Chem# - Semantically Enriched Linked Open Chemical Data

The problem: Vast quantities of chemical data (e.g. crystal structures, NMR spectra, experimental reports) are generated every day. The majority of this data is never published, and the data that is published is fragmented,trapped in legacy formats and difficult to discover. The solution: Semantically Enriched Linked Open Chemical Data: browsable, searchable, discoverable and interpretable by humans and machines alike, using standardized extensible data formats (Chemical Markup Language) and technologies (HTTP, RDF).Funded by JISC

Dimensional enrichment of statistical linked open data

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.Peer ReviewedPostprint (author's final draft

QB2OLAP : enabling OLAP on statistical linked open data

Publication and sharing of multidimensional (MD) data on the Semantic Web (SW) opens new opportunities for the use of On-Line Analytical Processing (OLAP). The RDF Data Cube (QB) vocabulary, the current standard for statistical data publishing, however, lacks key MD concepts such as dimension hierarchies and aggregate functions. QB4OLAP was proposed to remedy this. However, QB4OLAP requires extensive manual annotation and users must still write queries in SPARQL, the standard query language for RDF, which typical OLAP users are not familiar with. In this demo, we present QB2OLAP, a tool for enabling OLAP on existing QB data. Without requiring any RDF, QB(4OLAP), or SPARQL skills, it allows semi-automatic transformation of a QB data set into a QB4OLAP one via enrichment with QB4OLAP semantics, exploration of the enriched schema, and querying with the high-level OLAP language QL that exploits the QB4OLAP semantics and is automatically translated to SPARQL.Peer ReviewedPostprint (author's final draft

Exposing WikiPathways as Linked Open Data

Biology has become a data intensive science. Discovery of new biological facts increasingly relies on the ability to find and match appropriate biological data. For instance for functional annotation of genes of interest or for identification of pathways affected by over-expressed genes. Functional and pathway information about genes and proteins is typically distributed over a variety of databases and the literature.

Pathways are a convenient, easy to interpret way to describe known biological interactions. WikiPathways provides community curated pathways. WikiPathways users integrate their knowledge with facts from the literature and biological databases. The curated pathway is then reviewed and possibly corrected or enriched. Different tools (e.g. Pathvisio and Cytoscape) support the integration of WikiPathways-knowledge for additional tasks, such as the integration with personal data sets. 

Data from WikiPathways is increasingly also used for advanced analysis where it is integrated or compared with other data, Currently, integration with data from different biological sources is mostly done manually. This can be a very time consuming task because the curator often first needs to find the available resources, needs to learn about their specific content and qualities and often spends a lot of time to technically combine the two. 

Semantic web and Linked Data technologies eliminate the barriers between database silos by relying on a set of standards and best practices for representing and describing data. The architecture of the semantic web relies on the architecture of the web itself for integrating and mapping universal resource identifiers (URI), coupled with basic inference mechanisms to enable matching concepts and properties across data sources. Semantic Web and Linked Data technologies are increasingly being successfully applied as integration engines for linking biological elements. 

Exposing WikiPathways content as Linked Open Data to the Semantic Web, enables rapid, semi-automated integration with a the growing amount of biological resources available from the linked open data cloud, it also allows really fast queries of WikiPathways itself. 

We have harmonised WikiPathways content according to a selected set of vocabularies (Biopax, CHEMBL, etc), common to resources already available as Linked Open Data. 
WikiPathways content is now available as Linked Open Data for dynamic querying through a SPARQL endpoint: http://semantics.bigcat.unimaas.nl:8000/sparql

Linked Open Data az egyetemen

A Linked Data a Szemantikus Web technológiák egy praktikus alkalmazása adatok világméretű összekapcsolására. Az Open Data törekvés Európában is már komoly előrehaladást ért el, és áttekintő tanulmányok szerint a felsőoktatás területén is pozitívan befolyásolhatja az oktatás minőségét. Létrejött a Linked Universities szövetség is, amely az adatok Linked Open Data irányelvek szerinti publikálását segíti és fogja össze a tagintézményekben. Adatok Linked Data formátumú publikálásának kulcsfontosságú mozzanata az adatok felépítésének, sémájának a megtervezése. Jelen cikkben felvázoljuk az adatséma tervezés fő lépéseit, feladatait és a kapcsolódó fogalmak, kifejezések jelentéseit. Beszámolunk egy általunk megvalósított tervezési folyamatról, és ismertetjük az ennek eredményeként létrejött Egyetemi Adatmodellt. Munkánkban áttekintünk számos, egymást átfedő RDF sémát, és ismertetjük, hogy segítségükkel hogyan tudjuk leírni az egyetemi információs infrastruktúrát Linked Data formátumban. Adatmodellünkben négy fő adathalmaz osztályt különböztetünk meg, ezek a szervezetek, személyek, publikációk és tanegységek. Megadjuk a különböző osztályok adatait leíró tulajdonságokat, az osztályok adatai közötti kapcsolatokat és az adatmodell bővítés lehetőségének irányait

Linked open government data: lessons from Data.gov.uk

The movement to publish government data is an opportunity to populate the linked data Web with data of good provenance. The benefits range from transparency to public service improvement, citizen engagement to the creation of social and economic value. There are many challenges to be met before the vision is implemented, and this paper describes the efforts of the EnAKTing project to extract value from data.gov.uk, through the stages of locating data sources, integrating data into the linked data Web, and browsing and querying it

Integration of linked open data in case-based reasoning systems

This paper discusses the opportunities of integrating Linked Open Data (LOD) resources into Case-Based Reasoning (CBR) systems. Upon the application domain travel medicine, we will exemplify how LOD can be used to fill three out of four knowledge containers a CBR system is based on. The paper also presents the applied techniques for the realization and demonstrates the performance gain of knowledge acquisition by the use of LOD