Mashup Pubs publication categorizer

We present a novel approach to retrieve metadata to scholarly papers stored locally as PDF files. A fingerprint is produced from the PDF fulltext to query an online metadata repository. The returned results are matched back to identify the correct metadata entry. These metadata can then be stored in the PDF itself, indexed for a desktop search engine, and collected in a user‟s or community‟s bibliography. We think this hitherto missing link but with our tool now available data eases the organization of scholarly papers, and increases accessibility to one‟s collected academic content.

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human- and machine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites.

In this paper we present Triplify ­ a simplistic but effective approach to publish Linked Data from relational databases. Triplify is based on mapping HTTP-URI requests onto relational database queries. Triplify transforms the resulting relations into RDF statements and publishes the data on the Web in various RDF serializations, in particular as Linked Data.

Semantic web content management poses much manual work onto the community. To reduce this labour we have devised Caravela1, a generic approach to dynamic content integration and automatic categorization. Content and documents of different types can be integrated from diverse semi-structured sources and categorized along multiple dimensions. Automatic linking provides dynamic categorizations at no user cost. We illustrate our approach by an online bibliography categorizing scientific research publications.

Mashups are a new type of interactive web applications, combining content from multiple services or sources at runtime. While many such mashups are being developed most of them support rather simple data integration tasks. We therefore propose a framework for the development of more complex dynamic data integration mashups. The framework consists of components for query generation and online matching as well as for additional data transformation.