Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Frankenstein question answering architecture. We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions. The set of questions is offered to the research community as a gold standard dataset for the comparative evaluation of future geospatial question answering engines.

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flexible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing system (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern matching for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.

Knowledge graphs have become popular over the past years and frequently rely on the Resource Description Framework (RDF) or Property Graphs (PG) as underlying data models. However, the query languages for these two data models -SPARQL for RDF and Gremlin for property graph traversal -are lacking interoperability. We present Gremlinator, a novel SPARQL to Gremlin translator. Gremlinator translates SPARQL queries to Gremlin traversals for executing graph pattern matching queries over graph databases. This allows to access and query a wide variety of Graph Data Management Systems (DMS) using the W3C standardized SPARQL query language and avoid the learning curve of a new Graph Query Language. Gremlin is a system agnostic traversal language covering both OLTP graph database or OLAP graph processors, thus making it a desirable choice for supporting interoperability wrt. querying Graph DMSs. We present a comprehensive empirical evaluation of Gremlinator and demonstrate its validity and applicability by executing SPARQL queries on top of the leading graph stores Neo4J, Sparksee and Apache TinkerGraph and compare the performance with the RDF stores Virtuoso, 4Store and JenaTDB. Our evaluation demonstrates the substantial performance gain obtained by the Gremlin counterparts of the SPARQL queries, especially for star-shaped and complex queries.

No abstract