Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople

SPARQL enables users to access and browse knowledge graphs in a precise way. However, using SPARQL requires knowledge that many casual users lack. To counter this, specific tools have been created that enable more casual users to browse and query results. This paper evaluates and compares the most prominent techniques, QueryVOWL, SPARKLIS and the Wikidata Query Service (WQS), through a usability evaluation, using a mixed-method evaluation based on usability metrics and heuristics, containing both quantitative and qualitative data. The findings show that while WQS achieved the best results, usability problems were encountered in all tools. Key aspects for usability, extracted from the evaluation, serve as important contributions for future query builders

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help

Developing Ontologies withing Decentralized Settings

This chapter addresses two research questions: “How should a well-engineered methodology facilitate the development of ontologies within communities of practice?” and “What methodology should be used?” If ontologies are to be developed by communities then the ontology development life cycle should be better understood within this context. This chapter presents the Melting Point (MP), a proposed new methodology for developing ontologies within decentralised settings. It describes how MP was developed by taking best practices from other methodologies, provides details on recommended steps and recommended processes, and compares MP with alternatives. The methodology presented here is the product of direct first-hand experience and observation of biological communities of practice in which some of the authors have been involved. The Melting Point is a methodology engineered for decentralised communities of practice for which the designers of technology and the users may be the same group. As such, MP provides a potential foundation for the establishment of standard practices for ontology engineering

Reusing Ontological Background Knowledge in Semantic Wikis.

A number of approaches have been developed for combining wikis with semantic technologies. Many semantic wikis focus on enabling users to specify properties and relationships of individual elements. Complex schema information is typically not edited by the wiki user. Nevertheless, semantic wikis could benefit from taking existing schema information into account, and to allow users to specify additional information based on this schema. In this paper, we introduce an extension of Semantic MediaWiki that incorporates schema information from existing OWL ontologies. Based on the imported ontology, the system offers automatic classification of articles and aims at supporting the user in editing the wiki knowledge base in a logically consistent manner. We present our prototype implementation which uses the KAON2 ontology management system to integrate reasoning services into our wiki

Ontology evolution with Evolva

Ontology evolution is a painstaking and time-consuming process, especially in information rich and dynamic domains. While ontology evolution refers both to the adaptation of ontologies (e.g., through additions or updates possibly discovered from external data sources) and the management of these changes, no existing tools offer both functionalities. The Evolva framework aims to be a blueprint for a comprehensive ontology evolution tool that would cover both tasks. Additionally, Evolva proposes the use of background knowledge sources to reduce user involvement in the ontology adaptation step. This demo focuses on the initial, concrete implementation of our framework

Deriving human-readable labels from SPARQL queries

Over 80% of entities on the Semantic Web lack a human-readable label. This hampers the ability of any tool that uses linked data to offer a meaningful interface to human users. We argue that methods for deriving human-readable labels are essential in order to allow the usage of the Web of Data. In this paper we explore, implement, and evaluate a method for deriving human-readable labels based on the variable names used in a large corpus of SPARQL queries that we built from a set of log files. We analyze the structure of the SPARQL graph patterns and offer a classification scheme for graph patterns. Based on this classification, we identify graph patterns that allow us to derive useful labels. We also provide an overview over the current usage of SPARQL in the newly built corpus

Crowdsourcing tasks within linked data management

Many aspects of Linked Data management – including exposing legacy data and applications to semantic formats, designing vocabularies to describe RDF data, identifying links between entities, query processing, and data curation– are necessarily tackled through the combination of human effort with algorithmic techniques. In the literature on traditional data management the theoretical and technical groundwork to realize and manage such combinations is being established. In this paper we build upon and extend these ideas to propose a framework by which human and computational intelligence can co-exist by augmenting existing Linked Data and Linked Service technology with crowdsourcing functionality. Starting from a motivational scenario we introduce a set of generic tasks which may feasibly be approached using crowdsourcing platforms such as Amazon’s Mechanical Turk, explain how these tasks can be decomposed and translated into MTurk projects, and roadmap the extensions to SPARQL, D2RQ/R2R and Linked Data browsing that are required to achieve this vision

Semantic MediaWiki.

Semantic MediaWiki is an extension of MediaWiki - a widely used wild-engine that also powers Wikipedia. Its aim is to make semantic technologies available to a broad community by smoothly integrating them with the established usage of MediaWiki. The software is already used on a number of productive installations world-wide, but the main target remains to establish "Semantic Wikipedia" as an early adopter of semantic technologies on the web. Thus usability and scalability are as important as powerful semantic features. © Springer-Verlag Berlin Heidelberg 2006