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

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

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Storing RDF as a Graph

RDF is the first W3C standard for enriching information resources of the Web with detailed meta data. The semantics of RDF data is defined using a RDF schema. The most expressive language for querying RDF is RQL, which enables querying of semantics. In order to support RQL, a RDF storage system has to map the RDF graph model onto its storage structure. Several storage systems for RDF data have been developed, which store the RDF data as triples in a relational database. To evaluate an RQL query on those triple structures, the graph model has to be rebuilt from the triples. In this paper, we presented a new approach to store RDF data as a graph in a object-oriented database. Our approach avoids the costly rebuilding of the graph and efficiently queries the storage structure directly. The advantages of our approach have been shown by performance test on our prototype implementation OO-Store

Version Control in Online Software Repositories

Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb

Hypermedia Learning Objects System - On the Way to a Semantic Educational Web

While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the present paper we introduce Hylos, an online learning system. Hylos is based on a cellular eLearning Object (ELO) information model encapsulating meta data conforming to the LOM standard. Content management is provisioned on this semantic meta data level and allows for variable, dynamically adaptable access structures. Context aware multifunctional links permit a systematic navigation depending on the learners and didactic needs, thereby exploring the capabilities of the semantic web. Hylos is built upon the more general Multimedia Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE), its linking extension. MIR is an open system supporting the standards XML, Corba and JNDI. Hylos benefits from manageable information structures, sophisticated access logic and high-level authoring tools like the ELO editor responsible for the semi-manual creation of meta data and WYSIWYG like content editing.Comment: 11 pages, 7 figure

Version Control in Online Software Repositories

Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb

Dynamic Generation of Intelligent Multimedia Presentations Through Semantic Inferencing

This paper first proposes a high-level architecture for semi-automatically generating multimedia presentations by combining semantic inferencing with multimedia presentation generation tools. It then describes a system, based on this architecture, which was developed as a service to run over OAI archives - but is applicable to any repositories containing mixed-media resources described using Dublin Core. By applying an iterative sequence of searches across the Dublin Core metadata, published by the OAI data providers, semantic relationships can be inferred between the mixed-media objects which are retrieved. Using predefined mapping rules, these semantic relationships are then mapped to spatial and temporal relationships between the objects. The spatial and temporal relationships are expressed within SMIL files which can be replayed as multimedia presentations. Our underlying hypothesis is that by using automated computer processing of metadata to organize and combine semantically-related objects within multimedia presentations, the system may be able to generate new knowledge by exposing previously unrecognized connections. In addition, the use of multilayered information-rich multimedia to present the results, enables faster and easier information browsing, analysis, interpretation and deduction by the end-user

RDF DATABASES – CASE STUDY AND PERFORMANCE EVALUATION

The Resource Description Framework (RDF) data presentation model and the SPARQL query language have been the core of the semantic web technologies since the early 2000’s. In this article, we evaluate three RDF storage technologies. Our motivation is to find a storage solution that can be used to process “big data” RDF sets. Our method is based on measuring query response times with large samples (hundreds of thousands of RDF documents, millions of RDF statements). We find that all the proposed technologies provide much better performance than querying RDF data stored in files. However, with 300 000 documents, even with the fastest technology, an aggregation query still lasts more than 100 seconds in our environment. As a further performance improvement, we test the same data and queries with MongoDB, demonstrate its performance (10 seconds instead of 100) and scalability (up to 1000 000 documents). However, despite its benefits we must note that because of its data presentation and query limitations, MongoDB probably cannot serve as a generic storage for all kinds of RDF documents

Ontology-based Approximate Query Processing for Searching the Semantic Web with Corese

The semantic web relies on ontologies representing domains through their main concepts and the relations between them. Such a domain knowledge is the keystone to represent the semantic contents of web resources and services in metadata associated to them. These metadata then enable us to search for information based on the semantics of web resources rather than their syntactic forms. However, in the context of the semantic web there are many possibilities of executing queries that would not retrieve any resource. The viewpoints of the designers of ontologies, of the designers of annotations and of the users performing a Web search may not completely match. The user may not completely share or understand the viewpoints of the designers and this mismatch may lead to missed answers. Approximate query processing is then of prime importance for efficiently searching the Semantic Web. In this paper we present the Corese ontology-based search engine we have developped to handle RDF(S) and OWL Lite metadata. We present its theoretical foundation, its query language, and we stress its ability to process approximate queries