The advances of the Linked Open Data LOD initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs e.g., DBpedia connecting many other datasets. They also made possible new Web services for entity detection inside plain text e.g.,i¾?DBpedia Spotlight, thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm LDRANK for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.
We first mention works that generate snippets for native RDF documents. Ge @cite_8 , and Penin @cite_20 focus on the generation of snippets for ontology search. Bai @cite_17 generate snippets for a semantic web search engine.
@cite_17 @cite_20 @cite_8
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Recently, there has been interest in ranking the resources and generating corresponding expressive descriptions from the Semantic Web. This paper proposes an approach for automatically generating snippets from RDF documents and assisting users in better understanding the content of RDF documents returned by Semantic Web search engines. A heuristic method for discovering topics, based on the occurrences of RDF nodes and the URIs of original RDF documents, is presented and experimented in this paper. In order to make the snippets more understandable, two strategies are proposed and used for ranking the topic-related statements and the query-related statements respectively. Finally, the conclusion is drawn based on the discussion about the performances of our topic discovery and the whole snippet generation approaches on a test dataset provided by Sindice. In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms. We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations. We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations.