A Conceptual Layer on Top of BExIS 2
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1 A Conceptual Layer on Top of BExIS 2 Friederike Klan, Alsayed Algergawy, Erik Fäßler BExIS Developer Conference, Jena, June 9th to 10th 2016
2 AquaDiva model of water flow CSV throughfall and stemflow data CSV meteorological data CSV DNA sequencing data mass spectrometry data
3 AquaDiva Data Portal
4 An Example Search data related to Bechstedter Grund samp_id loc S4 S4 date /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ time ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
5 An Example Search data related to Bechstedter Grund samp_id loc S4 S4 date /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ time ph Fe2+ 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 NO20,01 0,08 0,15 0 0,05 temp_1 0,007 located in Bechstedter Grund temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
6 Another Example Search data referring to alkaline milieu samp_id loc S4 S4 date /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ time ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
7 Another Example Search data referring to alkaline milieu samp_id loc S4 S4 date /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ time ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 alkaline is a Thing which has a ph value > 7 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
8 Basic Idea samp_id loc 23 H43 18 H41 4 H51 7 H43 12 H41 35 H51 5 H51 8 H51 9 H43 17 H41 date time 12/10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /11/2011 ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5 Data Semantic Annotation located in Bechstedter Grund keywords Knowledge Base alkaline is... Search
9 Knowledge Base located in Bechstedter Grund Knowledge Base alkaline is...
10 Ontologies An ontology is a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. [Gruber 1993] can be formalized using description logics TBox = terminological box = data schema example: Lake Thing haspart.water Domain Knowledge ABox = assertion box = data example: lakemichigan : Lake (lakemichigan,milwaukeeriver) : hasinlet samp_id loc 23 H43 18 H41 4 H51 7 H43 12 H41 35 H51 5 H51 8 H51 9 H43 17 H41 date time 12/10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /11/2011 ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ Data NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
11 Querying a Knowledge Base reasoners can infer logical consequences from facts and axioms in the knowledge base they can be used to answer queries over the knowledge base Find all lakes that are fed by a river that has its spring located in the US. (lake). (river,spring). hasinlet(lake,river) hasspring(river,spring) locatedin(spring,'usa') lake river spring
12 Semantic Annotation samp_id H41 H43 loc 23 H43 18 H41 4 H51 7 H43 12 H41 35 H51 5 H51 8 H51 9 H43 17 H41 date time 12/10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /11/2011 Semantic Annotation located in Hainich Knowledge Base alkaline is... ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5 Data
13 Semantic Annotation samp_id loc S4 S4 date /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ time ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5
14 Ontology-Based Data Access Application variable bindings conjunctive query Reasoner loads Knowledge Base TBox ABox transforms samp_id loc 23 H43 18 H41 4 H51 7 H43 12 H41 35 H51 5 H51 8 H51 9 H43 17 H41 date time 12/10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /11/2011 ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5 Data
15 Ontology-Based Data Access Application variable bindings conjunctive query Reasoner loads Knowledge Base TBox Virtual ABox transforms Mapping samp_id loc 23 H43 18 H41 4 H51 7 H43 12 H41 35 H51 5 H51 8 H51 9 H43 17 H41 date time 12/10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /10/ /11/2011 ph 15:00 17:30 11:55 17:25 14:50 11:40 15:20 16:45 15:30 13:30 Fe2+ NO20,01 0,08 0,15 0 0,05 temp_1 0,007 temp_2 r_mm 9,1 9,9 9,8 8,7 8,8 2,4 2,3 8,1 21,2 23,5 Data
16 Example Ontop [Bagosi 2014] :Person rdf:type :birthname :Artist rdf:type MAPPING :Actor rdf:langstring rdf:type :Actress cast_info person_id role_id [[ mappingid Actor target imdb:name/{person_id} rdf:type dbpedia:actor. source select person_id from cast_info where cast_info.role_id = 1 mappingid target source Actress imdb:name/{person_id} rdf:type mo2:actress. select person_id from cast_info where cast_info.role_id = 2
17 Example Ontop [Bagosi 2014] :Person rdf:type :birthname :Artist rdf:type QUERY :Actor rdf:langstring rdf:type :Actress PREFIX : < PREFIX dbpedia: < PREFIX xsd: < SELECT $x WHERE { $x rdf:type :Actress. $x dbpedia:birthname "Pfeiffer, Michelle"^^xsd:string }
18 Applied to BExIS BExI persistence semantic annotation datatuples id 1... annotations xmlvariablevalues <xml data row...> dataset column id id entity IRI entity measurement values (materialized view) msmt instance id id value literal characteristic standard IRI characteristic IRI measurement standard
19 Applied to BExIS ontop mappings <observation instance> rdf:type Observation <observation instance> ofentity <entity class> <observation instance> hasmeasurement <measurement instance> <measurement> rdf:type Measurement <measurement instance> ofcharacteristic <characteristic class> <measurement instance> usesstandard <standard class> mapping <measurement instance> hasvalue <literal> annotations measurement values
20 Questions? Remarks? Suggestions? Ideas?
21 References p.2 CRC AquaDiva p.8-10, [Bagosi 2014] Timea Bagosi, Diego Calvanese, Josef Hardi, Sarah Komla-Ebri, Davide Lanti, Martin Rezk, Mariano Rodriguez-Muro, Mindaugas Slusnys, Guohui Xiao: The Ontop Framework for Ontology Based Data Access. CSWS 2014: [Gruber 1993] T. R. Gruber: A translation approach to portable ontologies. Knowledge Acquisition. 5(2): , 1993
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