Exploiting Linked Data and Knowledge Graphs in Large Organisations

2017 ◽  
Keyword(s):  
Linked Data ◽  
Knowledge Graphs

scholarly journals Sampo-UI: A full stack JavaScript framework for developing semantic portal user interfaces

Semantic Web ◽  
2021 ◽  
pp. 1-16
Author(s):  
Esko Ikkala ◽  
Eero Hyvönen ◽  
Heikki Rantala ◽  
Mikko Koho
Keyword(s):  
User Interfaces ◽  
Linked Data ◽  
State Of The Art ◽  
Software Framework ◽  
End User ◽  
Faceted Search ◽  
Software Developer ◽  
Current State ◽  
Knowledge Graphs ◽  
User Friendly

This paper presents a new software framework, Sampo-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the Sampo-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. Sampo-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web.

Knowledge Graphs and Linked Data for the Built Environment

2021 ◽  
pp. 157-183
Author(s):  
Pieter Pauwels ◽  
Aaron Costin ◽  
Mads Holten Rasmussen
Keyword(s):  
Built Environment ◽  
Linked Data ◽  
Knowledge Graphs

From data to knowledge: the relationships between vocabularies, linked data and knowledge graphs

2020 ◽  
Vol 77 (1) ◽  
pp. 93-105
Author(s):  
Junzhi Jia
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Linked Data ◽  
Design Methodology ◽  
Conceptual Analysis ◽  
Entity Recognition ◽  
Content Type ◽  
Data Layer ◽  
Knowledge Graphs ◽  
Organically Combine

PurposeThe purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions.Design/methodology/approachThis paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions.FindingsVocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data.Originality/valueThis paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.

scholarly journals Sequential linked data: The state of affairs

Semantic Web ◽  
2021 ◽  
pp. 1-36
Author(s):  
Enrico Daga ◽  
Albert Meroño-Peñuela ◽  
Enrico Motta
Keyword(s):  
Semantic Web ◽  
Computer Science ◽  
Data Structures ◽  
Linked Data ◽  
Data Models ◽  
The State ◽  
Sequential Data ◽  
Linked List ◽  
Knowledge Graphs ◽  
State Of Affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

scholarly journals Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations

2020 ◽  
Vol 35 ◽  
Author(s):  
Konstantinos I. Kotis ◽  
George A. Vouros ◽  
Dimitris Spiliotopoulos
Keyword(s):  
Life Cycle ◽  
Critical Review ◽  
Linked Data ◽  
Review Paper ◽  
Ontology Engineering ◽  
Status And Trends ◽  
Critical Issues ◽  
The Status ◽  
Knowledge Graphs ◽  
The Impact

Abstract The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘shared, commonly agreed and continuously evolved “live” conceptualizations’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

scholarly journals Approaches to measure class importance in Knowledge Graphs

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252862
Author(s):  
Daniel Fernández-Álvarez ◽  
Johannes Frey ◽  
Jose Emilio Labra Gayo ◽  
Daniel Gayo-Avello ◽  
Sebastian Hellmann
Keyword(s):  
Linked Data ◽  
State Of The Art ◽  
Data Summarization ◽  
The Core ◽  
Novel Approach ◽  
Core Elements ◽  
Importance Ranking ◽  
Knowledge Graphs ◽  
Measure Class ◽  
Excellent Tool

The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

Internationalization of Linked Data: The Case of the Greek DBpedia Edition

2012 ◽  
Author(s):  
Dimitris Kontokostas ◽  
Charalampos Bratsas ◽  
SSren Auer ◽  
Sebastian Hellmann ◽  
Ioannis Antoniou
Keyword(s):  
Linked Data
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