MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. AVAILABILITY AND IMPLEMENTATION: https://kghub.org.

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

This paper presents a novel architecture that brings together Information Extraction (IE) with Event Processing (EP)research areas to globally monitor human activities and biodiversity dynamics and measure their impact on ecosystems. The two areas (IE and EP) are rich on their own and we believe their integration will achieve a much more comprehensive solution to ecosystems monitoring. The integration is based on a closed loop mechanism that guarantees the communication and the evolution of the overall architecture. While we use Microblogging communities (e.g., Twitter) as a news producing tool, we keep track of the vulnerable ecosystems using a GIS tracking database. We also make use of Google Map/Earth API capabilities to dynamically update the GIS database. After a complete cycle, the architecture produces a list of vulnerable ecosystems. This architecture leverages the rich research in Scientific Workflows to achieve the integration and communication of the various components. We are in the process of developing a system that can be used by conservationists and decision makers to efficiently allocate their time and limited resources in response to ecosystems perturbation.