SWI-Prolog is neither a commercial Prolog system nor a purely academic enterprise, but increasingly a community project. The core system has been shaped to its current form while being used as a tool for building research prototypes, primarily for knowledge- intensive and interactive systems. Community contributions have added several interfaces and the constraint (CLP) libraries. Commercial involvement has created the initial garbage collector, added several interfaces and two development tools: PlDoc (a literate program- ming documentation system) and PlUnit (a unit testing environment). In this article we present SWI-Prolog as an integrating tool, supporting a wide range of ideas developed in the Prolog community and acting as glue between foreign resources. This article itself is the glue between technical articles on SWI-Prolog, providing context and experience in applying them over a longer period

It is widely accepted that proper data publishing is difficult. The majority of Linked Open Data (LOD) does not meet even a core set of data publishing guidelines. Moreover, datasets that are clean at creation, can get stains over time. As a result, the LOD cloud now contains a high level of dirty data that is difficult for humans to clean and for machines to process.Existing solutions for cleaning data (standards, guidelines, tools) are targeted towards human data creators, who can (and do) choose not to use them. This paper presents the LOD Laundromat which removes stains from data without any human intervention. This fully automated approach is able to make very large amounts of LOD more easily available for further processing right now.LOD Laundromat is not a new dataset, but rather a uniform point of entry to a collection of cleaned siblings of existing datasets. It provides researchers and application developers a wealth of data that is guaranteed to conform to a specified set of best practices, thereby greatly improving the chance of data actually being (re)used.

For magazine editors and others, finding suitable photographs for a particular purpose is increasingly problematic. Advances in storage media along with the Web enable us to store and distribute photographic images worldwide. While large databases containing photographic images exist, the tools and methods for searching and selecting an image are limited. Typically, the databases have a semistructured indexing scheme that allows a keyword search but not much more to help the user find the desired photograph.Currently, researchers promote the use of explicit background knowledge as a way out of the search problems encountered on the Internet and in multimedia databases. The semantic Web 1 and emerging standards (such as the resource description framework (RDF) 2 ) make creating a syntactic format specifying background knowledge for information resources possible.In this article, we explore the use of background knowledge contained in ontologies to index and search collections of photographs. We developed an annotation strategy and tool to help formulate annotations and search for specific images. We also compare our approach's performance with two existing Web-based search engine options. The article concludes with observations regarding the standards and tools we used in this annotation study. Our approachCompanies offering photographic images for sale often provide CDs containing samples of the images in reduced jpeg format. Magazine editors and others typically search these CDs to find an illustration for an article. To simulate this process and create our test case, we obtained three CDs with collections of animal photo samples. The CDs contained about 3,000 photos, but we used a subset of approximately 100 photos of apes for our annotation study. Figure 1 shows the general architecture used in our annotation study. We specified all ontologies in RDF Schema (RDFS) 2 using the Protégé-2000 3 ontology editor (version 1.4). This editor supports the construction of ontologies in a frame-like fashion with classes and slots. Protégé can save the ontology definitions in RDFS. The SWI-Prolog RDF parser 4 reads the resulting RDFS file into the annotation tool, which subsequently generates an annotation interface based on the RDFS specification. The tool supports reading in photographs, creating annotations, and storing annotations in an RDF file. A query tool with a similar interface can read RDF files and search for suitable photographs in terms of the ontology.The architecture shown in Figure 1 is in the same spirit as the one Yves Lafon and Bert Bos described. 5 However, we place more emphasis on the nature of the ontologies, the subject matter description, and the explicit link to a domain ontology. Developing ontologiesTo define semantic annotations for ape photographs, we needed at least two groups of definitions:• Structure of a photo annotation. We defined a photo annotation ontology that specifies an annotation's structure independent of the particular subject matter domain (in our case, apes). This ontology ...

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a b s t r a c tIn this article we describe a Semantic Web application for semantic annotation and search in large virtual collections of cultural-heritage objects, indexed with multiple vocabularies. During the annotation phase we harvest, enrich and align collection metadata and vocabularies. The semantic-search facilities support keyword-based queries of the graph (currently 20 M triples), resulting in semantically grouped result clusters, all representing potential semantic matches of the original query. We show two sample search scenario's. The annotation and search software is open source and is already being used by third parties. All software is based on established Web standards, in particular HTML/XML, CSS, RDF/OWL, SPARQL and JavaScript.