Improving Link Specifications using Context-Aware Information

There is an increasing interest in publishing data using the Linked Open Data philosophy. To link the RDF datasets, a link discovery task is performed to generate owl:sameAs links. There are two ways to perform this task: by means of a classi er or a link speci cation; we focus in the latter approach. Current link speci cation techniques only use the data properties of the instances that they are linking, and they do not take the context information into account. In this paper, we present a proposal that aims to generate context-aware link speci cations to improve the regular link speci cations, increasing the e ectiveness of the results in several real-world scenarios where the context is crucial. Our context-aware link speci cations are independent from similarity functions, transformations or aggregations. We have evaluated our proposal using two real-world scenarios in which we improve precision and recall with respect to regular link speci cations in 23% and 58%, respectively.Ministerio de Economía y Competitividad TIN2013-40848-

AYNEC: All you need for evaluating completion techniques in knowledge graphs

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.Ministerio de Economía y Competitividad TIN2016-75394-

On using high-level structured queries for integrating deep-web information sources

The actual value of the Deep Web comes from integrating the data its applications provide. Such applications offer human-oriented search forms as their entry points, and there exists a number of tools that are used to fill them in and retrieve the resulting pages programmatically. Solution that rely on these tools are usually costly, which motivated a number of researchers to work on virtual integration, also known as metasearch. Virtual integration abstracts away from actual search forms by providing a unified search form, i.e., a programmer fills it in and the virtual integration system translates it into the application search forms. We argue that virtual integration costs might be reduced further if another abstraction level is provided by issuing structured queries in high-level languages such as SQL, XQuery or SPARQL; this helps abstract away from search forms. As far as we know, there is not a proposal in the literature that addresses this problem. In this paper, we propose a reference framework called IntegraWeb to solve the problems of using high-level structured queries to perform deep-web data integration. Furthermore, we provide a comprehensive report on existing proposals from the database integration and the Deep Web research fields, which can be used in combination to address our problem within the previous reference framework.Ministerio de Ciencia y Tecnología TIN2007-64119Junta de Andalucía P07- TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010- 21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-

Towards Discovering Conceptual Models behind Web Sites

Deep Web sites expose data from a database, whose conceptual model remains hidden. Having access to that model is mandatory to perform several tasks, such as integrating different web sites; extracting information from the web unsupervisedly; or creating ontologies. In this paper, we propose a technique to discover the conceptual model behind a web site in the Deep Web, using a statistical approach to discover relationships between entities. Our proposal is unsupervised, not requiring the user to have expert knowledge; and it does not focus on a single view on the database, instead it integrates all views containing entities and relationships that are exposed in the web site.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-09988-

CALA: Classifying Links Automatically based on their URL

Web page classification refers to the problem of automatically assigning a web page to one or moreclasses after analysing its features. Automated web page classifiers have many applications, and many re- searchers have proposed techniques and tools to perform web page classification. Unfortunately, the ex- isting tools have a number of drawbacks that makes them unappealing for real-world scenarios, namely:they require a previous extensive crawling, they are supervised, they need to download a page beforeclassifying it, or they are site-, language-, or domain-dependent. In this article, we propose CALA, a toolfor URL-based web page classification. The strongest features of our tool are that it does not require aprevious extensive crawling to achieve good classification results, it is unsupervised, it is based exclu- sively on URL features, which means that pages can be classified without downloading them, and it issite-, language-, and domain-independent, which makes it generally applicable. We have validated ourtool with 22 real-world web sites from multiple domains and languages, and our conclusion is that CALAis very effective and efficient in practice.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-

An Architecture for Efficient Web Crawling

Virtual Integration systems require a crawling tool able to navigate and reach relevant pages in the Deep Web in an efficient way. Existing proposals in the crawling area fulfill some of these requirements, but most of them need to download pages in order to classify them as relevant or not. We propose a crawler supported by a web page classifier that uses solely a page URL to determine page relevance. Such a crawler is able to choose in each step only the URLs that lead to relevant pages, and therefore reduces the number of unnecessary pages downloaded, minimising bandwidth and making it efficient and suitable for virtual integration systems.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08- TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-

MostoDEx: A tool to exchange RDF data using exchange samples

The Web is evolving into a Web of Data in which RDF data are becoming pervasive, and it is organised into datasets that share a common purpose but have been developed in isolation. This motivates the need to devise complex integration tasks, which are usually performed using schema mappings; generating them automatically is appealing to relieve users from the burden of handcrafting them. Many tools are based on the data models to be integrated: classes, properties, and constraints. Unfortunately, many data models in the Web of Data comprise very few or no constraints at all, so relying on constraints to generate schema mappings is not appealing. Other tools rely on handcrafting the schema mappings, which is not appealing at all. A few other tools rely on exchange samples but require user intervention, or are hybrid and require constraints to be available. In this article, we present MostoDEx, a tool to generate schema mappings between two RDF datasets. It uses a single exchange sample and a set of correspondences, but does not require any constraints to be available or any user intervention. We validated and evaluated MostoDEx using many experiments that prove its effectiveness and efficiency in practice.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08- TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-EMinisterio de Economía y Competitividad TIN2013-40848-