OntoLex-Lemon is a model for representing lexical information, focusing on the use of lexical entries in texts rather than their definitions. This work proposes an extension to the model that aims to capture the definition of senses attributed to lexical entries. We explicitly represent a conceptual setup authored by an agent that operates on lexical content. It either proposes new senses for existing lexical entries in a language or coins new terms to express proposed senses. It provides textual and/or formal definitions to senses/concepts, and can serve as an interpretation of other senses/concepts through rephrasing, translation, formalization, or comparison. Because a conceptual setup and its interpretations may not be unanimously accepted, it is important to support the selection of relevant meanings, as for example, those proposed by a certain author. We illustrate the application of our proposed extension with two case studies, one about the philosophical definition of the concept of idea and its interpretations, and one about historical attributions of meaning to the Dutch East India Company (VOC).

Entities change over time, and while information about entity change is contained in knowledge graphs (KGs), it is often not stated explicitly. This makes KGs less useful for investigating entities over time, or downstream tasks such as historical entity linking. In this paper, we present an approach and experiments that make explicit entity change in Wikidata. Our contributions are a mapping between an existing change ontology and Wikidata properties to identify types of change, and a dataset of entities with explicit evolution information and analytics on this dataset.

Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE) framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance.