Children searching information on the Internet: Performance on children's interfaces compared to Google

Children frequently make use of the Internet to search for information. However, research shows that children experience many problems with searching and browsing the web. The last decade numerous search environments have been developed, especially for children. Do these search interfaces support children in effective information-seeking? And do these interfaces add value to today’s popular search engines, such as Google? In this explorative study, we compared children’s search performance on four interfaces designed for children, with their performance on Google. We found that the children did not perform better on these interfaces than on Google. This study also uncovered several problems that children experienced with these search interfaces, which can be of use for designers of future search interfaces for children

Content-Aware DataGuides for Indexing Large Collections of XML Documents

XML is well-suited for modelling structured data with textual content. However, most indexing approaches perform structure and content matching independently, combining the retrieved path and keyword occurrences in a third step. This paper shows that retrieval in XML documents can be accelerated significantly by processing text and structure simultaneously during all retrieval phases. To this end, the Content-Aware DataGuide (CADG) enhances the wellknown DataGuide with (1) simultaneous keyword and path matching and (2) a precomputed content/structure join. Extensive experiments prove the CADG to be 50-90% faster than the DataGuide for various sorts of query and document, including difficult cases such as poorly structured queries and recursive document paths. A new query classification scheme identifies precise query characteristics with a predominant influence on the performance of the individual indices. The experiments show that the CADG is applicable to many real-world applications, in particular large collections of heterogeneously structured XML documents

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Conceptual Linking: Ontology-based Open Hypermedia

This paper describes the attempts of the COHSE project to define and deploy a Conceptual Open Hypermedia Service. Consisting of • an ontological reasoning service which is used to represent a sophisticated conceptual model of document terms and their relationships; • a Web-based open hypermedia link service that can offer a range of different link-providing facilities in a scalable and non-intrusive fashion; and integrated to form a conceptual hypermedia system to enable documents to be linked via metadata describing their contents and hence to improve the consistency and breadth of linking of WWW documents at retrieval time (as readers browse the documents) and authoring time (as authors create the documents)

Learning Visual Features from Snapshots for Web Search

When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based on the extracted textual elements, link analysis, and user logs. However, Web pages are not solely linked texts, but have structured layout organizing a large variety of elements in different styles. Such layout itself can convey useful visual information, indicating the relevance of a Web page. For example, the query-independent layout (i.e., raw page layout) can help identify the page quality, while the query-dependent layout (i.e., page rendered with matched query words) can further tell rich structural information (e.g., size, position and proximity) of the matching signals. However, such visual information of layout has been seldom utilized in Web search in the past. In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking. Both query-independent and query-dependent snapshots are considered as the new inputs. We then propose a novel visual perception model inspired by human's visual search behaviors on page viewing to extract the visual features. This model can be learned end-to-end together with traditional human-crafted features. We also show that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme. Experiments on benchmark collections demonstrate that learning visual features from Web page snapshots can significantly improve the performance of relevance ranking in ad-hoc Web retrieval tasks.Comment: CIKM 201

GEORDi: Supporting lightweight end-user authoring and exploration of Linked Data

The US and UK governments have recently made much of the data created by their various departments available as data sets (often as csv files) available on the web. Known as ”open data” while these are valuable assets, much of this data remains useless because it is effectively inaccessible for citizens to access for the following reasons: (1) it is often a tedious, many step process for citizens simply to find data relevant to a query. Once the data candidate is located, it often must be downloaded and opened in a separate application simply to see if the data that may satisfy the query is contained in it. (2) It is difficult to join related data sets to create richer integrated information (3) it is particularly difficult to query either a single data set, and even harder to query across related data sets. (4) To date, one has had to be well versed in semantic web protocols like SPARQL, RDF and URI formation to integrate and query such sources as reusable linked data. Our goal has been to develop tools that will let regular, non-programmer web citizens make use of this Web of Data. To this end, we present GEORDi, a set of integrated tools and services that lets citizen users identify, explore, query and represent these open data sources over the web via Linked Data mechanisms. In this paper we describe the GEORDi process of authoring new and translating existing open data in a linkable format, GEORDi’s lens mechanism for rendering rich, plain language descriptions and views of resources, and the GEORDI link-sliding paradigm for data exploration. With these tools we demonstrate that it is possible to make the Web of open (and linked) data accessible for ordinary web citizen users