Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases.

Background: How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may extract such biomedical facts, although the state-of-the-art focuses on pair-based proportional (pairwise) analogies such as man:woman::king:queen ("queen = -man +king +woman").

Mobilising computable biomedical knowledge: challenges for clinical decision support from a medical knowledge provider.

Introduction: This paper summarises a talk given at the first UK workshop on mobilising computable biomedical knowledge on 29 October 2019 in London. It examines challenges in mobilising computable biomedical knowledge for clinical decision support from the perspective of a medical knowledge provider.

Methods: We developed the themes outlined below after personally reflecting on the challenges that we have encountered in this field and after considering the barriers that knowledge providers face in ensuring that their content is accessed and used by healthcare professionals.

Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes.

Background: Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge on a scale requires cross checking with ground truths (i.e.

The Montagues and the Capulets.

Two households, both alike in dignity, In fair Genomics, where we lay our scene, (One, comforted by its logic's rigour, Claims ontology for the realm of pure, The other, with blessed scientist's vigour, Acts hastily on models that endure), From ancient grudge break to new mutiny, When 'being' drives a fly-man to blaspheme. From forth the fatal loins of these two foes, Researchers to unlock the book of life; Whole misadventured piteous overthrows, Can with their work bury their clans' strife. The fruitful passage of their GO-mark'd love, And the continuance of their studies sage, Which, united, yield ontologies undreamed-of, Is now the hour's traffic of our stage; The which if you with patient ears attend, What here shall miss, our toil shall strive to mend.

Building ontologies in DAML + OIL.

In this article we describe an approach to representing and building ontologies advocated by the Bioinformatics and Medical Informatics groups at the University of Manchester. The hand-crafting of ontologies offers an easy and rapid avenue to delivering ontologies. Experience has shown that such approaches are unsustainable.

Scale and context: issues in ontologies to link health- and bio-informatics.

Bridging levels of scale and context are key problems for integrating Bio- and Health Informatics. Formal, logic-based ontologies using expressive formalisms are naturally "fractal" and provide new methods to support these aims. The basic notion of composition can be used to bridge scales; axioms can be used to carry implicit information; specific context markers can be included in definitions; and a hierarchy of semantic links can be used to represent subtle differences in point of view.