Yes / Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles three technical contributions that significantly advance the state-of-the-art in exploration of semantic data using semantic web techniques and technologies.

Yes / The recent growth of the Web of Data has brought to the fore the need to develop intelligent means to support user exploration through big data graphs. To be effective, approaches for data graph exploration should take into account the utility from a user’s point of view. We have been investigating knowledge utility – how useful the trajectories in a data graph are for expanding users’ knowledge. Following the theory for meaningful learning, according to which new knowledge is developed starting from familiar entities (anchors) and expanding to new and unfamiliar entities, we propose here an approach to identify knowledge anchors in a data graph. Our approach is underpinned by the Cognitive Science notion of basic level objects in domain taxonomies. Several metrics for extracting knowledge anchors in a data graph, and the corresponding algorithms, are presented. The metrics performance is examined, and a hybridization approach that combines the strengths of each metric is proposed.

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses.

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses.

Yes / This paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain.