Knowledge graphs (KGs) are crucial for applications like search engines, conversational agents, and recommender systems because they provide structured, semantically rich representations of knowledge, enabling more accurate information retrieval, contextual understanding, and intelligent decision-making. Entity matching approaches aim to find identity relations between different entity descriptions. In KGs, owl:sameAs is the standard predicate that represents the identity relation and its expresses the strict identity as defined by Libniz Identity Relation[2]. However, in many cases, a strict identity relation between entities in a knowledge graph is impractical, as some entities have more complex relationships that do not conform to a rigid one-to-one correspondence with real-world objects. In this work we investigate the notions of weak identity and weak difference while ensuring an explanation of the decision that can be taken. The developed approach is able to assign each individual pair to a range in the decision space that we define in this paper. Thanks to the computation of a holistic abstraction of the entity matching process, we ensure providing an explanation for each assignment in terms of sets of incomplete and/or conflicting properties. A first experimental evaluation on a benchmark of DBepdia and YAGO entities has provided promising results.

BACKGROUND: Managing and organizing biological knowledge remains a major challenge, due to the complexity of living systems. Recently, systemic representations have been promising in tackling such a challenge at the whole-cell scale. In such representations, the cell is considered as a system composed of interlocked subsystems. The need is now to define a relevant formalization of the systemic description of cellular processes. RESULTS: We introduce BiPOm (Biological interlocked Process Ontology for metabolism) an ontology to represent metabolic processes as interlocked subsystems using a limited number of classes and properties. We explicitly formalized the relations between the enzyme, its activity, the substrates and the products of the reaction, as well as the active state of all involved molecules. We further showed that the information of molecules such as molecular types or molecular properties can be deduced by automatic reasoning using logical rules. The information necessary to populate BiPOm can be extracted from existing databases or existing bio-ontologies. CONCLUSION: BiPOm provides a formal rule-based knowledge representation to relate all cellular components together by considering the cellular system as a whole. It relies on a paradigm shift where the anchorage of knowledge is rerouted from the molecule to the biological process. AVAILABILITY: BiPOm can be downloaded at https://github.com/SysBioInra/SysOnto.

UNLABELLED: Live Cell Array (LCA) technology allows the acquisition of high-resolution time-course profiles of bacterial gene expression by the systematic assessment of fluorescence in living cells carrying either transcriptional or translational fluorescent protein fusion. However, the direct estimation of promoter activities by time-dependent derivation of the fluorescence datasets generates high levels of noise. Here, we present BasyLiCA, a user-friendly open-source interface and database dedicated to the automatic storage and standardized treatment of LCA data. Data quality reports are generated automatically. Growth rates and promoter activities are calculated by tunable discrete Kalman filters that can be set to incorporate data from biological replicates, significantly reducing the impact of noise measurement in activity estimations. AVAILABILITY: The BasyLiCA software and the related documentation are available at http://genome.jouy.inra.fr/basylica.