Abstract. Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

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Billions of facts pertaining to a multitude of domains are now availableon the Web as RDF data. However, accessing this data is stilla difficult endeavour for non-expert users. In order to melioratethe access to this data, approaches imposing minimal hurdles totheir users are required. Although many question answering systemsover Linked Data have being proposed, retrieving the desireddata is still significantly challenging. In addition, developing andevaluating question answering systems remains a very complex task.To overcome these obstacles, we present a modular and extensibleopen-source question answering framework. We demonstratehow the framework can be used by integrating two state-of-the-artquestion answering systems. As a result our evaluation shows thatoverall better results can be achieved by the use of combinationrather than individual stand-alone versions

The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. The distributed character of the Web brings manifold collaborative possibilities to interchange data. The commonly adopted collaborative solutions for RDF data are centralized (e. g. SPARQL endpoints and wiki systems). But to support distributed collaboration, a system is needed, that supports divergence of datasets, brings the possibility to conflate diverged states, and allows distributed datasets to be synchronized. In this paper, we present Quit Store, it was inspired by and it builds upon the successful Git system. The approach is based on a formal expression of evolution and consolidation of distributed datasets. During the collaborative curation process, the system automatically versions the RDF dataset and tracks provenance information. It also provides support to branch, merge, and synchronize distributed RDF datasets. The merging process is guarded by specific merge strategies for RDF data. Finally, we use our reference implementation to show overall good performance and demonstrate the practical usability of the system. (Edgard Marx) URL: http://aksw.org/NatanaelArndt (Natanael Arndt), http://aksw.org/NormanRadtke (Norman Radtke), http://aksw.org/MichaelMartin (Michael Martin), http://aksw.org/EdgardMarx (Edgard Marx) 1 http://lod-cloud.net/ 2

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Over the last years, a considerable amount of structured data has been published on the Web as Linked Open Data (LOD). Despite recent advances, consuming and using Linked Open Data within an organization is still a substantial challenge. Many of the LOD datasets are quite large and despite progress in Resource Description Framework (RDF) data management their loading and querying within a triple store is extremely time-consuming and resource-demanding. To overcome this consumption obstacle, we propose a process inspired by the classical Extract-Transform-Load (ETL) paradigm. In this article, we focus particularly on the selection and extraction steps of this process. We devise a fragment of SPARQL Protocol and RDF Query Language (SPARQL) dubbed SliceSPARQL, which enables the selection of well-defined slices of datasets fulfilling typical information needs. SliceSPARQL supports graph patterns for which each connected subgraph pattern involves a maximum of one variable or Internationalized resource identifier (IRI) in its join conditions. This restriction guarantees the efficient processing of the query against a sequential dataset dump stream. Furthermore, we evaluate our slicing approach on three different optimization strategies. Results show that dataset slices can be generated an order of magnitude faster than by using the conventional approach of loading the whole dataset into a triple store.

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