carlos.raymundo@upc.edu.pe / This paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In the past decade, there has been an increasing need for semantic-aware data search and indexing in textual (structured and NoSQL) databases, as full-text search systems became available to non-experts where users have no knowledge about the data being searched and often formulate query keywords which are different from those used by the authors in indexing relevant documents, thus producing noisy and sometimes irrelevant results. In this paper, we address the problem of semantic-aware querying and provide a general framework for modeling and processing semantic-based keyword queries in textual databases, i.e., considering the lexical and semantic similarities/disparities when matching user query and data index terms. To do so, we design and construct a semantic-aware inverted index structure called SemIndex, extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex, in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. To investigate the practicality and effectiveness of SemIndex, we discuss its physical design within a standard commercial RDBMS allowing to create, store, and query its graph structure, thus enabling the system to easily scale up and handle large volumes of data. We have conducted a battery of experiments to test the performance of SemIndex, evaluating its construction time, storage size, query processing time, and result quality, in comparison with legacy inverted index. Results highlight both the effectiveness and scalability of our approach. / This study is partly funded by the National Council for Scientific Research - Lebanon (CNRS-L), by the Lebanese American University (LAU), and the Research Support Foundation of the State of Sao Paulo ( FAPESP ). Appendix SemIndex Weighting Scheme We propose a set of weighting functions to assign weight scores to SemIndex entries, including: index nodes , index edges, data nodes , and data edges . The weighting functions are used to select and rank semantically relevant results w.r.t. the user's query (cf. SemIndex query processing in Section 5). Other weight functions could be later added to cater to the index designer's needs. / Revisión por pares

There has been an increasing interest in Semantic Web services (SWS) as a proposed solution to facilitate automatic discovery, composition and deployment of existing syntactic Web services. Successful implementation and wider adoption of SWS by research and industry are, however, profoundly based on the existence of effective and easy to use methods for service semantic description. Unfortunately, Web service semantic annotation is currently performed by manual means. Manual annotation is a difficult, error-prone and time-consuming task and few approaches exist aiming to semi-automate that task. Existing approaches are difficult to use since they require ontology building. Moreover, these approaches employ ineffective matching methods and suffer from the Low Percentage Problem. The latter problem happens when a small number of service elements - in comparison to the total number of elements – are annotated in a given service. This research addresses the Web services annotation problem by developing a semi-automatic annotation approach that allows SWS developers to effectively and easily annotate their syntactic services. The proposed approach does not require application ontologies to model service semantics. Instead, a standard query template is used: This template is filled with data and semantics extracted from WSDL files in order to produce query instances. The input of the annotation approach is the WSDL file of a candidate service and a set of ontologies. The output is an annotated WSDL file. The proposed approach is composed of five phases: (1) Concept extraction; (2) concept filtering and query filling; (3) query execution; (4) results assessment; and (5) SAWSDL annotation. The query execution engine makes use of name-based and structural matching techniques. The name-based matching is carried out by CN-Match which is a novel matching method and tool that is developed and evaluated in this research. The proposed annotation approach is evaluated using a set of existing Web services and ontologies. Precision (P), Recall (R), F-Measure (F) and Percentage of annotated elements are used as evaluation metrics. The evaluation reveals that the proposed approach is effective since - in relation to manual results - accurate and almost complete annotation results are obtained. In addition, high percentage of annotated elements is achieved using the proposed approach because it makes use of effective ontology extension mechanisms.

Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-17T12:28:32Z No. of bitstreams: 1 arquivototal.pdf: 3788055 bytes, checksum: 45e407b9d1250559dd18ae265ae1410b (MD5) / Made available in DSpace on 2017-08-17T12:28:32Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3788055 bytes, checksum: 45e407b9d1250559dd18ae265ae1410b (MD5) Previous issue date: 2014-02-12 / The emergence of Digital TV brought, beyond high definition, the potential of the interactivity and metadata relative to the TV programmes, but some information provided by the broadcasts can be insufficient to the user make a decision on what he/she is going to watch. This work aims to propose a Semantic Query approach in the Interactive Digital TV environment, based on the concepts and designs of the Semantic Web, providing an advanced service of semantic query, and a way for doing metadata enrichment coming from the broadcasters, adding semantic relationships and expanding the information in a graph of data coming from the Linked Data cloud. A Web Service of Semantic Query integrated into the Knowledge-TV platform was developed to validate this proposal. / O surgimento da TV Digital trouxe, além da alta definição, o potencial da interatividade e metadados relativos à programação da TV. Porém, algumas informações disponibilizadas pelas emissoras podem não ser suficientes para o usuário tomar uma decisão sobre o que vai assistir. Esse trabalho tem como objetivo propor uma abordagem de Consultas Semânticas no ambiente da TV Digital Interativa baseada nos conceitos e padrões da Web Semântica, especificando uma arquitetura de Serviço Web com o objetivo de prôver um meio de enriquecimento dos metadados vindos da emissora, expandindo as informações e adicionando relacionamentos semânticos em um grafo de dados provenientes da nuvem Linked Data. Um Serviço Web de Consulta Semântica integrado à plataforma Knowledge-TV foi desenvolvido para validar essa abordagem.