The field is moving towards the integration of semantic technologies and large language models to improve decision-making and analysis in various domains. This is evident in the development of knowledge graphs and legal-specific large language models that can efficiently support resource allocation, identify and mitigate undesired conditions, and provide intuitive interfaces for human interaction. Noteworthy papers include: Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs, which presents a novel industry-oriented semantic model for analyzing and mitigating undesired conditions in flexible cyber-physical production systems. Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women, which introduces a legal knowledge graph targeting legal cases of violence against women and develops two complementary approaches for automated legal knowledge graph construction. IBPS: Indian Bail Prediction System, which presents an AI-powered framework designed to assist in bail decision-making by predicting outcomes and generating legally sound rationales. LLMs for Law: Evaluating Legal-Specific LLMs on Contract Understanding, which evaluates 10 legal-specific large language models on three English language contract understanding tasks and compares them with 7 general-purpose large language models. Evaluating the Role of Large Language Models in Legal Practice in India, which empirically evaluates how well large language models perform key legal tasks in the Indian context.