Our understanding of social reality is shaped by the specific ways in which that reality is framed by different sources. Analyzing framing means examining how these sources are able to convey particular worldviews by foregrounding or downplaying certain aspects of experience. Current computational approaches address this task by automatically identifying communicative patterns (e.g., topic selection or rhetorical strategies) that characterize individual artifacts. However, they often remain document-bound, overlooking the comparative dimension that enables the uncovering of convergent or conflicting narratives about the same actor, event, or issue. In this paper, we propose DORIS, an ontology that supports both document-level and cross-document framing analysis using SPARQL queries on automatically constructed Knowledge Graphs. We validate the proposed approach through a case study of historical news articles, exploring multiple framings of a real-world event using Fillmore’s Frame Semantics and the FrameNet resource. Code and data are available on GitHub at https://github.com/beatrice-f/DORIS/.

Large Language Models (LLMs) face significant challenges when queried about long-tail knowledge, i.e., information that is rarely encountered during their training process. These difficulties arise due to the inherent sparsity of such data. Furthermore, LLMs often lack the ability to verify or ground their responses in authoritative sources, which can lead to plausible yet inaccurate outputs when addressing infrequent subject matter. Our work aims to investigate these phenomena by introducing KE-MHISTO, a multilingual benchmark for Entity Linking and Question Answering in the domain of historical music knowledge, available in both Italian and English. We demonstrate that KE-MHISTO provides significantly broader coverage of long-tail knowledge compared to existing alternatives. Moreover, it poses substantial challenges for state-of-the-art models. Our experiments reveal that smaller, multilingual models can achieve performance comparable to significantly larger counterparts, highlighting the potential of efficient, language-aware approaches for long-tail knowledge extraction. KE-MHISTO is available at: https://github.com/polifonia-project/KE-MHISTO.

In this paper, we present an evaluation of two different approaches to the free-form Question Answering (QA) task. The main difference between the two approaches is that one is based on latent representations of knowledge, and the other uses explicit knowledge representation. For the evaluation, we developed DynaKnowledge, a new benchmark composed of questions concerning Wikipedia low-frequency entities. We wanted to ensure, on the one hand, that the questions are answerable and, on the other, that the models can provide information about very specific facts. The evaluation that we conducted highlights that the proposed benchmark is particularly challenging. The best model answers correctly only on 50% of the questions. Analysing the results, we also found that ChatGPT shows low reliance on low-frequency entity questions, manifesting a popularity bias. On the other hand, a simpler model based on explicit knowledge is less affected by this bias. With this paper, we want to provide a living benchmark for open-form QA to test knowledge and latent representation models on a dynamic benchmark.