Using Word Embeddings for Visual Data Exploration with Ontodia and Wikidata

One of the big challenges in Linked Data consumption is to create visual and natural language interfaces to the data usable for non-technical users. Ontodia provides support for diagrammatic data exploration, showcased in this publication in combination with the Wikidata dataset. We present improvements to the natural language interface regarding exploring and querying Linked Data entities. The method uses models of distributional semantics to find and rank entity properties related to user input in Ontodia. Various word embedding types and model settings are evaluated, and the results show that user experience in visual data exploration benefits from the proposed approach

A Comparative Evaluation of Visual and Natural Language Question Answering Over Linked Data

With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches towards this goal are knowledge graph visualization and natural language interfaces. Here, we investigate specifically question answering (QA) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems. Given a QA benchmark (QALD7), we evaluate a visual method which is based on iteratively creating diagrams until the answer is found, against four QA systems that have natural language queries as input. Besides other benefits, the visual approach provides higher performance, but also requires more manual input. The results indicate that the methods can be used complementary, and that such a combination has a large positive impact on QA performance, and also facilitates additional features such as data exploration.Comment: KEOD 201

Wikidata from a Research Perspective -- A Systematic Mapping Study of Wikidata

Wikidata is one of the most edited knowledge bases which contains structured data. It serves as the data source for many projects in the Wikimedia sphere and beyond. Since its inception in October 2012, it has been increasingly growing in term of both its community and its content. This growth is reflected by an expanding number of research focusing on Wikidata. Our study aims to provide a general overview of the research performed on Wikidata through a systematic mapping study in order to identify the current topical coverage of existing research as well as the white spots which need further investigation. In this study, 67 peer-reviewed research from journals and conference proceedings were selected, and classified into meaningful categories. We describe this data set descriptively by showing the publication frequency, the publication venue and the origin of the authors and reveal current research focuses. These especially include aspects concerning data quality, including questions related to language coverage and data integrity. These results indicate a number of future research directions, such as, multilingualism and overcoming language gaps, the impact of plurality on the quality of Wikidata's data, Wikidata's potential in various disciplines, and usability of user interface