Keyword Search on RDF Graphs - A Query Graph Assembly Approach

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is $O(k^{2l} \cdot l^{3l})$, where $l$ is the number of the keywords and $k$ is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both $l$ and $k$ are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency

Analysis of multiple update techniques on a RDF keyword search system

Keyword search is a technology that allows non-expert users to explore and retrieve information and it is traditionally used for unstructured data, such as in Web page searches. In the last decade, this search method has also become popular for exploring structured data, such as relational databases or graphs. Instead of using complex SQL or SPARQL queries and when the underlying schema is known, the user writes a series of words(keywords) to search for what he or she needs, getting as answers the ones more matching with the search. Keyword search systems are challenged by two fundamental parameters, efficiency and effectiveness. In fact, efficiency and effectiveness are two qualities of a SPARQL, or SQL, query that returns an answer quickly and always accurate even when operating on large amounts of data. The "virtual documents" method allows keyword search systems to work also on large databases by generating answers to keyword queries in a reasonable time. This paper aims to replicate the keyword search systems based on "virtual documents" TSA+BM25 and TSA+VDP for RDF graphs. In addition, two methods of update processing in a keyword search system, will be presented and analyzed: BruteForce and semiTSA. Although keyword search is a growing research matter, the topic of updates on structured data, such as RDF data, had not yet been addressed in the literature.Keyword search is a technology that allows non-expert users to explore and retrieve information and it is traditionally used for unstructured data, such as in Web page searches. In the last decade, this search method has also become popular for exploring structured data, such as relational databases or graphs. Instead of using complex SQL or SPARQL queries and when the underlying schema is known, the user writes a series of words(keywords) to search for what he or she needs, getting as answers the ones more matching with the search. Keyword search systems are challenged by two fundamental parameters, efficiency and effectiveness. In fact, efficiency and effectiveness are two qualities of a SPARQL, or SQL, query that returns an answer quickly and always accurate even when operating on large amounts of data. The "virtual documents" method allows keyword search systems to work also on large databases by generating answers to keyword queries in a reasonable time. This paper aims to replicate the keyword search systems based on "virtual documents" TSA+BM25 and TSA+VDP for RDF graphs. In addition, two methods of update processing in a keyword search system, will be presented and analyzed: BruteForce and semiTSA. Although keyword search is a growing research matter, the topic of updates on structured data, such as RDF data, had not yet been addressed in the literature

Application of Semantics to Solve Problems in Life Sciences

Fecha de lectura de Tesis: 10 de diciembre de 2018La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnología, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL

Connected Information Management

Society is currently inundated with more information than ever, making efficient management a necessity. Alas, most of current information management suffers from several levels of disconnectedness: Applications partition data into segregated islands, small notes don’t fit into traditional application categories, navigating the data is different for each kind of data; data is either available at a certain computer or only online, but rarely both. Connected information management (CoIM) is an approach to information management that avoids these ways of disconnectedness. The core idea of CoIM is to keep all information in a central repository, with generic means for organization such as tagging. The heterogeneity of data is taken into account by offering specialized editors. The central repository eliminates the islands of application-specific data and is formally grounded by a CoIM model. The foundation for structured data is an RDF repository. The RDF editing meta-model (REMM) enables form-based editing of this data, similar to database applications such as MS access. Further kinds of data are supported by extending RDF, as follows. Wiki text is stored as RDF and can both contain structured text and be combined with structured data. Files are also supported by the CoIM model and are kept externally. Notes can be quickly captured and annotated with meta-data. Generic means for organization and navigation apply to all kinds of data. Ubiquitous availability of data is ensured via two CoIM implementations, the web application HYENA/Web and the desktop application HYENA/Eclipse. All data can be synchronized between these applications. The applications were used to validate the CoIM ideas

Extracting Temporal Expressions from Unstructured Open Resources

AETAS is an end-to-end system with SOA approach that retrieves plain text data from web and blog news and represents and stores them in RDF, with a special focus on their temporal dimension. The system allows users to acquire, browse and query Linked Data obtained from unstructured sources

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE