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The potential of large language models (LLM) has captured the imagination of the public and researchers alike. In contrast to previous generations of machine learning models, LLMs are general-purpose tools, which can communicate with humans. In particular, they are able to define terms and answer factual questions based on some internally represented knowledge. Thus, LLMs support functionalities that are closely related to ontologies. In this perspective article, I will discuss the consequences of the advent of LLMs for the field of applied ontology. [ABSTRACT FROM AUTHOR]

Ontology development methodologies emphasise knowledge gathering from domain experts and documentary resources, and knowledge representation using an ontology language such as OWL or FOL. However, working ontologists are often surprised by how challenging and slow it can be to develop ontologies. Here, with a particular emphasis on the sorts of ontologies that are content-heavy and intended to be shared across a community of users (reference ontologies), we propose that a significant and heretofore under-emphasised contributor of challenges during ontology development is the need to create, or bring about, consensus in the face of disagreement. For this reason reference ontology development cannot be automated, at least within the limitations of existing AI approaches. Further, for the same reason ontologists are required to have specific social-negotiating skills which are currently lacking in most technical curricula. [ABSTRACT FROM AUTHOR]

The article discusses ideas developed by the 2013 Ontology Summit community regarding the evaluation of the quality of ontologies. It presents a model for the ontology life cycle and evaluation criteria for the phases of the life cycle. It discusses how the best practices in systems engineering and software engineering can be utilized in ontology development and use. Aspects of the quality of ontologies discussed are intelligibility, fidelity, craftmanship, fitness and deployability.

Modules allow the reuse of an ontology as part of another ontology. If the ontologies do not share a common domain of discourse a simple 'cut and paste' approach to module reuse leads to unintended consequences, the Horatio problem. To solve this problem ISO/IEC 24707's Common Logic includes modules as a syntactic category. However, the semantics of modules is treated incongruently in ISO/IEC 24707. In this paper we propose an alternative semantics of modules, discuss their logical properties, and how they can be used in ontology development. [ABSTRACT FROM AUTHOR]

In this paper, the authors examine some of the implications of born-digital research environments by discussing the emergence of data mining and the analysis of social media platforms. With the rise of individual online activity in chat rooms, social networking sites and micro-blogging services, new repositories for social science research have become available in large quantities. Given the changes of scale that accompany such research, both in terms of data mining and the communication of results, the authors term this type of research ‘massified research’. This article argues that while the private and commercial processing of these new massive data sets is far from unproblematic, the use by academic practitioners poses particular challenges with respect to established ethical protocols. These involve reconfigurations of the external relations between researchers and participants, as well as the internal relations that compose the identities of the participant, the researcher and that of the data. Consequently, massified research and its outputs operate in a grey area of undefined conduct with respect to these concerns. The authors work through the specific case study of using Twitter's public Application Programming Interface for research and visualization. To conclude, this article proposes some potential best practices to extend current procedures and guidelines for such massified research. Most importantly, the authors develop these under the banner of ‘agile ethics’. The authors conclude by making the counterintuitive suggestion that researchers make themselves as vulnerable to potential data mining as the subjects who comprise their data sets: a parity of practice. [ABSTRACT FROM AUTHOR]

The micro blogging platform Twitter is actively used by millions of people. By using the geo tags of the messages sent a virtual landscape of online activity at a certain place is generated to visualize the interface between the real-world location and the virtual activity. These New City Landscapes (NCL) visualize the amount of activity as density surfaces with hills, peaks and valleys. Across a set of different cities from around the world the resulting landscape morphologies are compared and characterized as types. This article focuses on temporal narratives and how they travel through the urban fabric via the virtual landscape in different cities around the world. The findings are discussed as cross comparisons according to activity in time and space. [ABSTRACT FROM AUTHOR]

The goal of the Ontology Summit 2010 was to address the current shortage of persons with ontology expertise by developing a strategy for the education of ontologists. To achieve this goal we studied how ontologists are currently trained, the requirements identified by organizations that hire ontologists, and developments that might impact the training of ontologists in the future. We developed recommendations for the body of knowledge that should be taught and the skills that should be developed by future ontologists; these recommendations are intended as guidelines for institutions and organizations that may consider establishing a program for training ontologists. Further, we recommend a number of specific actions for the community to pursue. [ABSTRACT FROM AUTHOR]

In cognitive science, image schemas are identified as fundamental patterns of cognition. They are schematic prelinguistic conceptualisations of events and serve as conceptual building blocks for concepts. This paper proposes that image schemas can play an important role in computational concept invention, namely within the computational realisation of conceptual blending. We propose to build a library of formalised image schemas, and illustrate how they can guide the search for a base space in the concept invention work flow. Their schematic nature is captured by the idea of organising image schemas into families. Formally, they are represented as heterogeneous, interlinked theories. [ABSTRACT FROM AUTHOR]

Modules allow the reuse of an ontology as part of another ontology. If the ontologies do not share a common domain of discourse a simple 'cut and paste' approach to module reuse leads to unintended consequences, the Horatio problem. To solve this problem ISO/IEC 24707's Common Logic includes modules as a syntactic category. However, the semantics of modules is treated incongruently in ISO/IEC 24707. In this paper we propose an alternative semantics of modules, discuss their logical properties, and how they can be used in ontology development. [ABSTRACT FROM AUTHOR]

Chemical data is increasingly openly available in databases such as PubChem, which contains approximately 110 million compound entries as of February 2021. With the availability of data at such scale, the burden has shifted to organisation, analysis and interpretation. Chemical ontologies provide structured classifications of chemical entities that can be used for navigation and filtering of the large chemical space. ChEBI is a prominent example of a chemical ontology, widely used in life science contexts. However, ChEBI is manually maintained and as such cannot easily scale to the full scope of public chemical data. There is a need for tools that are able to automatically classify chemical data into chemical ontologies, which can be framed as a hierarchical multi-class classification problem. In this paper we evaluate machine learning approaches for this task, comparing different learning frameworks including logistic regression, decision trees and long short-term memory artificial neural networks, and different encoding approaches for the chemical structures, including cheminformatics fingerprints and character-based encoding from chemical line notation representations. We find that classical learning approaches such as logistic regression perform well with sets of relatively specific, disjoint chemical classes, while the neural network is able to handle larger sets of overlapping classes but needs more examples per class to learn from, and is not able to make a class prediction for every molecule. Future work will explore hybrid and ensemble approaches, as well as alternative network architectures including neuro-symbolic approaches. [ABSTRACT FROM AUTHOR]

Ontohub is a repository engine for managing distributed heterogeneous ontologies. The distributed nature enables communities to share and exchange their contributions easily. The heterogeneous nature makes it possible to integrate ontologies written in various ontology languages. Ontohub supports a wide range of formal logical and ontology languages, as well as various structuring and modularity constructs and inter-theory (concept) mappings, building on the Object Management Group's Distributed Ontology, Model and Specification Language (DOL). Ontohub repositories are organised as Git repositories, thus inheriting all features of this popular version control system. Moreover, Ontohub is the first repository engine meeting a substantial amount of the requirements formulated in the context of the Open Ontology Repository (OOR) initiative, including an API for federation as well as support for logical inference and axiom selection. [ABSTRACT FROM AUTHOR]

Motivation: Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined.Results: We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource.Availability and implementation: The relations we define are available from http://purl.obolibrary.org/obo/ro.owl. They are used in the Drosophila anatomy ontology (http://purl.obolibrary.org/obo/fbbt/2011-09-06/), which drives the web resource http://www.virtualflybrain.orgContact: djs93@gen.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

A correction to the article "A Strategy for Building Neuroanatomy Ontologies " that was published in the 2012 issue is presented.