211 research outputs found
WADaR: Joint wrapper and data repair
No full textWeb scraping (or wrapping) is a popular means for acquiring data from the web. Recent advancements have made scal- able wrapper-generation possible and enabled data acquisi- tion processes involving thousands of sources. This makes wrapper analysis and maintenance both needed and chal- lenging as no scalable tools exists that support these tasks. We demonstrate WADaR, a scalable and highly auto- mated tool for joint wrapper and data repair. WADaR uses off-the-shelf entity recognisers to locate target entities in wrapper-generated data. Markov chains are used to deter- mine structural repairs, that are then encoded into suitable repairs for both the data and corresponding wrappers. We show that WADaR is able to increase the quality of wrapper-generated relations between 15% and 60%, and to fully repair the corresponding wrapper without any knowl- edge of the original website in more than 50% of the cases. © 2015 VLDB Endowment 2150-8097/15/08
Implementation of Web Query Languages Reconsidered
No full textVisions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised.
This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts:
First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail.
Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages.
Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data.
CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval.
CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs
Datalog+/-: a family of languages for ontology querying
No full textIn ontology-based data access, an extensional database is enhanced by an ontology that generates new intensional knowledge which has to be considered when answering queries. In this setting, tractable data complexity (i.e., complexity w.r.t. the data only) of query answering is crucial, given the need to deal with large data sets. This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying. Plain Datalog is extended by allowing existential quantifiers, the equality predicate, and the truth constant false to appear in rule heads. At the same time, the resulting language is syntactically restricted, so as to achieve decidability and even tractability
AMaχoS—Abstract Machine for Xcerpt
No full textWeb query languages promise convenient and efficient access
to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web
query language with strong emphasis on novel high-level constructs for
effective and convenient query authoring, particularly tailored to versatile
access to data in different Web formats such as XML or RDF.
However, so far it lacks an efficient implementation to supplement the
convenient language features. AMaχoS is an abstract machine implementation
for Xcerpt that aims at efficiency and ease of deployment. It
strictly separates compilation and execution of queries: Queries are compiled
once to abstract machine code that consists in (1) a code segment
with instructions for evaluating each rule and (2) a hint segment that
provides the abstract machine with optimization hints derived by the
query compilation. This article summarizes the motivation and principles
behind AMaχoS and discusses how its current architecture realizes
these principles
Survey over Existing Query and Transformation Languages
No full textRapporto Scientifico del progetto europeo REWERSE
http://rewerse.ne
Red: Redundancy-driven data extraction from result pages
No full textData-driven websites are mostly accessed through search interfaces. Such sites follow a common publishing pattern that, surprisingly, has not been fully exploited for unsupervised data extraction yet: the result of a search is presented as a paginated list of result records. Each result record contains the main attributes about one single object, and links to a page dedicated to the details of that object. We present red, an automatic approach and a prototype system for extracting data records from sites following this publishing pattern. red leverages the inherent redundancy between result records and corresponding detail pages to design an effective, yet fully-unsupervised and domain-independent method. It is able to extract from result pages all the attributes of the objects that appear both in the result records and in the corresponding detail pages. With respect to previous unsupervised methods, our method does not require any a priori domain-dependent knowledge (e.g, an ontology), can achieve a significantly higher accuracy while automatically selecting only object attributes, a task which is out of the scope of traditional fully unsupervised approaches. With respect to previous supervised or semi-supervised methods, red can reach similar accuracy in many domains (e.g., job postings) without requiring supervision for each domain, let alone each website
Data Model and Query Constructs for Versatile Web Query Languages
No full textAs the Semantic Web is gaining momentum, the need for
truly versatile query languages becomes increasingly apparent. A Web
query language is called versatile if it can access in the same query program
data in different formats (e.g. XML and RDF). Most query languages
are not versatile: they have not been specifically designed to cope
with both worlds, providing a uniform language and common constructs
to query and transform data in various formats. Moreover, most of them
do not provide a flexible data model that is powerful enough to naturally
convey both Semantic Web data formats (especially RDF and
Topic Maps) and XML. This article highlights challenges related to the
data model and language constructs for querying both standard Web
and Semantic Web data with an emphasis on facilitating sophisticated
reasoning. It is shown that Xcerpt’s data model and querying constructs
are particularly well-suited for the Semantic Web, but that some adjustments
of the Xcerpt syntax allow for even more effective and natural
querying of RDF and Topic Maps
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