The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a worldwide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes regular releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and thus make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

The discovery of all unique (and non-unique) column combinations in a given dataset is at the core of any data profiling effort. The results are useful for a large number of areas of data management, such as anomaly detection, data integration, data modeling, duplicate detection, indexing, and query optimization. However, discovering all unique and non-unique column combinations is an NP-hard problem, which in principle requires to verify an exponential number of column combinations for uniqueness on all data values. Thus, achieving efficiency and scalability in this context is a tremendous challenge by itself.In this paper, we devise Ducc, a scalable and efficient approach to the problem of finding all unique and non-unique column combinations in big datasets. We first model the problem as a graph coloring problem and analyze the pruning effect of individual combinations. We then present our hybrid column-based pruning technique, which traverses the lattice in a depth-first and random walk combination. This strategy allows Ducc to typically depend on the solution set size and hence to prune large swaths of the lattice. Ducc also incorporates row-based pruning to run uniqueness checks in just few milliseconds. To achieve even higher scalability, Ducc runs on several CPU cores (scale-up) and compute nodes (scale-out) with a very low overhead. We exhaustively evaluate Ducc using three datasets (two real and one synthetic) with several millions rows and hundreds of attributes. We compare Ducc with related work: Gordian and HCA. The results show that Ducc is up to more than 2 orders of magnitude faster than Gordian and HCA (631x faster than Gordian and 398x faster than HCA). Finally, a series of scalability experiments shows the efficiency of Ducc to scale up and out.

BackgroundTranslational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.ResultsWe developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action.ConclusionsThis work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine.AvailabilityTMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Before reaping the benefits of open data to add value to an organizations internal data, such new, external datasets must be analyzed and understood already at the basic level of data types, constraints, value patterns etc. Such data profiling, already difficult for large relational data sources, is even more challenging for RDF datasets, the preferred data model for linked open data.We present ProLOD++, a novel tool for various profiling and mining tasks to understand and ultimately improve open RDF data. ProLOD++ comprises various traditional data profiling tasks, adapted to the RDF data model. In addition, it features many specific profiling results for open data, such as schema discovery for user-generated attributes, association rule discovery to uncover synonymous predicates, and uniqueness discovery along ontology hierarchies. ProLOD++ is highly efficient, allowing interactive profiling for users interested in exploring the properties and structure of yet unknown datasets. I. PROFILING LINKED OPEN DATAAt the time of writing, Linked Open Data (LOD) as compiled in http://linkeddata.org comprised already more than 300 data sources including prominent examples, such as DBpedia, YAGO, and Freebase. A LOD dataset is usually represented in the Resource Description Framework (RDF) embodying an entity-relationship-graph or a set of triplified facts consisting of subjects, predicates, and objects. Most of the datasets are openly available and connected amongst each other via sameAs links between representations of same real-world entities. Hundreds more open RDF datasets are listed for instance at http://datahub.io.However, consuming LOD is not easy, because the sources are heterogeneous, often inconsistent, and lack often even basic metadata. One of the main reasons for this problem is that many of the data sources, such as DBpedia [7] or YAGO [13], have been extracted from unstructured data. Furthermore, a knowledge base usually evolves over time when more facts and entities are added and rigid schema and ontology definitions, hand-crafted at some point of time, lose validity over all entities of the dataset. Hence it is vital to thoroughly examine and understand each dataset, its structure, and its properties before usage.Manually inspecting datasets can achieve this goal only to a limited extent: algorithms and tools are needed that profile the dataset to retrieve relevant and interesting meta-data analyzing the entire dataset [14]. Indeed, there are many commercial tools, such as IBM's Information Analyzer, Microsoft's SQL Server Integration Services (SSIS), or Informatica's Data Explorer, and some research prototypes, such as [12], for profiling relational datasets. However all of these tool were designed to profile relational data. LOD which is represented in RDF data has a very different nature and calls for specific profiling and mining techniques. Current tools to work on RDF data are limited to graph visualization and editing: LODlive 1 is a browser-based tool to browse and search in RDF datasets. RDF Pro...