42,154 research outputs found
Discovering Implicational Knowledge in Wikidata
Knowledge graphs have recently become the state-of-the-art tool for
representing the diverse and complex knowledge of the world. Examples include
the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or
Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata.
A distinguishing feature of Wikidata is that the knowledge is collaboratively
edited and curated. While this greatly enhances the scope of Wikidata, it also
makes it impossible for a single individual to grasp complex connections
between properties or understand the global impact of edits in the graph. We
apply Formal Concept Analysis to efficiently identify comprehensible
implications that are implicitly present in the data. Although the complex
structure of data modelling in Wikidata is not amenable to a direct approach,
we overcome this limitation by extracting contextual representations of parts
of Wikidata in a systematic fashion. We demonstrate the practical feasibility
of our approach through several experiments and show that the results may lead
to the discovery of interesting implicational knowledge. Besides providing a
method for obtaining large real-world data sets for FCA, we sketch potential
applications in offering semantic assistance for editing and curating Wikidata
Combination of DROOL rules and Protégé knowledge bases in the ONTO-H annotation tool
ONTO-H is a semi-automatic collaborative tool for the semantic annotation of documents, built as a Protégé 3.0 tab plug-in. Among its multiple functionalities aimed at easing the document annotation process, ONTO-H uses a rule-based system to create cascading annotations out from a single drag and drop operation from a part of a document into an already existing concept or instance of the domain ontology being used for annotation. It also gives support to the detection of name conflicts and instance duplications in the creation of the annotations. The rule system runs on top of the open source rule engine DROOLS and is connected to the domain ontology used for annotation by means of an ad-hoc programmed Java proxy
OntoWeaver S: supporting the design of knowledge portals
This paper presents OntoWeaver-S, an ontology-based infrastructure for building knowledge portals. In particular, OntoWeaver-S is integrated with a comprehensive web service platform, IRS-II, for the publication, discovery, and execution of web services. In this way, OntoWeaver-S supports the access and provision of remote web services for knowledge portals. Moreover, it provides a set of comprehensive site ontologies to model and represent knowledge portals, and thus is able to offer high level support for the design and development process. Finally, OntoWeaver-S provides a set of powerful tools to support knowledge portals at design time as well as at run time
Semantic web-based document: editing and browsing in AktiveDoc
This paper presents a tool for supporting sharing and reuse of knowledge in document creation (writing) and use (reading). Semantic Web technologies are used to support the production of ontology based annotations while the document is written. Free text annotations (comments) can be added to integrate the knowledge in the document. In addition the tool uses external services (e.g. a Semantic Web harvester) to propose relevant content to writing
user, enabling easy knowledge reuse. Similar facilities are provided for readers when their task does not coincide with the author’s one. The tool is specifically designed for Knowledge Management in organisations. In this paper we present and discuss how Semantic Web technologies are designed and integrated in the system
Specification and implementation of mapping rule visualization and editing : MapVOWL and the RMLEditor
Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values. (C) 2018 Elsevier B.V. All rights reserved
Isabelle/PIDE as Platform for Educational Tools
The Isabelle/PIDE platform addresses the question whether proof assistants of
the LCF family are suitable as technological basis for educational tools. The
traditionally strong logical foundations of systems like HOL, Coq, or Isabelle
have so far been counter-balanced by somewhat inaccessible interaction via the
TTY (or minor variations like the well-known Proof General / Emacs interface).
Thus the fundamental question of math education tools with fully-formal
background theories has often been answered negatively due to accidental
weaknesses of existing proof engines.
The idea of "PIDE" (which means "Prover IDE") is to integrate existing
provers like Isabelle into a larger environment, that facilitates access by
end-users and other tools. We use Scala to expose the proof engine in ML to the
JVM world, where many user-interfaces, editor frameworks, and educational tools
already exist. This shall ultimately lead to combined mathematical assistants,
where the logical engine is in the background, without obstructing the view on
applications of formal methods, formalized mathematics, and math education in
particular.Comment: In Proceedings THedu'11, arXiv:1202.453
Identifying Web Tables - Supporting a Neglected Type of Content on the Web
The abundance of the data in the Internet facilitates the improvement of
extraction and processing tools. The trend in the open data publishing
encourages the adoption of structured formats like CSV and RDF. However, there
is still a plethora of unstructured data on the Web which we assume contain
semantics. For this reason, we propose an approach to derive semantics from web
tables which are still the most popular publishing tool on the Web. The paper
also discusses methods and services of unstructured data extraction and
processing as well as machine learning techniques to enhance such a workflow.
The eventual result is a framework to process, publish and visualize linked
open data. The software enables tables extraction from various open data
sources in the HTML format and an automatic export to the RDF format making the
data linked. The paper also gives the evaluation of machine learning techniques
in conjunction with string similarity functions to be applied in a tables
recognition task.Comment: 9 pages, 4 figure
Generating and visualizing a soccer knowledge base
This demo abstract describes the SmartWeb Ontology-based Information Extraction System (SOBIE). A key feature of SOBIE is that all information is extracted and stored with respect to the SmartWeb ontology. In this way, other components of the systems, which use the same ontology, can access this information in a straightforward way. We will show how information extracted by SOBIE is visualized within its original context, thus enhancing the browsing experience of the end user
BIM semantic-enrichment for built heritage representation
In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities
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