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--- aira:start [2025/12/03 10:45] – [Schedule Autumn 2025] mtm
+++ aira:start [2025/12/04 16:30] (current) – [2025-12-04] mzk
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 ===== Schedule Autumn 2025 =====
+  * **[RPHD TRACK] 2025.12.11**: Anna Sofia Lippolis, PhD Candidate @ University of Bologna, [[#section20251211|Enhancing Knowledge Engineering with LLMs.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/35848296892369?p=0t3yTogmgGOsSwYeDO|MS Teams]]
+    * Recording:  TDA
+    * Presentation slides:  TDA
   * **[PHD TRACK] 2025.12.04**: Bartłomiej Małkus, PhD Candidate @ Jagiellonian University, [[#section20251204|Towards Explainable Meta-Models for Ensembles of Financial Alphas.]]
-    * Meeting link: TDA
+    * Meeting link: [[https://teams.microsoft.com/meet/33172349128262?p=yBHWXyoke6oH4OVDl4|MS Teams]]
     * Recording:  TDA
     * Presentation slides:  TDA
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+==== 2025-12-11 ====
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+**Speaker**: Anna Sofia Lippolis, PhD Candidate @ University of Bologna
+**Title**: Enhancing Knowledge Engineering with LLMs.
+**Abstract**:
+The development and spread of Large Language Models (LLMs) are having a growing impact on the world of the Semantic Web, profoundly transforming the field of Knowledge Engineering. This field, traditionally characterized by a high degree of manual work and collaboration between technical professionals and domain experts, faces various challenges related to scalability and the continuous evolution of knowledge. In this context, LLMs are emerging in several areas, from law to medicine, as tools that support researchers: from the automatic generation of ontologies to the assessment of the quality and semantic coverage of conceptual models, and even the exploration of analogical reasoning, through which it is possible to identify structural correspondences between different domains. This talk will present current research directions on collaboration between LLMs and researchers for knowledge modeling, within a critical overview of the opportunities offered by these tools for the future of the Semantic Web.
+**Biogram**:
+Anna Sofia Lippolis (she/her) is a PhD student at the University of Bologna, Italy, affiliated with the National Research Council’s Institute for Cognitive Sciences and Technologies (Rome, Italy). Her work investigates how semantic technologies intersect with Digital Humanities research and how AI can automate knowledge-engineering practices.
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 ==== 2025-12-04 ====
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 **Abstract**:
 One branch of systematic trading research studies large libraries of formulaic alphas: small predictive models built from price and volume data. In practice, these alphas are combined into an ensemble whose composition changes with market conditions. From an ML perspective, this can be viewed as a meta-model that selects and weights weak experts based on their characteristics and the current environment.
-In this talk I will introduce this setting with minimal financial background (cross-sectional returns, information coefficient, long–short factor portfolios), and then reframe it in familiar ML terms. I will show how individual alphas can be treated as models with their own structural and behavioural features, and how this enables clustering them into ""families"" and reasoning about dynamic ensemble construction. Finally, I will sketch the idea of an explainable meta-model that maps alpha features and market descriptors to ensemble decisions, and highlight open methodological questions and possible research directions.
+In this talk I will introduce this setting with minimal financial background (cross-sectional returns, information coefficient, long–short factor portfolios), and then reframe it in familiar ML terms. I will show how individual alphas can be treated as models with their own structural and behavioural features, and how this enables clustering them into "families" and reasoning about dynamic ensemble construction. Finally, I will sketch the idea of an explainable meta-model that maps alpha features and market descriptors to ensemble decisions, and highlight open methodological questions and possible research directions.
 **Biogram**: