Spreadsheets are widely used by knowledge workers, especially in the industrial sector. Their methodology enables a well understood, easy and fast possibility to enter data. As filling out a spreadsheet is more accessible to common knowledge workers than defining RDF statements, in this paper, we propose an easy-to-use, zero-configuration, web-based spreadsheet editor that simultaneously transfers spreadsheet entries into RDF statements. It enables various kinds of users to easily create semantic data whether they are RDF experts or novices. The typical scenario we address focuses on creating instance data starting with an empty knowledge base that is filled incrementally. In a user study, participants were able to create more statements in shorter time, having similar or even significantly outperforming quality, compared to other approaches.
Another approach, RightField @cite_12 , as its name suggests, focuses on ensuring that only data that is correct with respect to a given ontology may be entered into a spreadsheet.
@cite_12
2126777664 2109718074 2253817241 2133999934
Motivation: In the Life Sciences, guidelines, checklists and ontologies describing what metadata is required for the interpretation and reuse of experimental data are emerging. Data producers, however, may have little experience in the use of such standards and require tools to support this form of data annotation. Results: RightField is an open source application that provides a mechanism for embedding ontology annotation support for Life Science data in Excel spreadsheets. Individual cells, columns or rows can be restricted to particular ranges of allowed classes or instances from chosen ontologies. The RightField-enabled spreadsheet presents selected ontology terms to the users as a simple drop-down list, enabling scientists to consistently annotate their data. The result is ‘semantic annotation by stealth’, with an annotation process that is less error-prone, more efficient, and more consistent with community standards. Availability and implementation: RightField is open source under a BSD license and freely available from http: www.rightfield.org.uk Traditional approaches to Relation Extraction from text require manually defining the relations to be extracted. We propose here an approach to automatically discovering relevant relations, given a large text corpus plus an initial ontology defining hundreds of noun categories (e.g., Athlete, Musician, Instrument). Our approach discovers frequently stated relations between pairs of these categories, using a two step process. For each pair of categories (e.g., Musician and Instrument) it first co-clusters the text contexts that connect known instances of the two categories, generating a candidate relation for each resulting cluster. It then applies a trained classifier to determine which of these candidate relations is semantically valid. Our experiments apply this to a text corpus containing approximately 200 million web pages and an ontology containing 122 categories from the NELL system [, 2010b], producing a set of 781 proposed candidate relations, approximately half of which are semantically valid. We conclude this is a useful approach to semi-automatic extension of the ontology for large-scale information extraction systems such as NELL. While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11. The SWAT Tools ontology verbaliser generates a hierarchically organised hypertext designed for easy comprehension and navigation. The document structure, inspired by encyclopedias and glossaries, is organised at a number of levels. At the top level, a heading is generated for every concept in the ontology; at the next level, each entry is subdivided into logically-based headings like 'Definition' and 'Examples'; at the next, sentences are aggregated when they have parts in common; at the lowest level, phrases are hyperlinked to concept headings. One consequence of this organisation is that some statements are repeated because they are relevant to more than one entry; this means that the text is longer than one in which statements are simply listed. This trade-off between organisation and brevity is investigated in a user study.