Concept induction, which infers appropriate abstract categories from concrete instances, is both a core ontology engineering task and a testbed for LLM semantic abstraction. Although LLMs offer broad knowledge and reasoning that may ease expert-driven ontology construction, prior work shows that knowledge deficiencies still limit them in specialized domains, while systematic evaluation of their induction failures remains lacking. To address this gap, we propose a familiarity-controlled evaluation framework that uses knowledge probing to separate item familiarity from concept induction ability. The evaluation reveals a systematic tendency to induce overly general concepts: LLMs often produce semantically correct but overly broad labels, and direct property augmentation improves performance without resolving this granularity mismatch. We further propose PPACI, an ontology-prior-guided approach that builds concept prototypes by integrating concept definitions with member item properties. For each query group, PPACI uses item properties to retrieve local item-level and global group-level candidate concepts as induction anchors. Experiments across multiple LLMs show that PPACI reduces overly general predictions by 17–28 percentage points while maintaining competitive induction quality. Our code and data are publicly available at https://github.com/Orchideous77/PPACI.

Open-domain timeline summarization (TLS) faces challenges from information overload and data sparsity when processing large-scale textual streams. Existing methods struggle to capture coherent event narratives due to fragmented descriptions and often accumulate noise through iterative retrieval strategies that lack effective relevance evaluation. This paper proposes: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Intergration, which offers a novel perspective for open-domain TLS by time point completion and event element completion. R2A-TLS establishes an initial retrieval, reflection, and deep retrieval system that reduces noise through a double filtering mechanism that iteratively generates a timeline for each text which passes the filtering. Then, the system reflects on the initial timeline with the aim of identifying information gaps through causal chain analysis and FrameNet based element validation. These gaps are reformulated into targeted queries to trigger deep retrieval for refining timeline coherence and density. Empirical evaluation on Open-TLS dataset reveals that our approach outperforms the best prior published approaches.