Detecting and Diagnosing Syntactic and Semantic Errors in SPARQL Queries

ABSTRACT In this paper we present a tool to syntactically and semantically validate SPARQL queries. With this aim, we extract triple patterns and filter conditions from SPARQL queries and we use the OWL API and an OWL ontology reasoner in order to detect wrong expressions. Given an ontology and a query, the tool reports di↵erent kinds of programming errors: wrong use of vocabulary, wrong use of resources and literals, wrong filter conditions and wrong use of variables in triple patterns and filter conditions. When the OWL ontology reasoner is used the tool reports a diagnosis

Ensuring the Semantic Correctness of Workflow Processes: An Ontological Approach

International audienceWorkflow verification has been known as an important as-pect of workflow management systems. Many existing approaches con-centrate on ensuring the correctness of workflow processes at the syntac-tic level. However, these approaches are not sufficient to detect errors at the semantic level. This paper contributes to ensure the semantic cor-rectness of workflow processes. First, we propose a formal definition of semantic constraints and an O(n 3)-time algorithm for detecting redun-dant and conflicting constraints. Second, by relying on the CPN Ontology (a representation of Coloured Petri Nets with OWL DL ontology) and sets of semantic constraints, workflow processes are semantically created. And third, we show how to check the semantic correctness of workflow processes with the SPARQL query language

La vérification de patrons de workflow métier basés sur les flux de contrôle : une approche utilisant les systèmes à base de connaissances

This thesis tackles the problem of modelling semantically rich business workflow templates and proposes a process for developing workflow templates. The objective of the thesis is to transform a business process into a control flow-based business workflow template that guarantees syntactic and semantic validity. The main challenges are: (i) to define formalism for representing business processes; (ii) to establish automatic control mechanisms to ensure the correctness of a business workflow template based on a formal model and a set of semantic constraints; and (iii) to organize the knowledge base of workflow templates for a workflow development process. We propose a formalism which combines control flow (based on Coloured Petri Nets (CPNs)) with semantic constraints to represent business processes. The advantage of this formalism is that it allows not only syntactic checks based on the model of CPNs, but also semantic checks based on Semantic Web technologies. We start by designing an OWL ontology called the CPN ontology to represent the concepts of CPN-based business workflow templates. The design phase is followed by a thorough study of the properties of these templates in order to transform them into a set of axioms for the CPN ontology. In this formalism, a business process is syntactically transformed into an instance of the CPN ontology. Therefore, syntactic checking of a business process becomes simply verification by inference, by concepts and by axioms of the CPN ontology on the corresponding instance.Cette thèse traite le problème de la modélisation des patrons de workflow sémantiquement riche et propose un processus pour développer des patrons de workflow. L'objectif est de transformer un processus métier en un patron de workflow métier basé sur les flux de contrôle qui garantit la vérification syntaxique et sémantique. Les défis majeurs sont : (i) de définir un formalisme permettant de représenter les processus métiers; (ii) d'établir des mécanismes de contrôle automatiques pour assurer la conformité des patrons de workflow métier basés sur un modèle formel et un ensemble de contraintes sémantiques; et (iii) d’organiser la base de patrons de workflow métier pour le développement de patrons de workflow. Nous proposons un formalisme qui combine les flux de contrôle (basés sur les Réseaux de Petri Colorés (CPNs)) avec des contraintes sémantiques pour représenter les processus métiers. L'avantage de ce formalisme est qu'il permet de vérifier non seulement la conformité syntaxique basée sur le modèle de CPNs mais aussi la conformité sémantique basée sur les technologies du Web sémantique. Nous commençons par une phase de conception d'une ontologie OWL appelée l’ontologie CPN pour représenter les concepts de patrons de workflow métier basés sur CPN. La phase de conception est suivie par une étude approfondie des propriétés de ces patrons pour les transformer en un ensemble d'axiomes pour l'ontologie. Ainsi, dans ce formalisme, un processus métier est syntaxiquement transformé en une instance de l’ontologie

Federated knowledge base debugging in DL-Lite A

Due to the continuously growing amount of data the federation of different and distributed data sources gained increasing attention. In order to tackle the challenge of federating heterogeneous sources a variety of approaches has been proposed. Especially in the context of the Semantic Web the application of Description Logics is one of the preferred methods to model federated knowledge based on a well-defined syntax and semantics. However, the more data are available from heterogeneous sources, the higher the risk is of inconsistency – a serious obstacle for performing reasoning tasks and query answering over a federated knowledge base. Given a single knowledge base the process of knowledge base debugging comprising the identification and resolution of conflicting statements have been widely studied while the consideration of federated settings integrating a network of loosely coupled data sources (such as LOD sources) has mostly been neglected. In this thesis we tackle the challenging problem of debugging federated knowledge bases and focus on a lightweight Description Logic language, called DL-LiteA, that is aimed at applications requiring efficient and scalable reasoning. After introducing formal foundations such as Description Logics and Semantic Web technologies we clarify the motivating context of this work and discuss the general problem of information integration based on Description Logics. The main part of this thesis is subdivided into three subjects. First, we discuss the specific characteristics of federated knowledge bases and provide an appropriate approach for detecting and explaining contradictive statements in a federated DL-LiteA knowledge base. Second, we study the representation of the identified conflicts and their relationships as a conflict graph and propose an approach for repair generation based on majority voting and statistical evidences. Third, in order to provide an alternative way for handling inconsistency in federated DL-LiteA knowledge bases we propose an automated approach for assessing adequate trust values (i.e., probabilities) at different levels of granularity by leveraging probabilistic inference over a graphical model. In the last part of this thesis, we evaluate the previously developed algorithms against a set of large distributed LOD sources. In the course of discussing the experimental results, it turns out that the proposed approaches are sufficient, efficient and scalable with respect to real-world scenarios. Moreover, due to the exploitation of the federated structure in our algorithms it further becomes apparent that the number of identified wrong statements, the quality of the generated repair as well as the fineness of the assessed trust values profit from an increasing number of integrated sources

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Modelo de acesso a fontes em linguagem natural no governo electrónico

Doutoramento em Engenharia InformáticaFor the actual existence of e-government it is necessary and crucial to provide public information and documentation, making its access simple to citizens. A portion, not necessarily small, of these documents is in an unstructured form and in natural language, and consequently outside of which the current search systems are generally able to cope and effectively handle. Thus, in thesis, it is possible to improve access to these contents using systems that process natural language and create structured information, particularly if supported in semantics. In order to put this thesis to test, this work was developed in three major phases: (1) design of a conceptual model integrating the creation of structured information and making it available to various actors, in line with the vision of e-government 2.0; (2) definition and development of a prototype instantiating the key modules of this conceptual model, including ontology based information extraction supported by examples of relevant information, knowledge management and access based on natural language; (3) assessment of the usability and acceptability of querying information as made possible by the prototype - and in consequence of the conceptual model - by users in a realistic scenario, that included comparison with existing forms of access. In addition to this evaluation, at another level more related to technology assessment and not to the model, evaluations were made on the performance of the subsystem responsible for information extraction. The evaluation results show that the proposed model was perceived as more effective and useful than the alternatives. Associated with the performance of the prototype to extract information from documents, comparable to the state of the art, results demonstrate the feasibility and advantages, with current technology, of using natural language processing and integration of semantic information to improve access to unstructured contents in natural language. The conceptual model and the prototype demonstrator intend to contribute to the future existence of more sophisticated search systems that are also more suitable for e-government. To have transparency in governance, active citizenship, greater agility in the interaction with the public administration, among others, it is necessary that citizens and businesses have quick and easy access to official information, even if it was originally created in natural language.Para a efectiva existência de governo electrónico é necessário e crucial a disponibilização de informação e documentação pública e tornar simples o acesso a esta pelos cidadãos. Uma parte, não necessariamente pequena, destes documentos encontra-se sob uma forma não estruturada e em linguagem natural e, consequentemente, fora do que os sistemas de pesquisa actuais conseguem em geral suportar e disponibilizar eficazmente. Assim, em tese, é possível melhorar o acesso a estes conteúdos com recurso a sistemas que processem linguagem natural e que sejam capazes de criar informação estruturada, em especial se suportados numa semântica. Com o objectivo de colocar esta tese à prova, o desenvolvimento deste trabalho integrou três grandes fases ou vertentes: (1) Criação de um modelo conceptual integrando a criação de informação estruturada e a sua disponibilização para vários actores, alinhado com a visão do governo electrónico 2.0; (2) Definição e desenvolvimento de um protótipo instanciando os módulos essenciais deste modelo conceptual, nomeadamente a extracção de informação suportada em ontologias e exemplos de informação relevante, gestão de conhecimento e acesso baseado em linguagem natural; (3) Uma avaliação de usabilidade e aceitabilidade da consulta à informação tornada possível pelo protótipo – e em consequência do modelo conceptual - por utilizadores num cenário realista e que incluiu comparação com formas de acesso existentes. Além desta avaliação, a outro nível, mais relacionado com avaliação de tecnologias e não do modelo, foram efectuadas avaliações do desempenho do subsistema responsável pela extracção de informação. Os resultados da avaliação mostram que o modelo proposto foi percepcionado como mais eficaz e mais útil que as alternativas. Associado ao desempenho do protótipo a extrair informação dos documentos, comparável com o estado da arte, os resultados obtidos mostram a viabilidade e as vantagens, com a tecnologia actual, de utilizar processamento de linguagem natural e integração de informação semântica para melhorar acesso a conteúdos em linguagem natural e não estruturados. O modelo conceptual e o protótipo demonstrador pretendem contribuir para a existência futura de sistemas de pesquisa mais sofisticados e adequados ao governo electrónico. Para existir transparência na governação, cidadania activa, maior agilidade na interacção com a administração pública, entre outros, é necessário que cidadãos e empresas tenham acesso rápido e fácil a informação oficial, mesmo que ela tenha sido originalmente criada em linguagem natural

Semantic-based framework for the generation of travel demand

Traffic and transportation have a wide-ranging impact on the daily lives of the human population and society. Activity-based travel demand generation models and traffic simulators are tools that have been developed to investigate traffic and transport problems and assist in developing solutions. The closer modelling of human behaviour, the emergence of new technologies and the availability of more detailed datasets is leading to greater modelling complexity. The robustness of conclusions in investigations is supported by comparison of multiple techniques and models yet variations in the platform, data requirements and dataset availability present barriers to their breadth. This thesis investigates the development of a Semantic Web framework for activity-based travel demand generation. It is proposed that the application of a knowledge-based approach and development of an orchestrating framework will enable a loosely coupled modular architecture. This approach will reduce the burden in preparing and accessing datasets through the construction of a platform-independent knowledge-base and facilitate switching between modules and datasets. The principal contributions of this work are the application of a knowledge-based approach to travel demand generation; the development of a Semantic-based framework to control the configuration of the process and the design; and demonstration of the Semantic based framework through the implementation and evaluation of the modular travel demand generation process, including integration with two third-party traffic simulators. The investigation found that the proposed approach can be successfully applied to model and control the travel demand generation process. Multiple configurations were explored, including utilising network communications, and found that this had a noticeable impact on execution duration but also the potential for mitigation through distributed computing. This presents the opportunity for an online infrastructure of datasets and module implementations for travel demand generation that users can select and access through the framework. This infrastructure would remove the need for ad hoc interfaces; data format conversion or platform dependence to facilitate the process of traffic modelling becoming quicker and more robust

Semantic Management of Location-Based Services in Wireless Environments

En los últimos años el interés por la computación móvil ha crecido debido al incesante uso de dispositivos móviles (por ejemplo, smartphones y tablets) y su ubicuidad. El bajo coste de dichos dispositivos unido al gran número de sensores y mecanismos de comunicación que equipan, hace posible el desarrollo de sistemas de información útiles para sus usuarios. Utilizando un cierto tipo especial de sensores, los mecanismos de posicionamiento, es posible desarrollar Servicios Basados en la Localización (Location-Based Services o LBS en inglés) que ofrecen un valor añadido al considerar la localización de los usuarios de dispositivos móviles para ofrecerles información personalizada. Por ejemplo, se han presentado numerosos LBS entre los que se encuentran servicios para encontrar taxis, detectar amigos en las cercanías, ayudar a la extinción de incendios, obtener fotos e información de los alrededores, etc. Sin embargo, los LBS actuales están diseñados para escenarios y objetivos específicos y, por lo tanto, están basados en esquemas predefinidos para el modelado de los elementos involucrados en estos escenarios. Además, el conocimiento del contexto que manejan es implícito; razón por la cual solamente funcionan para un objetivo específico. Por ejemplo, en la actualidad un usuario que llega a una ciudad tiene que conocer (y comprender) qué LBS podrían darle información acerca de medios de transporte específicos en dicha ciudad y estos servicios no son generalmente reutilizables en otras ciudades. Se han propuesto en la literatura algunas soluciones ad hoc para ofrecer LBS a usuarios pero no existe una solución general y flexible que pueda ser aplicada a muchos escenarios diferentes. Desarrollar tal sistema general simplemente uniendo LBS existentes no es sencillo ya que es un desafío diseñar un framework común que permita manejar conocimiento obtenido de datos enviados por objetos heterogéneos (incluyendo datos textuales, multimedia, sensoriales, etc.) y considerar situaciones en las que el sistema tiene que adaptarse a contextos donde el conocimiento cambia dinámicamente y en los que los dispositivos pueden usar diferentes tecnologías de comunicación (red fija, inalámbrica, etc.). Nuestra propuesta en la presente tesis es el sistema SHERLOCK (System for Heterogeneous mobilE Requests by Leveraging Ontological and Contextual Knowledge) que presenta una arquitectura general y flexible para ofrecer a los usuarios LBS que puedan serles interesantes. SHERLOCK se basa en tecnologías semánticas y de agentes: 1) utiliza ontologías para modelar la información de usuarios, dispositivos, servicios, y el entorno, y un razonador para manejar estas ontologías e inferir conocimiento que no ha sido explicitado; 2) utiliza una arquitectura basada en agentes (tanto estáticos como móviles) que permite a los distintos dispositivos SHERLOCK intercambiar conocimiento y así mantener sus ontologías locales actualizadas, y procesar peticiones de información de sus usuarios encontrando lo que necesitan, allá donde esté. El uso de estas dos tecnologías permite a SHERLOCK ser flexible en términos de los servicios que ofrece al usuario (que son aprendidos mediante la interacción entre los dispositivos), y de los mecanismos para encontrar la información que el usuario quiere (que se adaptan a la infraestructura de comunicación subyacente)

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions