The Ecological Interpretation of Unbiased Estimator for the Taxonomic Ratio: Different Approaches for Local and Regional Flora

Taxonomic ratio in an ecological context is considered as an indicator of the level of competitive exclusion. In spite of more than a century of discussions on taxonomic ratio, the problem of finding an unbiased estimator for flora characterisation remains unsolved. The traditional form of taxonomic ratio (species/genus or species/families ratio) is biased, which depends on the area of territory for which the floral composition was established. This circumstance makes the taxonomic ratio an inadequate characteristic of the flora. To solve the problem of finding an unbiased estimator for the taxonomic ratio, we have combined two fundamental ecological generalisations. The first is that species that belong to the same genus usually live in similar habitats and have similar morphological features. The struggle for life between species from the same genus is, therefore, more intense than between species from different genera. The second is species–area relationship. We have considered the problem of finding an unbiased taxonomic relationship using the Arrhenius curves to fit species–area relationships. This combination allowed us to find a form of unbiased taxonomic relationship. The example of Cyanophyceae flora shows that this indicator is closely related to a wide range of ecological and biogeographical characteristics of vegetation. The residual of the linear equation of dependence of the logarithm of the number of species on the logarithm of the number of genera is an unbiased indicator of the taxonomic relation, which is independent of the number of genera (or number of families) and the sampling size (or area). An unbiased taxonomic relationship is a characteristic of regional flora, which depends on a wide range of its ecological and biogeographical features.

Phylogenetic analysis of forty Solanum melongena L. accessions by SDS-page

Work was carried out to resolve the existing intraspecific taxonomic relation and protein richest accessions of Solanum melongena by using SDS-PAGE with the reference of their genetic diversity. Phylogenetic relatedness within samples was studied by cluster analysis using an Unweighted Pair Group Method with Arithmetic Mean (UPGMA) to construct a dendrogram. Electrophoretogram of accessions No. (1-19) 018477, 018482 (Faisalabad), 18484 (Sahiwal), whereas accessions from 20 - 40 from D. I Khan (18504, 18500, 18505, 14466(3), Sahiwal (20344) and Batgram (20509) was unique in protein banding position. Largest dendrogram of cluster 1 divided into 6 (6a,b), 7 (7a,b), 8 (8a,b) and 9 (9a,b) sub clusters including accessions 20425 - 4745(3). The results demonstrated that accessions have low level of genetic diversity and almost similar protein contents. No relationship was found between genetic divergence and genetic status of the samples.

A revision of taxonomic relation between Oenothera royfraseri and O. turoviensis (sect. Oenothera, subsect. Oenothera; Onagraceae) based on multivariate analyses of morphological characters

The taxonomic relation between Oenothera royfraseri and O. turoviensis (sect. Oenothera, subsect. Oenothera; Onagraceae) has remained unresolved. According to the representatives of the so-called American school of taxonomy (W. Dietrich, P.H. Raven, W.L. Wagner) the former name is one of almost 70 synonyms of widely treated O. biennis (AB-II plastome-genome combination) while the latter is a synonym of O. parviflora (BC-IV arrangement). On the other hand, European researchers (K. Rostański, A. Soldano, V. Jehlík) tend to assign both names to one species, under the name O. royfraseri. In order to establish the taxonomic relation of the studied taxa, morphometric comparisons, based on qualitative and quantitative traits, were carried out. The studies included European specimens labelled as O. royfraseri and/or O. turoviensis (with the nomenclatural types of the two names) as well as representatives of the two other species, O. biennis and O. parviflora, which were taken as a background. The performed multivariate statistical analyses (correspondence analysis, principal component analysis, discriminant analysis followed by canonical discriminant analysis) provided strong evidence supporting the American’s hypothesis on separateness of the two species. As it was demonstrated, O. royfraseri and O. turoviensis differ mostly by the sepal tips arrangement, which is considered by American and European researchers as one of the most essential variables in taxa recognition within the group. The obtained results have also indicated that O. royfraseri is distinct from European representatives of O. biennis, which is partially concordant with Rostański’s opinion. The two last-mentioned species can be discriminated by red vs. green papillae, strigillose vs. glandular hair predominance, respectively, as well as by quantitative features of the flowers, which are significantly larger in O. biennis.

Towards relation extraction from Arabic text: a review

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

A Semantic Framework for Extracting Taxonomic Relations from Text Corpus

Nowadays, ontologies have been exploited in many current applications due to the abilities in representing knowledge and inferring new knowledge. However, the manual construction of ontologies is tedious and time-consuming. Therefore, the automated ontology construction from text has been investigated. The extraction of taxonomic relations between concepts is a crucial step in constructing domain ontologies. To obtain taxonomic relations from a text corpus, especially when the data is deficient, the approach of using the web as a source of collective knowledge (a.k.a web-based approach) is usually applied. The important challenge of this approach is how to collect relevant knowledge from a large amount of web pages. To overcome this issue, we propose a framework that combines Word Sense Disambiguation (WSD) and web approach to extract taxonomic relations from a domain-text corpus. This framework consists of two main stages: concept extraction and taxonomic-relation extraction. Concepts acquired from the concept-extraction stage are disambiguated through WSD module and passed to stage of extraction taxonomic relations afterward. To evaluate the efficiency of the proposed framework, we conduct experiments on datasets about two domains of tourism and sport. The obtained results show that the proposed method is efficient in corpora which are insufficient or have no training data. Besides, the proposed method outperforms the state of the art method in corpora having high WSD results.