Paleontology Knowledge Graph for Data-Driven Discovery

A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.

Paleobiogeographic Knowledge Graph:An Ongoing Work with Fundamental Support for Future Research

Paleobiogeography investigates geographical distributions of fossil organisms and controlling factors that affect their distributions in geological history,to reveal the macro-evolution and coordinated development of life and the environment.It is a crucial window for understanding the biosphere and the geographical environment.After two centuries of development,paleobiogeographic studies have led to the accumulation of significant amounts of knowledge and data;however,the voluminous outputs present the characteristics of an“isolated island”with a scattered,limited number of authoritative definitions of terminologies and semantic heterogeneity among them.This makes data queries cumbersome,the rate of data reuse low,and data sharing more difficult.A knowledge graph(KG)has the advantage of expressing concepts and their semantic relations,which is an important tool for achieving data organization and fusion,and data mining;further,it is also a key technology for realizing the unrestricted sharing of paleobiogeographic information.Through our efforts over the past two years,a paleobiogeographic KG was developed based on the established construction procedure of the KG,which contains 273 concepts,172 properties,and 47 rules.Meanwhile,the completion of this KG and the construction of a paleobiogeographic platform for display and analysis are now being carried out.

A comprehensive construction of the domain ontology for stratigraphy

Stratigraphic knowledge,the cornerstone of geoscience,needs to be represented by the Knowledge Graph based upon ontology,in order to apply the state-of-the-art big-data techniques.This study aims to comprehensively construct the ontologies for the stratigraphic domain.This has been achieved by a federated,crowd intelligence-based collaboration among domain experts of major stratigraphic subdisciplines.The initial step is to enumerate key terms from authoritative references and incorporate them into the Geoscience Professional Knowledge Graphs(GPKGs)of Deep-time Digital Earth Project.During this process,semantic heterogeneities were meticulously addressed by professional judgement aided by an automatic detection of Homonyms at the GPKGs platform.Afterwards,these terms were further differentiated as either classes or properties and arranged in a hierarchical framework in a top-down process.Consequently,seven ontologies are constructed for major stratigraphic branches,i.e.,Lithostratigraphy,Biostratigraphy,Chronostratigraphy,Chemostratigraphy,Magnetostratigraphy,Cyclostratigraphy and Sequence Stratigraphy.The ontology of Biostratigraphy,among them,is elaborated here,as no biostratigraphic ontology has been attempted before to our knowledge.The constructed biostratigraphic ontology comprises following major root classes:Fossil,Biostratigraphic unit,Biostratigraphic horizon.Altogether,they contribute to the eventual dating and correlating of strata in another root class:Biostratigraphic correlation.In summary,the achievements of this study are probably heretofore the most comprehensive ontologies for the stratigraphic domain.Moreover,a proto model of semantic search engine was conceived to discuss potential application of our work for better querying stratigraphic references,utilizing the semantic liaison of the classes in the constructed ontologies.

Two-Dimensional Ground Deformation Monitoring in Shanghai Based on SBAS and MSBAS InSAR Methods

Shanghai has experienced the greatest land subsidence in China in the past sixty years and produced undesirable environmental impact. However, horizontal ground deformation has not been understood yet. Therefore ground deformation monitoring together with the analysis of its driving forces are critical for geo-hazards early-warning, city planning and sustainable urbanization in Shanghai. In this paper, two-dimensional ground deformation monitoring was performed in Shanghai with SBAS and MSBAS InSAR methods. Twenty-nine Multi-Look Fine 6 （MF6） Radarsat-2 SLC data acquired during 2011-2013 were used to derive vertical ground deformation. Meanwhile, six descending Multi-Look Fine 6 （MF6） and four ascending Multi-Look Fine 2 （MF2） spanning April to August, 2008, were used to derive vertical and horizontal ground deformation during the observation period. The results indicate that vertical and horizontal deformations in 2008 were not homogeneously distributed in different districts ranging from 0-2 cm/year. Vertical deformation rate during 2011-2013 were decreased to less than 1 cm/year in most district of Shanghai area. Activities from groundwater exploitation and rapid urbanization are responsible for most of the ground deformation in Shanghai. Thus, future ground deformation in vertical and horizontal directions should be warranted.