Alessio Palmero Aprosio

Automatic Expansion of DBpedia Exploiting Wikipedia Cross-Language Information

DBpedia is a project aiming to represent Wikipedia content in RDF triples. It plays a central role in the Semantic Web, due to the large and growing number of resources linked to it. Nowadays, only 1.7M Wikipedia pages are deeply classified in the DBpedia ontology, although the English Wikipedia contains almost 4M pages, showing a clear problem of coverage. In other languages (like French and Spanish) this coverage is even lower. The objective of this paper is to define a methodology to increase the coverage of DBpedia in different languages. The major problems that we have to solve concern the high number of classes involved in the DBpedia ontology and the lack of coverage for some classes in certain languages. In order to deal with these problems, we first extend the population of the classes for the different languages by connecting the corresponding Wikipedia pages through cross-language links. Then, we train a supervised classifier using this extended set as training data. We evaluated our system using a manually annotated test set, demonstrating that our approach can add more than 1M new entities to DBpedia with high precision (90%) and recall (50%). The resulting resource is available through a SPARQL endpoint and a downloadable package.

These are your rights: A natural language processing approach to automated RDF licenses generation

In the latest years, the Web has seen an increasing interest in legal issues, concerning the use and re-use of online published material. In particular, several open issues affect the terms and conditions under which the data published on the Web is released to the users, and the users rights over such data. Though the number of licensed material on the Web is considerably increasing, the problem of generating machine readable licenses information is still unsolved. In this paper, we propose to adopt Natural Language Processing techniques to extract in an automated way the rights and conditions granted by a license, and we return the license in a machine readable format using RDF and adopting two well known vocabularies to model licenses. Experiments over a set of widely adopted licenses show the feasibility of the proposed approach.

Towards an Automatic Creation of Localized Versions of DBpedia

DBpedia is a large-scale knowledge base that exploits Wikipedia as primary data source. The extraction procedure requires to manually map Wikipedia infoboxes into the DBpedia ontology. Thanks to crowdsourcing, a large number of infoboxes has been mapped in the English DBpedia. Consequently, the same procedure has been applied to other languages to create the localized versions of DBpedia. However, the number of accomplished mappings is still small and limited to most frequent infoboxes. Furthermore, mappings need maintenance due to the constant and quick changes of Wikipedia articles. In this paper, we focus on the problem of automatically mapping infobox attributes to properties into the DBpedia ontology for extending the coverage of the existing localized versions or building from scratch versions for languages not covered in the current version. The evaluation has been performed on the Italian mappings. We compared our results with the current mappings on a random sample re-annotated by the authors. We report results comparable to the ones obtained by a human annotator in term of precision, but our approach leads to a significant improvement in recall and speed. Specifically, we mapped 45,978 Wikipedia infobox attributes to DBpedia properties in 14 different languages for which mappings were not yet available. The resource is made available in an open format.

Automatic Mapping of Wikipedia Templates for Fast Deployment of Localised DBpedia Datasets

DBpedia is a Semantic Web resource that aims at representing Wikipedia in RDF triples. Due to the large and growing number of resources linked to it, DBpedia has become central for the Semantic Web community. The English version currently covers around 1.7M Wikipedia pages. However, the English Wikipedia contains almost 4M pages. This means that there is a substantial problem of coverage (even bigger in other languages). The coverage slowly increases thanks to the manual effort made by various local communities. This effort is aimed at manually mapping Wikipedia templates into DBpedia ontology classes and then run the open-source software provided by the DBpedia community to extract the triples. In this paper, we present an approach to automatically map templates and we release the resulting resource in 25 languages. We describe the used algorithm, starting from the existing mappings on other languages and extending them using the cross-lingual information available in Wikipedia. We evaluate our system on the mappings of a set of languages already included in DBpedia (but not used during the training phase), demonstrating that our approach can replicate the human mappings with high precision and recall, and producing an additional set of mappings not included in the original DBpedia.

Natural language interaction with the web of data by mining its textual side

The Semantic Web is an extension of the classical web. The data and schemas it adds coexist with the documents that were already linked and available. This not only allows interoperability, reusability and potentially unforeseen applications of opened data, but it also creates a unique situation of availability on the web of huge collections of the same pieces of information available at the same time as text and as structured data. An interesting example is the couple Wikipedia-DBpedia: exploiting these interlinked structured and unstructured data sources in parallel can offer a great potential for both Natural Language Processing and Semantic Web applications. Starting from these observations, this paper addresses the problem of enhancing interactions between non-expert users and data available on the Web. In particular, we present QAKiS, a system for open domain Question Answering over linked data, that addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. In the current version, the relational patterns are automatically extracted from Wikipedia, while DBpedia is the data set to be queried using a natural language interface.

A RADAR for information reconciliation in Question Answering systems over Linked Data1

In the latest years, more and more structured data is published on the Web and the need to support typical Web users to access this body of information has become of crucial importance. Question Answering systems over Linked Data try to address this need by allowing users to query Linked Data using natural language. These systems may query at the same time different heterogenous interlinked datasets, that may provide different results for the same query. The obtained results can be related by a wide range of heterogenous relations, e.g., one can be the specification of the other, an acronym of the other, etc. In other cases, such results can contain an inconsistent set of information about the same topic. A well known example of such heterogenous interlinked datasets are language-specific DBpedia chapters, where the same information may be reported in different languages. Given the growing importance of multilingualism in the Semantic Web community, and in Question Answering over Linked Data in particular, we choose to apply information reconciliation to this scenario. In this paper, we address the issue of reconciling information obtained by querying the SPARQL endpoints of language-specific DBpedia chapters. Starting from a categorization of the possible relations among the resulting instances, we provide a framework to: (i) classify such relations, (ii) reconcile information using argumentation theory, (iii) rank the alternative results depending on the confidence of the source in case of inconsistencies, and (iv) explain the reasons underlying the proposed ranking. We release the resource obtained applying our framework to a set of language-specific DBpedia chapters, and we integrate such framework in the Question Answering system QAKiS, that exploits such chapters as RDF datasets to be queried using a natural language interface.