About the Appearance of Chairs during the Disappearance of Bikes—Space, Time, Photography, and a bit of Drama

 

Krzysztof Janowicz

Center for Spatial Studies and Department of Geography

University of California Santa Barbara

Photo of Krzysztof Janowicz

Wednesday, June 16, 2021

Abstract. As director of UCSB’s Center for Spatial Studies, my role is largely to moderate, facilitate, and provide an overall vision for our research. With the academic year coming to an end, I would like to use this opportunity to present some personal thoughts on spatial thinking, semantics, and theories of categorization by sharing a campus photography project during COVID in an interactive and, hopefully, cheerful style. Overall, the talk will be more about photography and society as compared to geoinformatics (in a narrower sense).

Google Earth Web (and KML) was used for an optional geographic discovery game. 

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Occasional Events

An archive of events that are not annual occurrences.

  • Dangermond Lectures: Annual lectures delivered in the Department of Geography colloquium series by an internationally outstanding researcher in geographic information science, supported through a gift from Jack and Laura Dangermond. (Includes videos archived since 2016)
  • GIS Days:  As part of Geography Awareness Week, the annual GIS Day enables GIS users and researchers to exchange ideas and present their work to a wide audience.
  • Golledge Lectures: The Reginald Golledge Distinguished Lecture in Geography was instituted in 1984, when Prof. Reginald Golledge lost his sight. Although Reg passed away in May 2009, the Golledge Distinguished Lecture continues in his honor as part of the Department of Geography colloquium series.
  • Hosted Conferences: Conferences hosted by the UCSB Center for Spatial Studies.
  • Spatial Unconferences: Outreach to domain specialists and applications of spatial information as a primary force to push the frontiers of Geographic Information Science, Spatial Cognition, and related fields.

NSF Convergence Accelerator Series Tracks A&B: Carole Goble

FAIRy Stories: The FAIR Data Principles in Theory and in Practice

Carole Goble

University of Manchester

Wednesday, May 19, 2021. 9:00 a.m. (PT)

Required Zoom Registration: here.

Upon registration, you will receive a confirmation email with Zoom login details

The National Science Foundation’s (NSF) tracks A and B of the Convergence Accelerator program is proud to present Carole Goble 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. The “FAIR Guiding Principles for scientific data management and stewardship” [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the armies of authors). Over the past 5 years, FAIR has become a movement, a mantra, and a methodology for scientific research and, increasingly, in the commercial and public sector. FAIR is now part of NIH, the European Commission, and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need.”

As a data infrastructure wrangler, I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-led approaches like Schema.org; work with big pharma on specialized FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches.

In this talk, I will use some of these projects to shine some light on the FAIR movement. Spoiler alert: Although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role—not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.”

[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18.

Bio: Carole Goble is a Professor of Computer Science at the University of Manchester, UK, where she leads a team of Researchers, Research Software Engineers, and Data Stewards. She has spent 25 years working in e-Science on reproducible science, open data and method sharing, knowledge and metadata management and computational workflows in a range of disciplines, and has led many scientific and e-Infrastructure projects and resources at the national and European level. She was an early pioneer of semantic web and linked data approaches in the Life Sciences.

Goble is extensively involved in ELIXIR, the pan-national European Research Infrastructures for Life Science data, and the European Open Science Cloud. She is a co-founder of the UK’s Software Sustainability Institute, coordinates the FAIRDOM infrastructure for research project assets management, and leads work at the international level on FAIR Research Objects and workflows. An advocate of FAIR and Open Data, she serves as the UK representative on the G7 Open Science Working Group and is one of the authors of the original FAIR data principles paper.

Please contact us for follow-up questions.

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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).

Please contact us for follow-up questions.

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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%.