7,978 research outputs found
Coping with lists in the ifcOWL ontology
Over the past few years, several suggestions have been made of how to convert an EXPRESS schema into an OWL ontology. The conversion from EXPRESS to OWL is of particular use to architectural design and construction industry, because one of the key data models in architectural design and construction industry, namely the Industry Foundation Classes (IFC) is represented using the EXPRESS information modelling language. In each of these conversion options, the way in which lists are converted (e.g. lists of coordinates, lists of spaces in a floor) is key to the structure and eventual strength of the resulting ontology. In this article, we outline and discuss the main decisions that can be made in converting LIST concepts in EXPRESS to equivalent OWL expressions. This allows one to identify which conversion option is appropriate to support proper and efficient information reuse in the domain of architecture and construction
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
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”
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
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
Semantic processing of EHR data for clinical research
There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015,
preprint versio
Development of Use Cases, Part I
For determining requirements and constructs appropriate for a Web query language, or in fact
any language, use cases are of essence. The W3C has published two sets of use cases for XML
and RDF query languages. In this article, solutions for these use cases are presented using
Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web
data such as XML documents with access to Semantic Web metadata
such as RDF resource
descriptions with reasoning abilities and rules familiar from logicprogramming.
To the
best knowledge of the authors, this is the first in depth study of how to solve use cases for
accessing XML and RDF in a single language: Integrated access to data and metadata
has been
recognized by industry and academia as one of the key challenges in data processing for the
next decade. This article is a contribution towards addressing this challenge by demonstrating
along practical and recognized use cases the usefulness of reasoning abilities, rules, and
semistructured
query languages for accessing both data (XML) and metadata
(RDF)
- …