Biological taxonomy and ontology development: scope and limitations

The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are reviewed. We contrast the common usage of a static 'snapshot' hierarchy in ontological representations of taxonomy with a more realistic situation that involves dynamic, piece-meal revisions of particular taxonomic groups and requires alignment with relevant preceding perspectives. Taxonomic practice is characterized by a range of phenomena that are orthogonal to the logical semantic background from which ontological entities and relationships originate, and therefore pose special challenges to ontological representation and reasoning. Among these phenomena are: (1) the notion that there is a single phylogenetic hierarchy in nature which taxonomy can only gradually approximate; (2) the evolvability of taxa which means that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life; (3) the hybrid approach of defining taxa both in reference to properties (intensional) and members (ostensive) which undermines the individual/class dichotomy sustaining conventional ontologies; (4) the idiosyncratic yet inferentially valuable usage of Linnaean ranks; (5) the indelible and semantically complex 250-year legacy of nomenclatural and taxonomic changes that characterizes the current system; (6) the insufficient taxonomic exploration of large portions of the tree of life; and the need to use a sophisticated terminology for aligning taxonomic entities in order to integrate both (7) single and (8) multiple hierarchies. We briefly such how such integration may proceed based on an initial expert alignment of concept relationship and subsequent use of first-order logic algorithms to maximize consistency, reveal implied relationships, and ultimately merge taxonomies.
 In light of the aforementioned obstacles, we suggest that research along the taxonomy/ontology interface should focus on either strictly nomenclatural entities or specialize in ontology-driven methods for producing alignments between multiple taxonomies. We furthermore suggest that the prospects of developing successful ontologies for taxonomy will largely depend on the ability of the taxonomic expert community to present their phylogenies and classifications in a way that is more compatible with ontological reasoning than concurrent practice. Minimally, this means (1) adopting rigorous standards for linking new core taxonomies to relevant peripheral taxonomies through comprehensive alignments so that their ontological/taxonomic connections are transparent; (2) using lineage-specific ontological standards for phenotype-based accounts of taxa while taking into account the phylogenetic contextuality of phenotypic descriptors; (3) presenting all nomenclatural and taxonomic novelties in an explicit, ontology-compatible format, including intensional and ostensive definitions; and (4) offering comprehensive intensional/ostensive alignments to entities in relevant preceding taxonomies

Bonheur du citoyen, malheur de l’écrivain. Pauvreté du roman judéo-français, richesse du roman judéo-allemand dans l’entre-deux-guerres.

Bonheur du citoyen, malheur de l’écrivain. Pauvreté du roman judéo-français, richesse du roman judéo-allemand dans l’entre-deux-guerres

EEFlux: A Landsat-based Evapotranspiration mapping tool on the Google Earth Engine

“EEFlux” is an acronym for ‘Earth Engine Evapotranspiration Flux.’ EEFlux is based on the operational surface energy balance model “METRIC” (Mapping ET at high Resolution with Internalized Calibration), and is a Landsat-imagebased process. Landsat imagery supports the production of ET maps at resolutions of 30 m, which is the scale of many human-impacted and human-interest activities including agricultural fields, forest clearcuts and vegetation systems along streams. ET over extended time periods provides valuable information regarding impacts of water consumption on Earth resources and on humans. EEFlux uses North American Land Data Assimilation System hourly gridded weather data collection for energy balance calibration and time integration of ET. Reference ET is calculated using the ASCE (2005) Penman-Monteith and GridMET weather data sets. The Statsgo soil data base of the USDA provides soil type information. EEFlux will be freely available to the public and includes a web-based operating console. This work has been supported by Google, Inc. and is possible due to the free Landsat image access afforded by the USGS

A Brief Overview of the NEBULA Future Internet Architecture

NEBULA is a proposal for a Future Internet Architecture. It is based on the assumptions that: (1) cloud computing will comprise an increasing fraction of the application workload offered to an Internet, and (2) that access to cloud computing resources will demand new architectural features from a network. Features that we have identified include dependability, security, flexibility and extensibility, the entirety of which constitute resilience.NEBULA provides resilient networking services using ultrareliable routers, an extensible control plane and use of multiple paths upon which arbitrary policies may be enforced. We report on a prototype system, Zodiac, that incorporates these latter two features

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers.Comment: Co-authored by participants from the October 2022 workshop: https://crcs.seas.harvard.edu/conservation-worksho

Reasoning about taxonomies and articulations

Taxonomically organized data pervade science, business and everyday life. Unfortunately, taxonomies are often under-specified, limiting their utility in contexts such as data inte-gration, information navigation and autonomous agent com-munication. This work formalizes taxonomies and relation-ships between them as formulas in logic. This formalization concretizes notions such as consistency and inconsistency of taxonomies and articulations (inter-taxonomic relations) between them, enables the derivation of new articulations based on a given set of taxonomies and articulations and provides a framework for testing assumptions about under-specified taxonomies. Given the typical intractability of reasoning with tax-onomies and articulations, this research investigates many optimizations: from those that reduce the search space, to those that leverage parallel processing, to those investigat-ing logics more tractable than first-order logic (e.g., monadic first-order logic, propositional logic, description logics, and subsets of the RCC-5 spatial algebra). Finally, in addition to reasoning with taxonomies and articulations, this research investigates how to repair inconsistent taxonomies and ar-ticulations, how to explain inconsistencies and discovered relations, and how to merge taxonomies given articulations. Critical to this research is the development of a framework for testing logics and support for the development of tax-onomies and articulations. This framework, CleanTax is already well under way and has been used to study articu-lations between two large-scale biological taxonomies. 1

Reasoning about taxonomies

Taxonomically organized data pervade science, business, and everyday life. Unfortunately, taxonomies are often under-specified, or even inconsistent, limiting their utility in contexts such as data integration, information navigation, and autonomous agent communication. This work formalizes taxonomies and articulations (relationships between taxa in taxonomies) as first-order formulas. This formalization concretizes notions such as consistency and inconsistency of taxonomies and articulations between them, enables the derivation of new articulations based on a given set of taxonomies and articulations, and provides a framework for testing assumptions about under-specified taxonomies. Given the typical intractability of reasoning with taxonomies and articulations, this research also investigates many optimizations: from those that reduce the search space, to those that leverage parallel processing, to those investigating logics more tractable than first-order logic (e.g., monadic first-order logic, propositional logic, description logics, and subsets of the RCC-5 spatial algebra). Finally, in addition to reasoning with taxonomies and articulations, this research investigates how to merge taxonomies given articulations and how to merge data sets that have been annotated to aligned taxonomies. Critical to this research is the development of a framework for testing logics and supporting the development of taxonomies and articulations. This framework, CLEANTAX, has been implemented and has been used to study articulations between several large-scale biological taxonomies