Keynote Speakers at ISWC 2024
The Keynote Speakers at ISWC 2024 are Ioana Manolescu, Chaitan Baru, and Ora Lassila. Find the talks’ abstracts and the speakers’ bios below.
Ioana Manolescu
Ioana Manolescu is a Senior Researcher at Inria, the national French Institute of Research in Computer Science and Applied Mathematics, and a part-time professor at Ecole Polytechnique, France’s leading engineering school. Her main research interests are data integration, semistructured data management, and DB+AI methods and tools for data journalism and journalistic fact-checking. She has co-authored 170 articles and several books and is a recipient of the ACM SIGMOD 2020 Contribution Award. She has been a member of the SIGMOD Executive Committee and of the PVLDB Endowment Board, an Editor-in-chief of the SIGMOD Record, and a general chair of the ICDE conference.
Retrieve (and Leverage) the Inner Graph Behind the Data
Many challenging data integration problems, in particular in data journalism, feature heterogeneity at the level of the schema and the data model. To overcome the heterogeneity, we have shown how data of many (semi)structured models can be converted in fine-granularity graphs, enriched and densified with the help of information extraction. Such fine-grained graphs, however, are hard to grasp for non-technical users. To help them get acquainted with a dataset, we devised an abstraction method, which identifies, in fine-granularity data graphs, structured objects endowed with an internal structure, and relationships between them. Given a semistructured dataset, we automatically produce an Entity-Relationship style diagram; in contrast with traditional E-R models, our entities may feature deep nesting, reflecting the nested and possibly recursive structure present in some data models. We thus obtain an automated way of “rescuing” the conceptual model, which we argue is best viewed as a graph, behind any application dataset. We then describe automatic techniques for finding the most interesting paths connecting entities in a dataset.
Web sites:
https://connectionstudio.inria.fr/
https://team.inria.fr/cedar/projects/abstra/
Joint work with Nelly Barret, Madhulika Mohanty, Prajna Upadhyay and many other colleagues
Chaitan Baru
Dr. Chaitanya Baru is Senior Advisor in NSF’s new Technology, Innovation, and Partnerships (TIP) Directorate. He joined NSF in October 2022 after a 25-year career at the San Diego Supercomputer Center (SDSC), University of California, San Diego where his work centered on translational and applied research in computer science and data science.
From 2014-2018, he was on assignment at NSF as Senior Advisor for Data Science in the Computer and Information Science and Engineering Directorate, where he co-chaired the NSF Harnessing the Data Revolution Big Idea (HDR) and helped draft the HDR roadmap, which included the idea for an Open Knowledge Network. He provided guidance to NSF’s BIGDATA program. and was instrumental in establishing the first partnership between NSF and AWS, Google, Microsoft Azure, and IBM. He helped initiate the HDR Data Science Corps program and served as an advisor to the NSF Big Data Regional Innovations Hubs and TRIPODS programs. From 2019–2021, he returned on assignment to NSF as Senior Advisor to the Convergence Accelerator program and helped establish Track A of the program on Open Knowledge Network (OKN). Prior to joining SDSC, he held positions at IBM in the Database Group and was on the EECS faculty at the University of Michigan, Ann Arbor. Baru has an ME and PhD in Electrical Engineering from the University of Florida and a BTech in Electronics Engineering from the Indian Institute of Technology, Madras.
Knowledge as Infrastructure – and a National Research Resource
We are amid three revolutions in AI: deep learning, knowledge graphs, and automated reasoning, as articulated by Prof. Kenneth Forbus during the AI Session at the National Science Board Meeting on May 1, 2024. Technology in each of these areas has progressed to the extent that it is used daily and is delivering significant value. While deep learning, which has unleashed powerful tools like ChatGPT is a large focus of today’s AI, knowledge graphs and reasoning are complements to deep learning. Knowledge graphs have already been powering key web and e-commerce services provided by large corporations. Automated reasoning techniques are in wide use in many areas—for example, in the auto industry to find flaws in its electrical systems and verify behavior with respect to design specifications.
In this talk, we will discuss techniques, technologies, and strategies for building out the knowledge infrastructure that will be essential and necessary to support the next generation of AI. The National Science Foundation’s Open Knowledge Network (OKN), envisioned as part of NSFs Harnessing the Data Revolution Big Idea, provides an important start. The Prototype-OKN (https://www.proto-okn.net/) is a coordinated effort among 18 projects funded under the NSF Proto-OKN project, in collaboration with NASA, NIH, NIJ, NOAA, USGS and other agencies, to create an open knowledge network structure linking disparate, heterogenous information from diverse sources. Of these, 15 projects are “Theme 1” efforts focused on providing solutions for specific problems working in conjunction with federal, state or other government agencies. Two projects—FRINK and SPIDER—are working towards providing a common Proto-OKN “fabric”, or technical infrastructure, to deploy Theme 1 solutions, and one project—EduGate—focusing on the education, training, and outreach for this effort.
Knowledge representation and reasoning are not new topics in Computer Science. However, to make translational impact in real-world applications we need to make a shift towards treating the development and operation of computer science-based knowledge structures as infrastructure—in addition to just bespoke solutions to specific problems. This would begin with the hosting of repositories of knowledge in computational form based on open-source government, science, and other open data. The term “translational” implies focus on use-inspired, solution-oriented R&D efforts with the potential to directly impact people’s everyday lives. An important focus is on education and training and teaching of knowledge representation at every level from high school to computer science undergraduates, and above. A key consideration is to pay attention to issues from governance to ethics to help build trust in the system.
Ora Lassila
Dr. Ora Lassila is a Principal Technologist in the Amazon Neptune graph database team and the co-chair of the W3C RDF-star Working Group. He was one of the creators of the original Semantic Web vision and was a co-author of the original RDF specification. His main interest is to make people understand that sharing data is not just about sharing the physical bits but also sharing the meaning of those bits, and he sees ontologies and knowledge graphs as a means to do that. He is also interested in improving the alignment between RDF graphs and Labeled Property Graphs.
Prior to joining Amazon, Ora held many positions and a variety of roles both in industry and academia. He served as an elected member of the W3C Advisory Board, and has been a member of several other advisory boards, program committees, and other groups mainly related to the Semantic Web. He helped get the ISWC conference series started in 2001 and subsequently served on the board of the Semantic Web Science Association.
Ora’s KR software flew onboard NASA’s Deep Space 1, a probe that passed the Asteroid Belt in 1999. In 1989, he was the Grand Prize Winner of the Usenix Obfuscated C Code Contest. He earned his doctorate in CS from the Helsinki University of Technology. His daughters do not think he is a real doctor, the kind who helps people.
Semantic Web and AI: Can we finally realize the original vision?
The original idea of the Semantic Web was not just to bring knowledge representation and reasoning to the Web but to make these technologies pervasive in our use of information technology. The idea has matured into modern enterprise knowledge graphs and – according to Gartner – has become a major mainstream trend. Organizations are using knowledge graphs without feeling the need to advertise this fact. However, getting here took a long time,” the original Semantic Web vision was introduced at the tail end of the so-called “AI winter”, and the close relationship with AI greatly slowed its adoption. Now, given the popularity of generative AI, we should re-examine the vision and understand what is the role of AI in the Semantic Web, and what is the role of the Semantic Web in AI.
In this talk I will reflect on the more than a quarter century of work on building the technologies for the Semantic Web and how this work still continues. I will discuss the “wins”, the “losses”, the lessons learned, and describe how I see the future. I believe we are now ready for the next step: to finally realize the original Semantic Web vision, that of agents, planning, and reasoning.