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Pascal Hitzler
Pascal Hitzler is a prominent computer scientist specializing in Semantic Web and Artificial Intelligence. He currently holds the Lloyd T. Smith Creativity in Engineering Chair at Kansas State University, where he serves as the Director of the Center for Artificial Intelligence and Data Science (CAIDS) and Co-Director of the Institute for Digital Agriculture and Advanced Analytics (ID3A).12
Hitzler's academic background includes a Ph.D. in Mathematics from the National University of Ireland, University College Cork, completed in 2001, and a Diplom (equivalent to a Master's degree) in Mathematics from the University of Tübingen in Germany, obtained in 1998.12
His research is extensive, with over 400 publications covering diverse areas such as semantic web, artificial intelligence, neurosymbolic integration, knowledge representation and reasoning, machine learning, and set-theoretic topology.1 Hitzler is also the founding Editor-in-chief of the Semantic Web journal and the Neurosymbolic Artificial Intelligence journal.1
Some of Hitzler's notable achievements include:
- Co-authoring the W3C Recommendation OWL 2 Primer.12
- Writing "Foundations of Semantic Web Technologies," which was recognized as an Outstanding Academic Title in 2010.12
- Being listed among the top 1% of scientists worldwide in Artificial Intelligence & Image Processing in 2020.2
- Receiving the SWSA Ten-Year Award at the 2020 International Semantic Web Conference.2
Prior to his current position, Hitzler held roles at Wright State University, the University of Karlsruhe, and TU Dresden.12 He has been active in organizing academic conferences and workshops, including the Neural-Symbolic Learning and Reasoning Workshop and the U.S. Semantic Technologies Symposium.2
As of February 2025, Hitzler continues to lead research and development in artificial intelligence and data science at Kansas State University.13
Highlights
Very preliminary announcement, 19th Conference on Neural-Symbolic Learning and Reasoning, NeSy 2025, Santa Cruz, CA, 8-10 September 2025 https://lnkd.in/gzNH-cPP Leilani Gilpin Artur d'Avila Garcez Tarek R. Besold
Call for Papers Large Language Models for Knowledge Graph and Ontology Engineering 2024 AAAI Fall Symposium
Large Language Models (LLMs) and Knowledge Graphs (KGs) are highly trending. The interplay between these two technologies can go both ways, but the two directions are quite different in approach. This symposium specifically focuses on how LLMs can be used as tools to augment the extant capacity for ontology and knowledge graph engineering. Knowledge Graph Engineering (KGE) and Ontology Engineering (OE – together KG/OE) challenges in particular have to do with the (to date still) high involvement of humans and human expert in the KG/OE life cycle, including creation/modeling, alignment, evolution, reusability (from both ontological commitment and accessibility perspectives). The KG/OE communities have made steady progress in the past 20 years, but only now with LLMs, key KG/OE challenges appear to become addressable at scale. The goal of this symposium to focus and coordinate research. We wish to create a space and foundational community for the sharing of ideas for prompt engineering, fine-tuning, neurosymbolic approaches, quality control, and human-in-the-loop methods: all with LLMs for OE/KGE.
Topics include, but are not limited to: LLMs for Knowledge Graph and Ontology Creation LLMs for Ontology and Entity Mapping LLMs for Knowledge Graph and Ontology Evolution LLMs for Knowledge Graph access and use LLMs as Natural Language Interfaces for Knowledge Graphs and Ontologies
Format of the Symposium: The program will consist of presentations of accepted full papers, posters, lightning talks, keynotes, and significant time for panel and plenary discussions.
Submission of papers: Full papers (for oral presentation): 8-10 pages (not counting references). Short papers (for poster presentation): 3-4 pages (not counting references). Lightning talks (for brief spotlight presentation): 1-2 pages extended abstract (not counting references).
Submissions are to be made via the official AAAI Symposium Easychair submission portal at https://lnkd.in/g8NSn_bZ. You must choose from the appropriate symposia from the available tracks.
Deadline for submission of papers: July 31, 2024.
Symposium Committee: Pascal Hitzler, Kansas State University, USA ([email protected]) Andrea Nuzzolese, CNR, Italy ([email protected]) Catia Pesquita, Universidade de Lisboa, Portugal ([email protected]) Cogan Shimizu, Wright State University ([email protected])
For more information, see https://lnkd.in/gk9AN-Yj
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Mesh makes it much easier to stay in touch with the people you care about. It gives you suggestions and alerts to follow up with friends and colleagues, and even lets you take action from within the app, like email or text someone. Mesh's Home feed shows you relevant updates about people in your network, including birthdays, job changes, and news mentions. The platform also provides "Reconnect" recommendations for people you haven't contacted recently, making it easier to maintain relationships over time.
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Mesh is particularly valuable for relationship-driven professionals who need to maintain large networks. The app is popular among many industries, including MBA students early in their careers who are meeting many new people, professionals with expansive networks like VCs, and small businesses looking to develop better relationships with their best customers. Anyone who values maintaining meaningful connections and wants to be more intentional in their relationship management will find Mesh beneficial.
Mesh offers competitive pricing in the personal CRM market. Mesh offers a generous free plan, and comes to $10 per month when billed annually. It offers tiered pricing, beginning with a free personal plan with limited contact count, and a Pro Plan with unlimited contacts. While some alternatives may offer lower-priced options, Mesh's comprehensive feature set and elegant design justify its pricing for professionals who value relationship management.
Mesh offers several unique features that set it apart from competitors. Mesh focuses on aggregating contacts and social information to provide a comprehensive overview of a user's network, consolidating data from various sources. Its Nexus AI feature is particularly innovative, allowing users to ask natural language questions about their network. Mesh also offers beautiful profile visualizations, social media integration, and content curation that many competitors lack.
Yes. Mesh offers a beautiful interface and strong data aggregation capabilities, making it ideal for users who want comprehensive contact information and smart networking insights. Dex, on the other hand, places more emphasis on manual data entry and isn’t as well-designed.
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