Site-Specific Rules Extraction in Precision Agriculture

El incremento sostenible en la producción alimentaria para satisfacer las necesidades de una población mundial en aumento es un verdadero reto cuando tenemos en cuenta el impacto constante de plagas y enfermedades en los cultivos. Debido a las importantes pérdidas económicas que se producen, el uso de tratamientos químicos es demasiado alto; causando contaminación del medio ambiente y resistencia a distintos tratamientos. En este contexto, la comunidad agrícola divisa la aplicación de tratamientos más específicos para cada lugar, así como la validación automática con la conformidad legal. Sin embargo, la especificación de estos tratamientos se encuentra en regulaciones expresadas en lenguaje natural. Por este motivo, traducir regulaciones a una representación procesable por máquinas está tomando cada vez más importancia en la agricultura de precisión.Actualmente, los requisitos para traducir las regulaciones en reglas formales están lejos de ser cumplidos; y con el rápido desarrollo de la ciencia agrícola, la verificación manual de la conformidad legal se torna inabordable.En esta tesis, el objetivo es construir y evaluar un sistema de extracción de reglas para destilar de manera efectiva la información relevante de las regulaciones y transformar las reglas de lenguaje natural a un formato estructurado que pueda ser procesado por máquinas. Para ello, hemos separado la extracción de reglas en dos pasos. El primero es construir una ontología del dominio; un modelo para describir los desórdenes que producen las enfermedades en los cultivos y sus tratamientos. El segundo paso es extraer información para poblar la ontología. Puesto que usamos técnicas de aprendizaje automático, implementamos la metodología MATTER para realizar el proceso de anotación de regulaciones. Una vez creado el corpus, construimos un clasificador de categorías de reglas que discierne entre obligaciones y prohibiciones; y un sistema para la extracción de restricciones en reglas, que reconoce información relevante para retener el isomorfismo con la regulación original. Para estos componentes, empleamos, entre otra técnicas de aprendizaje profundo, redes neuronales convolucionales y “Long Short- Term Memory”. Además, utilizamos como baselines algoritmos más tradicionales como “support-vector machines” y “random forests”.Como resultado, presentamos la ontología PCT-O, que ha sido alineada con otras ontologías como NCBI, PubChem, ChEBI y Wikipedia. El modelo puede ser utilizado para la identificación de desórdenes, el análisis de conflictos entre tratamientos y la comparación entre legislaciones de distintos países. Con respecto a los sistemas de extracción, evaluamos empíricamente el comportamiento con distintas métricas, pero la métrica F1 es utilizada para seleccionar los mejores sistemas. En el caso del clasificador de categorías de reglas, el mejor sistema obtiene un macro F1 de 92,77% y un F1 binario de 85,71%. Este sistema usa una red “bidirectional long short-term memory” con “word embeddings” como entrada. En relación al extractor de restricciones de reglas, el mejor sistema obtiene un micro F1 de 88,3%. Este extractor utiliza como entrada una combinación de “character embeddings” junto a “word embeddings” y una red neuronal “bidirectional long short-term memory”.<br /

Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Modelling and accessing regulatory knowledge for computer-assisted compliance audit

The ingredients for an effective automated audit of a building design include a building model containing the design information, a computerised regulatory knowledge model, and a practical method of processing these computable representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of industry-wide standardisation. The current research project, therefore, focuses on investigating the use of the industry standard building information model and the adoption of open standard legal knowledge interchange and executable workflow models for automating conventional compliant design processes. This paper provides a non-exhaustive overview of common approaches to model and access regulatory knowledge for a compliance audit. The strengths and weaknesses of two comparative open standard knowledge representation approaches are discussed using an example regulatory document

Business rules based legacy system evolution towards service-oriented architecture.

Enterprises can be empowered to live up to the potential of becoming dynamic, agile and real-time. Service orientation is emerging from the amalgamation of a number of key business, technology and cultural developments. Three essential trends in particular are coming together to create a new revolutionary breed of enterprise, the service-oriented enterprise (SOE): (1) the continuous performance management of the enterprise; (2) the emergence of business process management; and (3) advances in the standards-based service-oriented infrastructures. This thesis focuses on this emerging three-layered architecture that builds on a service-oriented architecture framework, with a process layer that brings technology and business together, and a corporate performance layer that continually monitors and improves the performance indicators of global enterprises provides a novel framework for the business context in which to apply the important technical idea of service orientation and moves it from being an interesting tool for engineers to a vehicle for business managers to fundamentally improve their businesses

Large-Scale Legal Reasoning with Rules and Databases

Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing rangeof scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases

RULE BASED ADAPTATION: LITERATURE REVIEW

Rule based adaptive systems are growing in popularity and rules have been considered as an effective and elastic way to adapt systems. A rule based approach allows transparent monitoring of performed adaptation actions and gives an important advantage of easily modifiable adaptation process. The goal of this paper is to summarize literature review on rule based adaptation systems. The emphasis is put on rule types, semantics used for defining rules and measurement of effectiveness and correctness of rule based adaptation systems. The literature review has been done following a systematic approach consisting of three steps: planning, reviewing and analysis. Targeted research questions have been used to guide the review process. The review results are to be used for conducting further research in the area of rule based context-aware adaptive systems. This paper accents the potential of using rules as means to perform adaptive actions in enterprise applications taking into account contextual factors as well as points challenges, difficulties and open issues for planning, developing, implementing and running of such systems

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

An Automated Framework for the Extraction of Semantic Legal Metadata from Legal Texts

Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97.2% and 82.4% and recall scores of 94.9% and 92.4%