Interchanging lexical resources on the Semantic Web

Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in ‘‘data silos’’ and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gaps

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.

A Description Framework and Event-Driven Architecture for the Semantic Web and Semantic Grid

In this introductory chapter we put in context and give a brief outline of the work that we thoroughly present in the rest of the dissertation. We consider this work divided in two main parts. The first part is the Firenze Framework, a knowledge level description framework rich enough to express the semantics required for describing both semantic Web services and semantic Grid services. We start by defining what the Semantic Grid is and its relation with the Semantic Web; and the possibility of their convergence since both initiatives have become mainly service-oriented. We also introduce the main motivators of the creation of this framework, one is to provide a valid description framework that works at knowledge level; the other to provide a description framework that takes into account the characteristics of Grid services in order to be able to describe them properly. The other part of the dissertation is devoted to Vega, an event-driven architecture that, by means of proposed knowledge level description framework, is able to achieve high scale provisioning of knowledge-intensive services. In this introductory chapter we portrait the anatomy of a generic event-driven architecture, and we briefly enumerate their main characteristics, which are the reason that make them our choice.

A core ontological model for semantic sensor web infrastructures

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

Enabling query technologies for the semantic sensor web

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose 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. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

Detecting Good Practices and Pitfalls when Publishing Vocabularies on the Web

The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies bringing their semantic to the data being published. These ontologies should be evaluated at different stages, both during their development and their publication. As important as correctly modelling the intended part of the world to be captured in an ontology, is publishing, sharing and facilitating the (re)use of the obtained model. In this paper, 11 evaluation characteristics, with respect to publish, share and facilitate the reuse, are proposed. In particular, 6 good practices and 5 pitfalls are presented, together with their associated detection methods. In addition, a grid-based rating system is generated. Both contributions, the set of evaluation characteristics and the grid system, could be useful for ontologists in order to reuse existing LD vocabularies or to check the one being built.

An approach to ontology for institutional facts in the semantic web

Refinement in software engineering allows a specification to be developed in stages, with design decisions taken at earlier stages constraining the design at later stages. Refinement in complex data models is difficult due to lack of a way of defining constraints, which can be progressively maintained over increasingly detailed refinements. Category theory provides a way of stating wide scale constraints. These constraints lead to a set of design guidelines, which maintain the wide scale constraints under increasing detail. Previous methods of refinement are essentially local, and the proposed method does not interfere very much with these local methods. The result is particularly applicable to semantic web applications, where ontologies provide systems of more or less abstract constraints on systems, which must be implemented and therefore refined by participating systems. With the approach of this paper, the concept of committing to an ontology carries much more force. (c) 2005 Elsevier B.V. All rights reserved.

Semantic caching for web-based spatial applications

Client-side caching of spatial data is an important yet very much under investigated issue. Effective caching of vector spatial data has the potential to greatly improve the performance of spatial applications in the Web and wireless environments. In this paper, we study the problem of semantic spatial caching, focusing on effective organization of spatial data and spatial query trimming to take advantage of cached data. Semantic caching for spatial data is a much more complex problem than semantic caching for aspatial data. Several novel ideas are proposed in this paper for spatial applications. A number of typical spatial application scenarios are used to generate spatial query sequences. An extensive experimental performance study is conducted based on these scenarios using real spatial data. We demonstrate a significant performance improvement using our ideas.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.

AKTive Food:semantic web based knowledge conduits for the organic food industry

We present a vision and a proposal for using Semantic Web technologies in the organic food industry. This is a very knowledge intensive industry at every step from the producer, to the caterer or restauranteur, through to the consumer. There is a crucial need for a concept of environmental audit which would allow the various stake holders to know the full environmental impact of their economic choices. This is a di?erent and parallel form of knowledge to that of price. Semantic Web technologies can be used e?ectively for the calculation and transfer of this type of knowledge (together with other forms of multimedia data) which could contribute considerably to the commercial and educational impact of the organic food industry. We outline how this could be achieved as our essential ob jective is to show how advanced technologies could be used to both reduce ecological impact and increase public awareness.

Natural language processing as a foundation of the semantic Web

The main argument of this paper is that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web (WWW), whether its advocates realise this or not. Chiefly, we argue, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels (in the original SW diagram) based on lower level empirical computations over usage. Our aim is definitely not to claim logic-bad, NLP-good in any simple-minded way, but to argue that the SW will be a fascinating interaction of these two methodologies, again like the WWW (which has been basically a field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite RDF knowledge stores for the SW from existing unstructured text databases in the WWW, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. We also assume that, whatever the limitations on current SW representational power we have drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable.

A semantic web environment for component model

Component-based development (CBD) has become an important emerging topic in the software engineering field. It promises long-sought-after benefits such as increased software reuse, reduced development time to market and, hence, reduced software production cost. Despite the huge potential, the lack of reasoning support and development environment of component modeling and verification may hinder its development. Methods and tools that can support component model analysis are highly appreciated by industry. Such a tool support should be fully automated as well as efficient. At the same time, the reasoning tool should scale up well as it may need to handle hundreds or even thousands of components that a modern software system may have. Furthermore, a distributed environment that can effectively manage and compose components is also desirable. In this paper, we present an approach to the modeling and verification of a newly proposed component model using Semantic Web languages and their reasoning tools. We use the Web Ontology Language and the Semantic Web Rule Language to precisely capture the inter-relationships and constraints among the entities in a component model. Semantic Web reasoning tools are deployed to perform automated analysis support of the component models. Moreover, we also proposed a service-oriented architecture (SOA)-based semantic web environment for CBD. The adoption of Semantic Web services and SOA make our component environment more reusable, scalable, dynamic and adaptive.