Inma Hernández

Generating SPARQL executable mappings to integrate ontologies

Data translation is an integration task that aims at populating a target model with data of a source model by means of mappings. Generating them automatically is appealing insofar it may reduce integration costs. Matching techniques automatically generate uninterpreted mappings, a.k.a. correspondences, that must be interpreted to perform the data translation task. Other techniques automatically generate executable mappings, which encode an interpretation of these correspondences in a given query language. Unfortunately, current techniques to automatically generate executable mappings are based on instance examples of the target model, which usually contains no data, or based on nested relational models, which cannot be straightforwardly applied to semantic-web ontologies. In this paper, we present a technique to automatically generate SPARQL executable mappings between OWL ontologies. The original contributions of our technique are as follows: 1) it is not based on instance examples but on restrictions and correspondences, 2) we have devised an algorithm to make restrictions and correspondences explicit over a number of language-independent executable mappings, and 3) we have devised an algorithm to transform language-independent into SPARQL executable mappings. Finally, we evaluate our technique over ten scenarios and check that the interpretation of correspondences that it assumes is coherent with the expected results.

CALA: An unsupervised URL-based web page classification system

Unsupervised web page classification refers to the problem of clustering the pages in a web site so that each cluster includes a set of web pages that can be classified using a unique class. The existing proposals to perform web page classification do not fulfill a number of requirements that would make them suitable for enterprise web information integration, namely: to be based on a lightweight crawling, so as to avoid interfering with the normal operation of the web site, to be unsupervised, which avoids the need for a training set of pre-classified pages, or to use features from outside the page to be classified, which avoids having to download it. In this article, we propose CALA, a new automated proposal to generate URL-based web page classifiers. Our proposal builds a number of URL patterns that represent the different classes of pages in a web site, so further pages can be classified by matching their URLs to the patterns. Its salient features are that it fulfills all of the previous requirements, and it has been validated by a number of experiments using real-world, top-visited web sites. Our validation proves that CALA is very effective and efficient in practice.

A statistical approach to URL-based web page clustering

Most web page classifiers use features from the page content, which means that it has to be downloaded to be classified. We propose a technique to cluster web pages by means of their URL exclusively. In contrast to other proposals, we analyze features that are outside the page, hence, we do not need to download a page to classify it. Also, it is non-supervised, requiring little intervention from the user. Furthermore, we do not need to crawl extensively a site to build a classifier for that site, but only a small subset of pages. We have performed an experiment over 21 highly visited websites to evaluate the performance of our classifier, obtaining good precision and recall results.

Benchmarking Data Exchange among Semantic-Web Ontologies

The increasing popularity of the Web of Data is motivating the need to integrate semantic-web ontologies. Data exchange is one integration approach that aims to populate a target ontology using data that come from one or more source ontologies. Currently, there exist a variety of systems that are suitable to perform data exchange among these ontologies; unfortunately, they have uneven performance, which makes it appealing assessing and ranking them from an empirical point of view. In the bibliography, there exist a number of benchmarks, but they cannot be applied to this context because they are not suitable for testing semantic-web ontologies or they do not focus on data exchange problems. In this paper, we present MostoBM, a benchmark for testing data exchange systems in the context of such ontologies. It provides a catalogue of three real-world and seven synthetic data exchange patterns, which can be instantiated into a variety of scenarios using some parameters. These scenarios help to analyze how the performance of data exchange systems evolves as the exchanging ontologies are scaled in structured and/or data. Finally, we provide an evaluation methodology to compare data exchange systems side by side and to make informed and statistically sound decisions regarding: 1) which data exchange system performs better; and 2) how the performance of a system is influenced by the parameters of our benchmark.

Exchanging Data amongst Linked Data applications

The goal of data exchange is to populate the data model of a target application using data that come from one or more source applications. It is common to address data exchange building on correspondences that are transformed into executable mappings. The problem that we address in this article is how to generate executable mappings in the context of Linked Data applications, that is, applications whose data models are semantic-web ontologies. In the literature, there are many proposals to generate executable mappings. Most of them focus on relational or nested-relational data models, which cannot be applied to our context; unfortunately, the few proposals that focus on ontologies have important drawbacks, namely: they solely work on a subset of taxonomies, they require the target data model to be pre-populated or they interpret correspondences in isolation, not to mention the proposals that actually require the user to handcraft the executable mappings. In this article, we present MostoDE, a new automated proposal to generate SPARQL executable mappings in the context of Linked Data applications. Its salient features are that it does not have any of the previous drawbacks, it is computationally tractable and it has been validated using a series of experiments that prove that it is very efficient and effective in practice.

On benchmarking data translation systems for semantic-web ontologies

Data translation, also known as data exchange, is an integration task that aims at populating a target model using data from a source model. This task is gaining importance in the context of semantic-web ontologies due to the increasing interest in graph databases and semantic-web agents. Currently, there are a variety of semantic-web technologies that can be used to implement data translation systems. This makes it difficult to assess them from an empirical point of view. In this paper, we present a benchmark that provides a catalogue of seven data translation patterns that can be instantiated by means of seven parameters. This allows us to create a variety of synthetic, domain-independent scenarios one can use to test existing data translation systems. We also illustrate how to analyse three such systems using our benchmark. The main benefit of our benchmark is that it allows to compare data translation systems side by side within a homogeneous framework.

Integrating Deep-Web Information Sources

Deep-web information sources are difficult to integrate into automated business processes if they only provide a search form. A wrapping agent is a piece of software that allows a developer to query such information sources without worrying about the details of interacting with such forms. Our goal is to help software engineers construct wrapping agents that interpret queries written in high-level structured languages.We think that this shall definitely help reduce integration costs because this shall relieve developers from the burden of transforming their queries into low-level interactions in an ad-hoc manner. In this paper, we report on our reference framework, delve into the related work, and highlight current research challenges. This is intended to help guide future research efforts in this area.

MostoDE: A tool to exchange data amongst semantic-web ontologies

A semantic-web ontology, simply known as ontology, comprises a data model and data that should comply with it. Due to their distributed nature, there exist a large amount of heterogeneous ontologies, and a strong need for exchanging data amongst them, i.e., populating a target ontology using data that come from one or more source ontologies. Data exchange may be implemented using correspondences that are later transformed into executable mappings; however, exchanging data amongst ontologies is not a trivial task, so tools that help software engineers to exchange data amongst ontologies are a must. In the literature, there are a number of tools to automatically generate executable mappings; unfortunately, they have some drawbacks, namely: (1) they were designed to work with nested-relational data models, which prevents them to be applied to ontologies; (2) they require their users to handcraft and maintain their executable mappings, which is not appealing; or (3) they do not attempt to identify groups of correspondences, which may easily lead to incoherent target data. In this article, we present MostoDE, a tool that assists software engineers in generating SPARQL executable mappings and exchanging data amongst ontologies. The salient features of our tool are as follows: it allows to automate the generation of executable mappings using correspondences and constraints; it integrates several systems that implement semantic-web technologies to exchange data; and it provides visual aids for helping software engineers to exchange data amongst ontologies.

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.

Mosto: generating SPARQL executable mappings between ontologies

Data translation is an integration task that aims at populating a target model with data of a source model, which is usually performed by means of mappings. To reduce costs, there are some techniques to automatically generate executable mappings in a given query language, which are executed using a query engine to perform the data translation task. Unfortunately, current approaches to automatically generate executable mappings are based on nested relational models, which cannot be straightforwardly applied to semantic-web ontologies due to some differences between both models. In this paper, we present Mosto, a tool to perform the data translation using automatically generated SPARQL executable mappings. In this demo, ER attendees will have an opportunity to test this automatic generation when performing the data translation task between two different versions of the DBpedia ontology.