This paper describes an extension of the TableMiner+ system, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an e ective and e cient approach. It adds a graphical user interface to TableMiner+, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner+ an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.
Recovering semantics from the growing amount of tabular data on the Web is
a crucial task in realizing the vision of the Semantic Web. Traditional search
engines perform poorly on such data, as they fail to recover the semantics of
tabular structures [
TableMiner+ [
This work implements a graphical user interface speci cally for TableMiner+, to make it an easy-to-use tool for annotating Web tables using Linked Data, and
also extend it by enabling users to visualize and correct the generated annotations and Linked Data triples. As a result, data publishers can use TableMiner+ for transforming tabular data on the Web into high-quality Linked Data, or creating gold-standard for experiment purposes. 2
Recent years have seen an increasing number of work on Semantic Table
Interpretation, such as Venetis et al. [
Description of the TableMiner+ Application Interface
We use the TableMiner+ implementation distributed as part of the STI library
as basis for this work. The STI library provides an implementation of the system
introduced in Zhang [
The interface for starting a Semantic Table Annotation task is illustrated in Figure 3.1 (top)2. Users rstly select a Webpage containing relational tables that are to be annotated. A preview of the Webpage is shown and the list of tables potentially containing relational data is highlighted. The users then select the tables they wish to annotate. They can also con gure the system to alter settings such as feature weights and knowledge base query constraints. The users may provide an email address to subscribe for automatic alert when the annotation task completes. When the users are satis ed with the con guration and the input, they click the button to start the task, which will create annotations in JSON format. These will be interpreted and displayed using the visualization component described below.
use
The JSON les are then passed onto the visualization component, which consists of two interactive elements: an annotated table and a graph visualization module.
The annotated table is the rst point of interaction with the user, and presents the original table, annotated with the entities, concepts and relations identi ed by TableMiner+. The rst step for the UI is to investigate the header cells of the table - TableMiner+ creates a set of candidate concepts that best describe the header and the data in the column. Each associated concept has a score indicating the system's con dence. This set of candidate concepts is presented as a dropdown with the scores (Figure 3.1 section B). Users can select any of the concepts to indicate a more appropriate annotation by clicking on the respective concept. Table cell annotations can be visualized in the same way. As can be seen from the gure, some entities have already been recognized, while some haven't. In case the user can provide a URI that is appropriate for any missing annotation, this can be done by double clicking the relevant cell, which will provide a prompt for a text input. Further SPARQL queries can be triggered to the respective endpoints (based on the user customisations) that can identify any missing annotations.
The next aspect of the UI is the graph visualization, which is invoked from the `inspect' button on the rst cell of each table row. As an example, the header and it's relevant candidate concepts have been plotted as a graph in Figure 3.1. Header cells are shown as nodes labelled with the header columns (0-3), while the candidate classes are shown as nodes, linked with header elements. The most relevant class is shown with a strong link, while the others are presented as dashed lines. Clicking the dashed ones annotate the relevant header cell with the respective concept, which will then con rm the change with a strong link (here, a straight thick line). Header cells are also linked with each other with dashed lines, which is interpreted as only an indicative relation. However, if TableMiner+ creates any relations between the columns, it is re ected as straight lines as can be seen in Figure 3.1.
The proposed demo will follow the process of identifying a web page, downloading and extracting tables and then the annotation process itself. Finally, the annotations will be visualized with the TableMiner+ UI, enabling users to correct and provide any missing information. 4
This paper introduced a graphical user interface for TableMiner+ to facilitate the semi-automatic creation of high quality Linked Data and annotations on Web tables. Future work will extend the system to support, e.g., di erent knowledge bases, other algorithms, ne-grained task de nition that enable batch processing and zoning on tables (e.g., speci c columns).
Acknowledgement Part of this work is funded by ADD PROJECT HERE. We also thank the ADEQUATe3 project team under the lead of Dr Tomas Knap for contributing valuable design ideas.