Towards a Data Hub for Biodiversity with LOD

Wuwongse

et al. 2016

Self Cite

Abstract. Taxonomic knowledge provides a scientific name to each organismal group and is thus indispensable information for understanding biodiversity. However, the various perspectives of classifying organisms and changes in taxonomic knowledge have led to inconsistent classification information among different databases and repositories. To have a precise understanding of taxonomy, one needs to integrate relevant data across taxonomic databases. This is difficult to establish due to the ambiguity in taxon interpretation. Most researchers in earlier stages employed the Linked Open Data (LOD) technique to establish links in taxonomy transition. However, they overlooked the temporal representation of taxa and underlying knowledge of the change in taxonomy, so it is difficult for learners to gain perspective on how some identifiers of taxa are linked. To this end, this research is aimed at developing a model for presenting and preserving the change in taxonomic knowledge in the Resource Description Framework (RDF). Specifically, the proposed model takes advantage of linking Internet resources representing taxa, presenting historical information of taxa, and preserving the background knowledge of the change in taxonomic knowledge in order to enable a better understanding of organisms. We implement a prototype to demonstrate the feasibility and the performance of our approach. The results show that the proposed model is able to handle various practical cases of changes in taxonomic works and provides open and accurate access to linked data for biodiversity.

Section: Taxonomic Knowledge and Linked Datamentioning

confidence: 99%

Section: Entities For Ltkmentioning

confidence: 99%

Section: Entities For Ltkmentioning

confidence: 99%

Section: Working With Other Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Presenting and preserving the change in taxonomic knowledge for linked data

Wuwongse

et al. 2016

Self Cite

Link Prediction in Linked Data of Interspecies Interactions Using Hybrid Recommendation Approach

Hosoya

2015

Semantic Technology

Self Cite

Abstract. Linked Open Data for ACademia (LODAC) together with National Museum of Nature and Science have started collecting linked data of interspecies interaction and making link prediction for future observations. The initial data is very sparse and disconnected, making it very difficult to predict potential missing links using only one prediction model alone. In this paper, we introduce Link Prediction in Interspecies Interaction network (LPII) to solve this problem using hybrid recommendation approach. Our prediction model is a combination of three scoring functions, and takes into account collaborative filtering, community structure, and biological classification. We have found our approach, LPII, to be more accurate than other combinations of scoring functions. Using significance testing, we confirm that these three scoring functions are significant for LPII and they play different roles depending on the conditions of linked data. This shows that LPII can be applied to deal with other realworld situations of link prediction.

RDF Graph Visualization by Interpreting Linked Data as Knowledge

2016

Semantic Technology

Self Cite

Abstract. It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.