Healthcare representation learning on the Electronic Health Record (EHR) is seen as crucial for predictive analytics in the medical field. Many natural language processing techniques, such as word2vec, RNN and self-attention, have been adapted for use in hierarchical and time stamped EHR data, but fail when they lack either general or task-specific data. Hence, some recent works train healthcare representations by incorporating medical ontology (a.k.a. knowledge graph), by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are never exploited to enhance ontology learning. To address this, we propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics. Specifically, it consists of task-specific representation learning and graph-embedding modules to learn both patient journey and medical ontology interactively. Consequently, this creates a mutual integration to benefit both healthcare representation learning and medical ontology embedding. Moreover, such integration is achieved by a joint training of both task-specific predictive and ontology-based disease typing tasks based on fused embeddings of the two modules. Experiments conducted on two real-world diagnosis prediction datasets show that, our healthcare representation model MIMO not only achieves better predictive results than previous state-of-the-art approaches regardless of sufficient or insufficient training data, but also derives more interpretable embeddings of diagnoses.

The current preprint is an update to SituationCO v1.1 (Situation Core Ontology), which represents its new version 1.2. It specifies and defines all the terms, properties, relationships and axioms of SituationCO v1.2, being an ontology for particular and generic Situations placed at the core level in the context of a four-layered ontological architecture called FCD-OntoArch (Foundational, Core, and Domain Ontological Architecture for Sciences). This is a four-layered ontological architecture, which considers Foundational, Core, Domain and Instance levels. In turn, the domain level is split down in two sub-levels, namely: Top-domain and Low-domain ontological levels. So in fact, we can consider it to be a five-tier architecture. Ontologies at the same level can be related to each other, except for the foundational level where only ThingFO (Thing Foundational Ontology) is found. In addition, ontologies' terms and relationships at lower levels can be semantically enriched by ontologies' terms and relationships from the higher levels. Note that both ThingFO and ontologies at the core level such as SituationCO, ProcessCO, among others, are domain independent. SituationCO's terms and relationships are specialized primarily from ThingFO. It also completely reuses terms primarily from ProcessCO, ProjectCO and GoalCO ontologies. Stereotypes are the used mechanism for enriching SituationCO terms. Note that in the end of this document, we address the SituationCO vs. ThingFO non-taxonomic relationship verification matrix.

The present preprint specifies and defines all Terms, Properties, Relationships and Axioms of ThingFO (Thing Foundational Ontology) v1.2, which is a slightly updated version of its predecessor, ThingFO v1.1. It is an ontology for particular and universal Things placed at the foundational level in the context of a four-layered ontological architecture named FCD-OntoArch (Foundational, Core, and Domain Ontological Architecture for Sciences). This is a five-layered ontological architecture, which considers Foundational, Core, Domain and Instance levels. In turn, the domain level is split down in two sub-levels, namely: Top-domain and Low-domain. Ontologies at the same level can be related to each other, except for the foundational level where only the ThingFO ontology is. In addition, ontologies' terms and relationships at lower levels can be semantically enriched by ontologies' terms and relationships from the higher levels. ThingFO and ontologies at the core level such as SituationCO, ProcessCO, ProjectCO, among others, are domain independent. ThingFO is made up of three main concepts, namely: Thing with the semantics of Particular, Thing Category with the semantics of Universal, and Assertion that represents human statements about different aspects of Particulars and Universals. Note that annotations of updates from the previous version (v1.1) to the current one (v1.2) can be found in Appendix A.

Biodiversity, the variation within and between species and ecosystems, is essential for human well-being and the equilibrium of the planet. It is critical for the sustainable development of human society and is an important global challenge. Biodiversity research has become increasingly data-intensive and it deals with heterogeneous and distributed data made available by global and regional initiatives, such as GBIF, ILTER, LifeWatch, BODC, PANGAEA, and TERN, that apply different data management practices. In particular, a variety of metadata and semantic resources have been produced by these initiatives to describe biodiversity observations, introducing interoperability issues across data management systems. To address these challenges, the InteroperAble Descriptions of Observable Property Terminology WG (I-ADOPT WG) was formed by a group of international terminology providers and data center managers in 2019 with the aim to build a common approach to describe what is observed, measured, calculated, or derived. Based on an extensive analysis of existing semantic representations of variables, the WG has recently published the I-ADOPT framework ontology to facilitate interoperability between existing semantic resources and support the provision of machine-readable variable descriptions whose components are mapped to FAIR vocabulary terms. The I-ADOPT framework ontology defines a set of high level semantic components that can be used to describe a variety of patterns commonly found in scientific observations. This contribution will focus on how the I-ADOPT framework can be applied to represent variables commonly used in the biodiversity domain.

In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.

The construction of an ontology of scientific knowledge objects, presented here, is part of the development of an approach oriented towards the visualization of scientific knowledge. It is motivated by the fact that the concepts that are used to organize scientific knowledge (theorem, law, experience, proof, etc.) appear in existing ontologies but that none of these ontologies is centered on this topic and presents them in a simple and easily understandable organization. This ontology has been constructed by 1) selecting concepts that appear in high level ontologies or in ontologies of knowledge objects of specific fields and 2) interviewing scientists in different fields. We have aligned this ontology with some of the sources used, which has allowed us to verify its consistency with respect to them. The validation of the ontology consists in using it to formalize knowledge from various sources, which we have begun to do in the field of physics. The access to scientific knowledge, whether general or factual, must necessarily involve a visual, auditory or other presentation that appeals to one or more senses of the human being.

Entity-Relationship (E-R) Modeling is one approach to visualize what story your data is trying to tell. This goal of this predecessor to object modeling (e.g. UML or CRC cards) is to give you a high-level, graphical view of the core components of an enterprise--the E-R diagram. An E-R diagram (sometimes called a Chen diagram, after its creator, Peter Chen) is a conceptual graph that captures meaning rather than implementation [1]. Once you have the diagram, you can convert it to a set of tables.

In theory, ontology-based process modelling (OBPM) bares great potential to extend business process management. Many works have studied OBPM and are clear on the potential amenities, such as eliminating ambiguities or enabling advanced reasoning over company processes. However, despite this approval in academia, a widespread industry adoption is still nowhere to be seen. This can be mainly attributed to the fact, that it still requires high amounts of manual labour to initially create ontologies and annotations to process models. As long as these problems are not addressed, implementing OBPM seems unfeasible in practice. In this work, we therefore identify requirements needed for a successful implementation of OBPM and assess the current state of research w.r.t. these requirements. Our results indicate that the research progress for means to facilitate OBPM are still alarmingly low and there needs to be urgent work on extending existing approaches.

Property graphs constitute data models for representing knowledge graphs. They allow for the convenient representation of facts, including facts about facts, represented by triples in subject or object position of other triples. Knowledge graphs such as Wikidata are created by a diversity of contributors and a range of sources leaving them prone to two types of errors. The first type of error, falsity of facts, is addressed by property graphs through the representation of provenance and validity, making triples occur as first-order objects in subject position of metadata triples. The second type of error, violation of domain constraints, has not been addressed with regard to property graphs so far. In RDF representations, this error can be addressed by shape languages such as SHACL or ShEx, which allow for checking whether graphs are valid with respect to a set of domain constraints. Borrowing ideas from the syntax and semantics definitions of SHACL, we design a shape language for property graphs, ProGS, which allows for formulating shape constraints on property graphs including their specific constructs, such as edges with identities and key-value annotations to both nodes and edges. We define a formal semantics of ProGS, investigate the resulting complexity of validating property graphs against sets of ProGS shapes, compare with corresponding results for SHACL, and implement a prototypical validator that utilizes answer set programming.

We introduce and study several notions of approximation for ontology-mediated queries based on the description logics ALC and ALCI. Our approximations are of two kinds: we may (1) replace the ontology with one formulated in a tractable ontology language such as ELI or certain TGDs and (2) replace the database with one from a tractable class such as the class of databases whose treewidth is bounded by a constant. We determine the computational complexity and the relative completeness of the resulting approximations. (Almost) all of them reduce the data complexity from coNP-complete to PTime, in some cases even to fixed-parameter tractable and to linear time. While approximations of kind (1) also reduce the combined complexity, this tends to not be the case for approximations of kind (2). In some cases, the combined complexity even increases.