8 research outputs found
Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data
This paper gives a short overview of specific logical approaches to data extraction, data management, and reasoning about data. In particular, we survey theoretical results and formalisms that have been obtained and used in the context of the Lixto Project at TU Wien, the DIADEM project at the University of Oxford, and the VADA project, which is currently being carried out jointly by the universities of Edinburgh, Manchester, and Oxford. We start with a formal approach to web data extraction rooted in monadic second order logic and monadic Datalog, which gave rise to the Lixto data extraction system. We then present some complexity results for monadic Datalog over trees and for XPath query evaluation. We further argue that for value creation and for ontological reasoning over data, we need existential quantifiers (or Skolem terms) in rule heads, and introduce the Datalog± family. We give an overview of important members of this family and discuss related complexity issues
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Four Lessons in Versatility or How Query Languages Adapt to the Web
Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”
Conjunctive Queries over Trees
We study the complexity and expressive power of conjunctive queries over unranked labeled trees represented using a variety of structure relations such as "child'', "descendant'', and "following'' as well as unary relations for node labels. We establish a framework for characterizing structures representing trees for which conjunctive queries can be evaluated efficiently. Then we completely chart the tractability frontier of the problem and establish a dichotomy theorem for our axis relations, i.e., we find all subset-maximal sets of axes for which query evaluation is in polynomial time and show that for all other cases, query evaluation is NP-complete. All polynomial-time results are obtained immediately using the proof techniques from our framework. Finally, we study the expressiveness of conjunctive queries over trees and show that for each conjunctive query, there is an equivalent acyclic positive query (i.e., a set of acyclic conjunctive queries), but that in general this query is not of polynomial size
Keyword-Based Querying for the Social Semantic Web
Enabling non-experts to publish data on the web is an important
achievement of the social web and one of the primary goals of the social
semantic web. Making the data easily accessible in turn has received only
little attention, which is problematic from the point of view of
incentives: users are likely to be less motivated to participate in the
creation of content if the use of this content is mostly reserved to
experts.
Querying in semantic wikis, for example, is typically realized in terms of
full text search over the textual content and a web query language such as
SPARQL for the annotations. This approach has two shortcomings that limit
the extent to which data can be leveraged by users: combined queries over
content and annotations are not possible, and users either are restricted
to expressing their query intent using simple but vague keyword queries or
have to learn a complex web query language.
The work presented in this dissertation investigates a more suitable form
of querying for semantic wikis that consolidates two seemingly conflicting
characteristics of query languages, ease of use and expressiveness. This
work was carried out in the context of the semantic wiki KiWi, but the
underlying ideas apply more generally to the social semantic and social
web.
We begin by defining a simple modular conceptual model for the KiWi wiki
that enables rich and expressive knowledge representation. A component of
this model are structured tags, an annotation formalism that is simple yet
flexible and expressive, and aims at bridging the gap between atomic tags
and RDF. The viability of the approach is confirmed by a user study, which
finds that structured tags are suitable for quickly annotating evolving
knowledge and are perceived well by the users.
The main contribution of this dissertation is the design and
implementation of KWQL, a query language for semantic wikis. KWQL combines
keyword search and web querying to enable querying that scales with user
experience and information need: basic queries are easy to express; as the
search criteria become more complex, more expertise is needed to formulate
the corresponding query. A novel aspect of KWQL is that it combines both
paradigms in a bottom-up fashion. It treats neither of the two as an
extension to the other, but instead integrates both in one framework. The
language allows for rich combined queries of full text, metadata, document
structure, and informal to formal semantic annotations. KWilt, the KWQL
query engine, provides the full expressive power of first-order queries,
but at the same time can evaluate basic queries at almost the speed of the
underlying search engine. KWQL is accompanied by the visual query language
visKWQL, and an editor that displays both the textual and visual form of
the current query and reflects changes to either representation in the
other. A user study shows that participants quickly learn to construct
KWQL and visKWQL queries, even when given only a short introduction.
KWQL allows users to sift the wealth of structure and annotations in an
information system for relevant data. If relevant data constitutes a
substantial fraction of all data, ranking becomes important. To this end,
we propose PEST, a novel ranking method that propagates relevance among
structurally related or similarly annotated data. Extensive experiments,
including a user study on a real life wiki, show that pest improves the
quality of the ranking over a range of existing ranking approaches