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However, they face significant limitations in real-world applications, where knowledge is often incomplete or uncertain, which cannot be handled effectively by standard symbolic reasoning methods. In addition, building ontologies manually requires significant time and effort. Machine learning and Language Models (LMs) offer promising ways to tackle these problems. This tutorial, OntoLM, introduces the latest work at this intersection. It is a half-day program with two self-contained sessions: (1) Ontology embeddings, including geometric and LM-based embeddings, aim to encode logical and textual information into numerical representations for tasks such as ontology completion and inference. (2) Ontology reasoning and construction using embeddings and textual information leveraged by LMs. Topics include the use of LMs for OWL proof generation and abductive reasoning, ontology alignment with DeepOnto, and new concept placement. 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