Fast modularisation and aomic decomposition of ontologies using axiom dependency hypergraphs

In this paper we define the notion of an axiom dependency hypergraph, which explicitly represents how axioms are included into a module by the algorithm for computing locality-based modules. A locality-based module of an ontology corresponds to a set of connected nodes in the hypergraph, and atoms of an ontology to strongly connected components. Collapsing the strongly connected components into single nodes yields a condensed hypergraph that comprises a representation of the atomic decomposition of the ontology. To speed up the condensation of the hypergraph, we first reduce its size by collapsing the strongly connected components of its graph fragment employing a linear time graph algorithm. This approach helps to significantly reduce the time needed for computing the atomic decomposition of an ontology. We provide an experimental evaluation for computing the atomic decomposition of large biomedical ontologies. We also demonstrate a significant improvement in the time needed to extract locality-based modules from an axiom dependency hypergraph and its condensed version.

Monitorización inteligente de redes de comunicación

El avance tecnológico de los últimos años ha aumentado la necesidad de guardar enormes cantidades de datos de forma masiva, llegando a una situación de desorden en el proceso de almacenamiento de datos, a su desactualización y a complicar su análisis. Esta situación causó un gran interés para las organizaciones en la búsqueda de un enfoque para obtener información relevante de estos grandes almacenes de datos. Surge así lo que se define como inteligencia de negocio, un conjunto de herramientas, procedimientos y estrategias para llevar a cabo la “extracción de conocimiento”, término con el que se refiere comúnmente a la extracción de información útil para la propia organización. Concretamente en este proyecto, se ha utilizado el enfoque Knowledge Discovery in Databases (KDD), que permite lograr la identificación de patrones y un manejo eficiente de las anomalías que puedan aparecer en una red de comunicaciones. Este enfoque comprende desde la selección de los datos primarios hasta su análisis final para la determinación de patrones. El núcleo de todo el enfoque KDD es la minería de datos, que contiene la tecnología necesaria para la identificación de los patrones mencionados y la extracción de conocimiento. Para ello, se utilizará la herramienta RapidMiner en su versión libre y gratuita, debido a que es más completa y de manejo más sencillo que otras herramientas como KNIME o WEKA. La gestión de una red engloba todo el proceso de despliegue y mantenimiento. Es en este procedimiento donde se recogen y monitorizan todas las anomalías ocasionadas en la red, las cuales pueden almacenarse en un repositorio. El objetivo de este proyecto es realizar un planteamiento teórico y varios experimentos que permitan identificar patrones en registros de anomalías de red. Se ha estudiado el repositorio de MAWI Lab, en el que se han almacenado anomalías diarias. Se trata de buscar indicios característicos anuales detectando patrones. Los diferentes experimentos y procedimientos de este estudio pretenden demostrar la utilidad de la inteligencia de negocio a la hora de extraer información a partir de un almacén de datos masivo, para su posterior análisis o futuros estudios. ABSTRACT. The technological progresses in the recent years required to store a big amount of information in repositories. This information is often in disorder, outdated and needs a complex analysis. This situation has caused a relevant interest in investigating methodologies to obtain important information from these huge data stores. Business intelligence was born as a set of tools, procedures and strategies to implement the "knowledge extraction". Specifically in this project, Knowledge Discovery in Databases (KDD) approach has been used. KDD is one of the most important processes of business intelligence to achieve the identification of patterns and the efficient management of the anomalies in a communications network. This approach includes all necessary stages from the selection of the raw data until the analysis to determine the patterns. The core process of the whole KDD approach is the Data Mining process, which analyzes the information needed to identify the patterns and to extract the knowledge. In this project we use the RapidMiner tool to carry out the Data Mining process, because this tool has more features and is easier to use than other tools like WEKA or KNIME. Network management includes the deployment, supervision and maintenance tasks. Network management process is where all anomalies are collected, monitored, and can be stored in a repository. The goal of this project is to construct a theoretical approach, to implement a prototype and to carry out several experiments that allow identifying patterns in some anomalies records. MAWI Lab repository has been selected to be studied, which contains daily anomalies. The different experiments show the utility of the business intelligence to extract information from big data warehouse.

Semantic Service Discovery Techniques for the composable web

This PhD thesis contributes to the problem of resource and service discovery in the context of the composable web. In the current web, mashup technologies allow developers reusing services and contents to build new web applications. However, developers face a problem of information flood when searching for appropriate services or resources for their combination. To contribute to overcoming this problem, a framework is defined for the discovery of services and resources. In this framework, three levels are defined for performing discovery at content, discovery and agente levels. The content level involves the information available in web resources. The web follows the Representational Stateless Transfer (REST) architectural style, in which resources are returned as representations from servers to clients. These representations usually employ the HyperText Markup Language (HTML), which, along with Content Style Sheets (CSS), describes the markup employed to render representations in a web browser. Although the use of SemanticWeb standards such as Resource Description Framework (RDF) make this architecture suitable for automatic processes to use the information present in web resources, these standards are too often not employed, so automation must rely on processing HTML. This process, often referred as Screen Scraping in the literature, is the content discovery according to the proposed framework. At this level, discovery rules indicate how the different pieces of data in resources’ representations are mapped onto semantic entities. By processing discovery rules on web resources, semantically described contents can be obtained out of them. The service level involves the operations that can be performed on the web. The current web allows users to perform different tasks such as search, blogging, e-commerce, or social networking. To describe the possible services in RESTful architectures, a high-level feature-oriented service methodology is proposed at this level. This lightweight description framework allows defining service discovery rules to identify operations in interactions with REST resources. The discovery is thus performed by applying discovery rules to contents discovered in REST interactions, in a novel process called service probing. Also, service discovery can be performed by modelling services as contents, i.e., by retrieving Application Programming Interface (API) documentation and API listings in service registries such as ProgrammableWeb. For this, a unified model for composable components in Mashup-Driven Development (MDD) has been defined after the analysis of service repositories from the web. The agent level involves the orchestration of the discovery of services and contents. At this level, agent rules allow to specify behaviours for crawling and executing services, which results in the fulfilment of a high-level goal. Agent rules are plans that allow introspecting the discovered data and services from the web and the knowledge present in service and content discovery rules to anticipate the contents and services to be found on specific resources from the web. By the definition of plans, an agent can be configured to target specific resources. The discovery framework has been evaluated on different scenarios, each one covering different levels of the framework. Contenidos a la Carta project deals with the mashing-up of news from electronic newspapers, and the framework was used for the discovery and extraction of pieces of news from the web. Similarly, in Resulta and VulneraNET projects the discovery of ideas and security knowledge in the web is covered, respectively. The service level is covered in the OMELETTE project, where mashup components such as services and widgets are discovered from component repositories from the web. The agent level is applied to the crawling of services and news in these scenarios, highlighting how the semantic description of rules and extracted data can provide complex behaviours and orchestrations of tasks in the web. The main contributions of the thesis are the unified framework for discovery, which allows configuring agents to perform automated tasks. Also, a scraping ontology has been defined for the construction of mappings for scraping web resources. A novel first-order logic rule induction algorithm is defined for the automated construction and maintenance of these mappings out of the visual information in web resources. Additionally, a common unified model for the discovery of services is defined, which allows sharing service descriptions. Future work comprises the further extension of service probing, resource ranking, the extension of the Scraping Ontology, extensions of the agent model, and contructing a base of discovery rules. Resumen La presente tesis doctoral contribuye al problema de descubrimiento de servicios y recursos en el contexto de la web combinable. En la web actual, las tecnologías de combinación de aplicaciones permiten a los desarrolladores reutilizar servicios y contenidos para construir nuevas aplicaciones web. Pese a todo, los desarrolladores afrontan un problema de saturación de información a la hora de buscar servicios o recursos apropiados para su combinación. Para contribuir a la solución de este problema, se propone un marco de trabajo para el descubrimiento de servicios y recursos. En este marco, se definen tres capas sobre las que se realiza descubrimiento a nivel de contenido, servicio y agente. El nivel de contenido involucra a la información disponible en recursos web. La web sigue el estilo arquitectónico Representational Stateless Transfer (REST), en el que los recursos son devueltos como representaciones por parte de los servidores a los clientes. Estas representaciones normalmente emplean el lenguaje de marcado HyperText Markup Language (HTML), que, unido al estándar Content Style Sheets (CSS), describe el marcado empleado para mostrar representaciones en un navegador web. Aunque el uso de estándares de la web semántica como Resource Description Framework (RDF) hace apta esta arquitectura para su uso por procesos automatizados, estos estándares no son empleados en muchas ocasiones, por lo que cualquier automatización debe basarse en el procesado del marcado HTML. Este proceso, normalmente conocido como Screen Scraping en la literatura, es el descubrimiento de contenidos en el marco de trabajo propuesto. En este nivel, un conjunto de reglas de descubrimiento indican cómo los diferentes datos en las representaciones de recursos se corresponden con entidades semánticas. Al procesar estas reglas sobre recursos web, pueden obtenerse contenidos descritos semánticamente. El nivel de servicio involucra las operaciones que pueden ser llevadas a cabo en la web. Actualmente, los usuarios de la web pueden realizar diversas tareas como búsqueda, blogging, comercio electrónico o redes sociales. Para describir los posibles servicios en arquitecturas REST, se propone en este nivel una metodología de alto nivel para descubrimiento de servicios orientada a funcionalidades. Este marco de descubrimiento ligero permite definir reglas de descubrimiento de servicios para identificar operaciones en interacciones con recursos REST. Este descubrimiento es por tanto llevado a cabo al aplicar las reglas de descubrimiento sobre contenidos descubiertos en interacciones REST, en un nuevo procedimiento llamado sondeo de servicios. Además, el descubrimiento de servicios puede ser llevado a cabo mediante el modelado de servicios como contenidos. Es decir, mediante la recuperación de documentación de Application Programming Interfaces (APIs) y listas de APIs en registros de servicios como ProgrammableWeb. Para ello, se ha definido un modelo unificado de componentes combinables para Mashup-Driven Development (MDD) tras el análisis de repositorios de servicios de la web. El nivel de agente involucra la orquestación del descubrimiento de servicios y contenidos. En este nivel, las reglas de nivel de agente permiten especificar comportamientos para el rastreo y ejecución de servicios, lo que permite la consecución de metas de mayor nivel. Las reglas de los agentes son planes que permiten la introspección sobre los datos y servicios descubiertos, así como sobre el conocimiento presente en las reglas de descubrimiento de servicios y contenidos para anticipar contenidos y servicios por encontrar en recursos específicos de la web. Mediante la definición de planes, un agente puede ser configurado para descubrir recursos específicos. El marco de descubrimiento ha sido evaluado sobre diferentes escenarios, cada uno cubriendo distintos niveles del marco. El proyecto Contenidos a la Carta trata de la combinación de noticias de periódicos digitales, y en él el framework se ha empleado para el descubrimiento y extracción de noticias de la web. De manera análoga, en los proyectos Resulta y VulneraNET se ha llevado a cabo un descubrimiento de ideas y de conocimientos de seguridad, respectivamente. El nivel de servicio se cubre en el proyecto OMELETTE, en el que componentes combinables como servicios y widgets se descubren en repositorios de componentes de la web. El nivel de agente se aplica al rastreo de servicios y noticias en estos escenarios, mostrando cómo la descripción semántica de reglas y datos extraídos permiten proporcionar comportamientos complejos y orquestaciones de tareas en la web. Las principales contribuciones de la tesis son el marco de trabajo unificado para descubrimiento, que permite configurar agentes para realizar tareas automatizadas. Además, una ontología de extracción ha sido definida para la construcción de correspondencias y extraer información de recursos web. Asimismo, un algoritmo para la inducción de reglas de lógica de primer orden se ha definido para la construcción y el mantenimiento de estas correspondencias a partir de la información visual de recursos web. Adicionalmente, se ha definido un modelo común y unificado para el descubrimiento de servicios que permite la compartición de descripciones de servicios. Como trabajos futuros se considera la extensión del sondeo de servicios, clasificación de recursos, extensión de la ontología de extracción y la construcción de una base de reglas de descubrimiento.

Semantic middleware development for the Internet of Things

After the extraordinary spread of the World Wide Web during the last fifteen years, engineers and developers are pushing now the Internet to its next border. A new conception in computer science and networks communication has been burgeoning during roughly the last decade: a world where most of the computers of the future will be extremely downsized, to the point that they will look like dust at its most advanced prototypes. In this vision, every single element of our “real” world has an intelligent tag that carries all their relevant data, effectively mapping the “real” world into a “virtual” one, where all the electronically augmented objects are present, can interact among them and influence with their behaviour that of the other objects, or even the behaviour of a final human user. This is the vision of the Internet of the Future, which also draws ideas of several novel tendencies in computer science and networking, as pervasive computing and the Internet of Things. As it has happened before, materializing a new paradigm that changes the way entities interrelate in this new environment has proved to be a goal full of challenges in the way. Right now the situation is exciting, with a plethora of new developments, proposals and models sprouting every time, often in an uncoordinated, decentralised manner away from any standardization, resembling somehow the status quo of the first developments of advanced computer networking, back in the 60s and the 70s. Usually, a system designed after the Internet of the Future will consist of one or several final user devices attached to these final users, a network –often a Wireless Sensor Network- charged with the task of collecting data for the final user devices, and sometimes a base station sending the data for its further processing to less hardware-constrained computers. When implementing a system designed with the Internet of the Future as a pattern, issues, and more specifically, limitations, that must be faced are numerous: lack of standards for platforms and protocols, processing bottlenecks, low battery lifetime, etc. One of the main objectives of this project is presenting a functional model of how a system based on the paradigms linked to the Internet of the Future works, overcoming some of the difficulties that can be expected and showing a model for a middleware architecture specifically designed for a pervasive, ubiquitous system. This Final Degree Dissertation is divided into several parts. Beginning with an Introduction to the main topics and concepts of this new model, a State of the Art is offered so as to provide a technological background. After that, an example of a semantic and service-oriented middleware is shown; later, a system built by means of this semantic and service-oriented middleware, and other components, is developed, justifying its placement in a particular scenario, describing it and analysing the data obtained from it. Finally, the conclusions inferred from this system and future works that would be good to be tackled are mentioned as well. RESUMEN Tras el extraordinario desarrollo de la Web durante los últimos quince años, ingenieros y desarrolladores empujan Internet hacia su siguiente frontera. Una nueva concepción en la computación y la comunicación a través de las redes ha estado floreciendo durante la última década; un mundo donde la mayoría de los ordenadores del futuro serán extremadamente reducidas de tamaño, hasta el punto que parecerán polvo en sus más avanzado prototipos. En esta visión, cada uno de los elementos de nuestro mundo “real” tiene una etiqueta inteligente que porta sus datos relevantes, mapeando de manera efectiva el mundo “real” en uno “virtual”, donde todos los objetos electrónicamente aumentados están presentes, pueden interactuar entre ellos e influenciar con su comportamiento el de los otros, o incluso el comportamiento del usuario final humano. Ésta es la visión del Internet del Futuro, que también toma ideas de varias tendencias nuevas en las ciencias de la computación y las redes de ordenadores, como la computación omnipresente y el Internet de las Cosas. Como ha sucedido antes, materializar un nuevo paradigma que cambia la manera en que las entidades se interrelacionan en este nuevo entorno ha demostrado ser una meta llena de retos en el camino. Ahora mismo la situación es emocionante, con una plétora de nuevos desarrollos, propuestas y modelos brotando todo el rato, a menudo de una manera descoordinada y descentralizada lejos de cualquier estandarización, recordando de alguna manera el estado de cosas de los primeros desarrollos de redes de ordenadores avanzadas, allá por los años 60 y 70. Normalmente, un sistema diseñado con el Internet del futuro como modelo consistirá en uno o varios dispositivos para usuario final sujetos a estos usuarios finales, una red –a menudo, una red de sensores inalámbricos- encargada de recolectar datos para los dispositivos de usuario final, y a veces una estación base enviando los datos para su consiguiente procesado en ordenadores menos limitados en hardware. Al implementar un sistema diseñado con el Internet del futuro como patrón, los problemas, y más específicamente, las limitaciones que deben enfrentarse son numerosas: falta de estándares para plataformas y protocolos, cuellos de botella en el procesado, bajo tiempo de vida de las baterías, etc. Uno de los principales objetivos de este Proyecto Fin de Carrera es presentar un modelo funcional de cómo trabaja un sistema basado en los paradigmas relacionados al Internet del futuro, superando algunas de las dificultades que pueden esperarse y mostrando un modelo de una arquitectura middleware específicamente diseñado para un sistema omnipresente y ubicuo. Este Proyecto Fin de Carrera está dividido en varias partes. Empezando por una introducción a los principales temas y conceptos de este modelo, un estado del arte es ofrecido para proveer un trasfondo tecnológico. Después de eso, se muestra un ejemplo de middleware semántico orientado a servicios; después, se desarrolla un sistema construido por medio de este middleware semántico orientado a servicios, justificando su localización en un escenario particular, describiéndolo y analizando los datos obtenidos de él. Finalmente, las conclusiones extraídas de este sistema y las futuras tareas que sería bueno tratar también son mencionadas.

Ontology-based access to sensor data streams

Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

Unified access to media metadata on the web: Towards interoperability using a core vocabulary.

The goal of the W3C's Media Annotation Working Group (MAWG) is to promote interoperability between multimedia metadata formats on the Web. As experienced by everybody, audiovisual data is omnipresent on today's Web. However, different interaction interfaces and especially diverse metadata formats prevent unified search, access, and navigation. MAWG has addressed this issue by developing an interlingua ontology and an associated API. This article discusses the rationale and core concepts of the ontology and API for media resources. The specifications developed by MAWG enable interoperable contextualized and semantic annotation and search, independent of the source metadata format, and connecting multimedia data to the Linked Data cloud. Some demonstrators of such applications are also presented in this article.

On a meaningful integration of web services in data-intensive biomedical environments: The DICODE approach

This paper reports on an innovative approach that aims to reduce information management costs in data-intensive and cognitively-complex biomedical environments. Recognizing the importance of prominent high-performance computing paradigms and large data processing technologies as well as collaboration support systems to remedy data-intensive issues, it adopts a hybrid approach by building on the synergy of these technologies. The proposed approach provides innovative Web-based workbenches that integrate and orchestrate a set of interoperable services that reduce the data-intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities.

Short paper: From streaming data to linked data. A case study with bike sharing systems

Current methods and tools that support Linked Data publication have mainly focused so far on static data, without considering the growing amount of streaming data available on the Web. In this paper we describe a case study that involves the publication of static and streaming Linked Data for bike sharing systems and related entities. We describe some of the challenges that we have faced, the solutions that we have explored, the lessons that we have learned, and the opportunities that lie in the future for exploiting Linked Stream Data.

Collaborative semantic editing of linked data lexica.

The creation of language resources is a time-consuming process requiring the efforts of many people. The use of resources collaboratively created by non-linguists can potentially ameliorate this situation. However, such resources often contain more errors compared to resources created by experts. For the particular case of lexica, we analyse the case of Wiktionary, a resource created along wiki principles and argue that through the use of a principled lexicon model, namely lemon, the resulting data could be better understandable to machines. We then present a platform called lemon source that supports the creation of linked lexical data along the lemon model. This tool builds on the concept of a semantic wiki to enable collaborative editing of the resources by many users concurrently. In this paper, we describe the model, the tool and present an evaluation of its usability based on a small group of users.

Multilingual Variation in the context of Linked Data

In this paper we present a revisited classification of term variation in the light of the Linked Data initiative. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web with the idea of transforming it into a global graph. One of the crucial steps of this initiative is the linking step, in which datasets in one or more languages need to be linked or connected with one another. We claim that the linking process would be facilitated if datasets are enriched with lexical and terminological information. Being that the final aim, we propose a classification of lexical, terminological and semantic variants that will become part of a model of linguistic descriptions that is currently being proposed within the framework of the W3C Ontology-Lexica Community Group to enrich ontologies and Linked Data vocabularies. Examples of modeling solutions of the different types of variants are also provided.

Multilingual variation in the context of Linked Data

In this paper we present a revisited classification of term variation in the light of the Linked Data initiative. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web with the idea of transforming it into a global graph. One of the crucial steps of this initiative is the linking step, in which datasets in one or more languages need to be linked or connected with one another. We claim that the linking process would be facilitated if datasets are enriched with lexical and terminological information. Being that the final aim, we propose a classification of lexical, terminological and semantic variants that will become part of a model of linguistic descriptions that is currently being proposed within the framework of the W3C Ontology- Lexica Community Group to enrich ontologies and Linked Data vocabularies. Examples of modeling solutions of the different types of variants are also provided.

A semantic middleware architecture focused on data and heterogeneity management within the Smart Grid

There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests.

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud.

Linked Data for sharing, discovery and re-use of Language Resources at a Web scale

Conferencia por invitación, impartida el 31 d mayo de 2014 en el Workshop on Language Technology Service Platforms: Synergies, Standards, Sharing at LREC2014