NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1

D-Fussion: a semantic selective disssemination of information service for the research community in digital libraries

Introduction. In this paper we propose a multi-agent Selective Dissemination of Information service to improve the research community's access to digital library resources. The service also provides a new recommendation approach to satisfy researchers' specific information requirements. Method. The service model is developed by jointly applying Semantic Web technologies (used to define rich descriptions of resources and a concept scheme that helps in indexing and retrieving tasks), fuzzy linguistic modelling techniques (both ordinal and 2-tuple-based approaches, that allow us to flexibly represent and handle information that is subject to a certain degree of uncertainty), as well as content-based and collaborative filtering techniques. Analysis. An experiment has been carried out to test the performance of the proposed model using a prototype and several experts have been asked to assess the recommendations provided by the system. Results. The outcomes of the experiment reveal that the proposed model is feasible and efficient in terms of precision and recall. Conclusions. Semantic Web technologies and fuzzy linguistic modelling provide the means to develop value-added services for digital libraries, which improve users' access to resources of interest to them. Furthermore, the recommendation approach here proposed allows researchers to satisfy specific information needs not covered by traditional recommender systems.Introducción. En este artículo proponemos de un servicio de Diseminación Selectiva de Información multi-agente para mejorar el acceso de la comunidad investigadora a los recursos de bibliotecas digitales. El servicio también proporciona una nueva aproximación a la recomendación para satisfacer los requerimientos de información específicos de los investigadores. Método. El modelo del servicio se desarrolla aplicando conjuntamente las tecnologías de la Web Semántica (usadas para definir descripciones ricas de recursos y un esquema de concepto que ayuden en las tareas de indización y recuperación), las técnicas de modelado lingüístico difuso (tanto la aproximación ordinal y como la basada en 2-tuplas que nos permiten representar y manejar flexiblemente información sujeta a un cierto grado de incertidumbre), así como las técnicas de filtrado basadas en contenido y colaborativas. Análisis. Se realizó un experimento para probar el rendimiento del modelo propuesto usando un prototipo y se han pedido a varios expertos que valoren las recomendaciones proporcionadas por el sistema. Resultados. Los resultados del experimento revelan que el modelo propuesto es factible y eficaz en términos de precisión y relevancia. Conclusiones. Las tecnologías de Web semántica y el modelado lingüístico difuso proporcionan los medios para desarrollar servicios de valor agregado para bibliotecas digitales que mejoran el acceso de los usuarios a los recursos de interés. Además, la aproximación de la recomendación aquí propuesta permite a los investigadores satisfacer necesidades de información específicas no cubiertas por los sistemas de recomendación tradicionales.The research reported here was supported by the Consejería de Innovación, Ciencia y Empresa. Junta de Andalucía, Spain (project SAINFOWEB - 00602) and the Ministerio de Educación y Ciencia, Spain (project FUZZYLING - TIN2007-61079)

Throughput analysis for a high-performance FPGA-accelerated real-time search application

We propose an FPGA design for the relevancy computation part of a high-throughput real-time search application. The application matches terms in a stream of documents against a static profile, held in off-chip memory. We present a mathematical analysis of the throughput of the application and apply it to the problem of scaling the Bloom filter used to discard nonmatches