Jonathan P. Bona

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.

The agent takes the stage

The deployment of virtual characters in intermedia performance drives divergent agendas of this research group. From the perspective of performance studies, we examine the effect of computer-based characters as actors and believe explorations of mediated agency can open up new forms of engagement for live productions. From the visualisation point of view, we are interested in how abstraction and animation techniques, based on motion tracking and procedural methods, convey character and warp and extend the gestural repertoire of a human actor. In terms of interactive drama, we are working on stream of consciousness characters: algorithmically recombining text to create a psychological entity with an autonomous inner structure. From an artificial intelligence perspective, we investigate how to design and use intelligent agents as actors. These agendas reflect an odd mix of aesthetic and technical concerns, and rightly so, as they are driven by the different goals of our interdisciplinary team.

Mismatches between major subhierarchies and semantic tags in SNOMED CT

The fully specified name of a concept in SNOMED CT is formed by a term to which in the typical case is added a semantic tag. The latter is meant to disambiguate homonymous terms and to indicate in which major subhierarchy of SNOMED CT that concept fits. We have developed a method to determine whether a concepts tag correctly identifies its place in the hierarchy, and applied this method to an analysis of all active concepts in every SNOMED CT release from January 2003 to January 2017. Our results show (1) that there are concepts in almost every release whose semantic tag does not match their placement in the hierarchy, (2) that it is primarily disorder concepts that are involved, and (3) that the number of such mismatches increase since the July 2012 version. Our analysis determined that it is primarily the absence of a mechanism in the SNOMED CT authoring environment to suggest stated relationships for very similar concepts that is responsible for the mismatches. We argue that the SNOMED CT authoring environment should treat the semantic tags as part of the formal structure so that methods can be implemented to keep the sub-hierarchies in sync with the semantic tags.

Comparing Small Graph Retrieval Performance for Ontology Concepts in Medical Texts

Some terminologies and ontologies, such as SNOMED CT, allow for post–coordinated as well as pre-coordinated expressions. Post–coordinated expressions are, essentially, small segments of the terminology graphs. Compositional expressions add logical and linguistic relations to the standard technique of post-coordination. In indexing medical text, many instances of compositional expressions must be stored, and in performing retrieval on that index, entire compositional expressions and sub-parts of those expressions must be searched. The problem becomes a small graph query against a large collection of small graphs. This is further complicated by the need to also find sub-graphs from a collection of small graphs. In previous systems using compositional expressions, such as iNLP, the index was stored in a relational database. We compare retrieval characteristics of relational databases, triplestores, and general graph databases to determine which is most efficient for the task at hand.

Modality in the MGLAIR architecture

The MGLAIR cognitive agent architecture includes a general model of modality and support for concurrent multimodal perception and action. It provides afferent and efferent modalities as instantiable objects used in agent implementations. Each modality is defined by a set of properties that govern its use and its integration with reasoning and acting. This paper presents the MGLAIR model of modalities and mechanisms for their use in computational cognitive agents.