Engineering Temporal and Spatial Aspects in OWL using Patterns

WWW is a huge, open, heterogeneous system, however its contents data is mainly human oriented. The Semantic Web needs to assure that data is readable and “understandable” to intelligent software agents, though the use of explicit and formal semantics. Ontologies constitute a privileged artifact for capturing the semantic of the WWW data. Temporal and spatial dimensions are transversal to the generality of knowledge domains and therefore are fundamental for the reasoning process of software agents. Representing temporal/spatial evolution of concepts and their relations in OWL (W3C standard for ontologies) it is not straightforward. Although proposed several strategies to tackle this problem but there is still no formal and standard approach. This work main goal consists of development of methods/tools to support the engineering of temporal and spatial aspects in intelligent systems through the use of OWL ontologies. An existing method for ontology engineering, Fonte was used as framework for the development of this work. As main contributions of this work Fonte was re-engineered in order to: i) support the spatial dimension; ii) work with OWL Ontologies; iii) and support the application of Ontology Design Patterns. Finally, the capabilities of the proposed approach were demonstrated by engineering time and space in a demo ontology about football.A World WideWeb (WWW) é uma rede de dados enorme, aberta, muito rica, heterogénea e não controlada. Contudo, os dados existentes na rede são principalmente orientados ao consumo humano. A Semantic Web, de acordo com a perspectiva de Berners-Lee, deve fornecer condições para que a informação publicada seja lida e interpretada/compreendida por máquinas (agentes), através do enriquecimento semântico formal e explícito. As ontologias são a especificação formal de uma conceptualização partilhada e como tal constituem um artefacto privilegiado para capturar a semântica de um modelo. O formato standard proposto pela W3C (World Wide Web Consortium) para a representação de ontologias no contexto da WWW é o OWL (Web Ontology Language). As dimensões temporal e espacial são transversais à generalidade dos domínios. No processo de entendimento e raciocínio por agentes é crucial a consideração das dimensões temporal e espacial, em particular em tarefas como a análise de narrativas, contextualização, processamento de língua natural ou planeamento. Por exemplo, uma pessoa pode desempenhar vários papéis numa organização no decorrer do tempo; um objecto passa por diversas fases durante o processo de fabrico; ou o planeamento de uma viagem à Europa deve obedecer a diversas restrições temporais e espaciais. Apesar de os humanos demonstrarem uma capacidade inata para lidar com o tempo e o espaço, os agentes inteligentes de software precisam de especificações formais. Contudo, apesar da vasta investigação que tem sido levada a cabo no domínio da engenharia temporal/espacial esta é ainda uma tarefa complexa, trabalhosa e sujeita a erros, visto que é necessário ter conhecimento específico sobre o domínio a modelar e também sobre as teorias que modelam/capturam o tempo e o espaço. Integrar as dimensões temporal e espacial em sistemas inteligentes é uma tarefa complexa e propensa a erros, principalmente porque: 1. muitas vezes o Engenheiro de Conhecimento tem uma percepção intuitiva e informal do tempo e do espaço, enquanto os modelos existentes são formais e complexos, resultando em sistemas nos quais não é possível explorar adequadamente estas dimensões; 2. as dimensões extra, resultantes das componentes temporal e espacial, tornam a ontologia mais complexa, aumentando a dificuldade do processo de verificação e a garantia da completude e consistência do sistema; 3. diferentes intervenientes têm diferentes percepções do tempo e do espaço. Em particular, representar e raciocinar sobre a evolução temporal de conceitos e suas relações considerando ontologias em OWL enfrenta problemas adicionais. A linguagem OWL baseia-se na utilização de relações binárias, o que lhe confere enormes vantagens no processamento automático mas que impõe limitações ao nível da expressividade, tornando complexo representar relações que envolvam mais do que dois argumentos (como por exemplo a caracterização temporal ou espacial de relações). A comunidade científica tem estudado várias formas para fazer face a este problema, nomeadamente: 1. extensões da Lógica Descritiva (DL) com operadores temporais e espaciais; 2. extensões do esquema formal do OWL; 3. aplicação de técnicas de gestão de versões permitindo registar o histórico da evolução da ontologia; 4. ou ainda a criação de esquemas mais complexos para a representação da informação como a criação de conceitos auxiliares para simular a existência de relações n-árias. O principal objectivo deste trabalho consistiu no desenvolvimento de métodos e ferramentas capazes de suportar a engenharia de aspectos temporais e espaciais em sistemas inteligentes através da utilização de ontologias codificadas na linguagem OWL. Uma metodologia de engenharia de ontologias existente chamada Fonte foi utilizada como framework no desenvolvimento deste trabalho. Este método foi aplicado com sucesso na engenharia de aspectos temporais em sistemas inteligentes utilizando ontologias no formato F-Logic. O Fonte utiliza uma abordagem de dividir-para-conquistar de forma que a modelação de domínios complexos pode ser realizada através da composição de diferentes ontologias que definem as diferentes categorias de conhecimento envolvidas no domínio. O Fonte foi utilizado na engenharia dos aspectos temporais em ontologias. Como resultado deste trabalho foi realizada a reengenharia do método Fonte de forma a suportar também a dimensão espacial e a aplicação semiautomática de padrões de desenvolvimento de ontologias (PDO). Em particular este trabalho consistiu no desenvolvimento de: 1. uma linguagem de regras que permite a implementação de PDO e a sua aplicação através da metodologia Fonte 2. mecanismos de verificação que garantem a consistência da ontologia de domínio durante o processo de engenharia; 3. mecanismos de criação automática de propostas baseados em algoritmos de pesquisa semântica e estrutural; 4. ferramenta gráfica de suporte ao método Fonte. As capacidades da metodologia e ferramentas propostas e desenvolvidas foram demonstradas através da engenharia temporal e espacial de uma ontologia do domínio do futebol

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types

Consistent patterns of common species across tropical tree communities

Trees structure the Earth’s most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations1,2,3,4,5,6 in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth’s 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories7, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world’s most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.Publisher PDFPeer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution