Ontology-inferred phylogeny reconstruction for analyzing the evolutionary relationships between species: Ontological inference versus cladistics

Shaban-Nejad, Arash; Haarslev, Volker

doi:10.1109/bibe.2008.4696848

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Ontologies have already been used in the field of morphological cladistics, for example for the calculation of complexity as in Ram ırez and Michalik (2014) or the development of evolutionary models for culticular structures in dung beetles used in Bayesian inference as in Tarasov and G enier (2015). Shaban-Nejad and Haarslev (2008) developed a method of ontology inference using natural language processing software, a toolkit whose popularity is on the rise in systematic biology (Daly et al, 2015). In translating extracted terms directly into phylogenetic characters, however, ontology inference has a significant shortcoming.…”

Section: Introductionmentioning

confidence: 99%

The word is not enough: on morphemes, characters and ontological concepts

Göpel

Richter

2016

Cladistics

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The field of morphology has recently seen the arrival of computer‐aided ontologies, tools which permit the semantic organization of defined concepts and which therefore promise to be extremely useful in computer‐mediated approaches involving morphological data, for example in cladistics. The theoretical relationship between ontologies and cladistics, however, has hardly been explored. Here we examine the ontological status of the main terms in morphological cladistics, i.e. morpheme, character, character state and ontological concept. Morphemes are units of the descriptional perspective, whereas character states are units of the evolutionary perspective and refer to identical stages of transformation. Both morphemes and character states represent things, i.e. real entities. However, character state and morpheme denote different perspectives on these entities (description vs. evolution). Characters (transformation series; Hennig's ideographic character concept) and ontological concepts are both classes, but not of the same nature. Ontological concepts which are used to classify morphemes are constructs, i.e. totally man‐made classes that serve only as a way of classifying real entities for human recognition. Characters, however, are classes that encompass all character states of common descent and which therefore have objective, human‐independent properties. Characters, then, are natural kinds, classes which exhibit a natural identity in the same way as monophyla do, for example, which also have common descent as an objective property. Against this background, ontology‐based morphology can be a valuable addition to phylogenetic systematics. Formalized, machine‐parsable descriptions permit the generalization of morphemes in large datasets and can facilitate the recognition of identical character states. However, the expertise of the researcher is indispensable, and full automation of the transfer from the descriptive level to the evolutionary level thus appears impossible.

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Section: Introductionmentioning

confidence: 99%

The word is not enough: on morphemes, characters and ontological concepts

Göpel

Richter

2016

Cladistics

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“…In fact, our introduced approach, based on the insights from category theory, can be employed to develop algorithms and tools to assist ontology change management. In our recent experiments, we have applied the introduced agent-based method, formalized with category theory, in several biomedical applications, including the management of requirement volatility in e-health systems [21] and analyzing the evolutionary relationships between fungal species [22]. Currently, we are working to improve our rulebased graph transformation method and extend it to cover hierarchical distributed graphs, which support nested hierarchies in different levels of abstraction.…”

Section: Identity Preservation In Rlrmentioning

confidence: 99%

Strategic Health Information Management and Forecast: The Birdwatching Approach

Shaban-Nejad

Haarslev

2010

Computational Collective Intelligence. Technologies and Applications

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Abstract.To facilitate communication and the exchange of information between patients, nurses, lab technicians, health insurers, physicians, policy makers, and existing knowledge-based systems, a set of shared standard terminologies and controlled vocabularies are necessary. In modern health information management systems, these vocabularies are defined within formal representations called ontologies, where terminologies are only meaningful once linked to a descriptive dataset. When the datasets and their conveyed knowledge are changed, the ontological structure is altered accordingly. Despite the importance of this topic, the problem of managing evolving ontological structures is inadequately addressed by available tools and algorithms, partly because handling ontological change is not a purely computational affair. In this paper, we propose a framework inspired by a social activity, birdwatching. Using this model, the evolving ontological structures can be monitored and analyzed based on their state at a given time. Moreover, patterns of changes can be derived and used to predict and approximate a system's behavior based on potential future changes.

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