Populating the Semantic Web with Historical Text
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1 Populating the Semantic Web with Historical Text Kate Byrne, ICCS Supervisors: Prof Ewan Klein, Dr Claire Grover 9th December
2 Outline Overview of My Research populating the Semantic Web the Tether System Relation Extraction finding binary relations between NE pairs results Mapping to RDF grounding relations in the wider graph dealing with generic nodes 2
3 Populating the Semantic Web Semantic Web born in 1994 takeup slow Much of content is newly generated To add existing archives: need to expose as RDF Conversion to RDF not straightforward 3
4 Data from RCAHMS The Royal Commission on the Ancient and Historical Monuments of Scotland Founded in February One of Scotland s 6 National Collections The memory keeper for Scotland Mission survey the built environment maintain a record of buildings and archaeological sites promote understanding of the material Example: Informatics Forum, Aug
5 sfsjksjwjvssjkljljs sd lajoen s jjs kjdlk lksjlkj sks oihhg sk jjlkjlj jljbjl skj ekw Overview of Tether Relational database Published domain thesauri Text documents txt2rdf pipeline Graph of triples 5
6 sfsjksjwjvssjkljljs sd lajoen s jjs kjdlk lksjlkj sks oihhg sk jjlkjlj jljbjl skj ekw Overview of Tether Relational database Published domain thesauri Text documents txt2rdf pipeline Graph of triples 5
7 sfsjksjwjvssjkljljs sd lajoen s jjs kjdlk lksjlkj sks oihhg sk jjlkjlj jljbjl skj ekw NLP Work txt2rdf Text documents Pre processing Named Entity Recognition tokenise sentence and para split POS tag multi word tokens and features list of NEs and classes trained NER model generate triples remove unwanted relations attach siteids trained RE model set of NE pairs and features list of relations and classes Graph of triples RDF translation Relation Extraction 6
8 sfsjksjwjvssjkljljs sd lajoen s jjs kjdlk lksjlkj sks oihhg sk jjlkjlj jljbjl skj ekw NLP Work txt2rdf Text documents Pre processing Named Entity Recognition tokenise sentence and para split POS tag multi word tokens and features list of NEs and classes trained NER model generate triples remove unwanted relations attach siteids trained RE model set of NE pairs and features list of relations and classes Graph of triples RDF translation Relation Extraction 6
9 A Note on Evaluation Standard NLP metrics used Precision, Recall, F-score: P = TP TP + FP R = TP TP + FN F = But... precision actually preferred over recall End goal is Information Retrieval for non-experts no information is better than false information 2PR P + R 7
10 Overview of My Research populating the Semantic Web the Tether System Relation Extraction finding binary relations between NE pairs results Mapping to RDF grounding relations in the wider graph dealing with generic nodes 8
11 Finding Binary Relations in Text NER as first step Special attention paid to NE nesting Then look for relations between pairs of NEs: generate all possible pairings per document add features Sequential tagger labels each pairing 8
12 Named Entity Recognition 11 categories: ORG, PERSNAME, ROLE, SITETYPE, ARTEFACT, PLACE, SITENAME, ADDRESS, PERIOD, DATE, EVENT Unorthodox ones: EVENT - verb phrases not noun phrases: visited, was found SITETYPE, ARTEFACT, ROLE, EVENT class terms Nesting: [[[Edinburgh] PLACE University] ORG Library] ORG 9
13 Named Entity Recognition 11 categories: ORG, PERSNAME, ROLE, SITETYPE, ARTEFACT, PLACE, SITENAME, ADDRESS, PERIOD, DATE, EVENT Unorthodox ones: EVENT - verb phrases not noun phrases: visited, was found SITETYPE, ARTEFACT, ROLE, EVENT class terms Nesting: [[[Edinburgh] PLACE University] ORG Library] ORG 9
14 Named Entity Recognition 11 categories: ORG, PERSNAME, ROLE, SITETYPE, ARTEFACT, PLACE, SITENAME, ADDRESS, PERIOD, DATE, EVENT Unorthodox ones: EVENT - verb phrases not noun phrases: visited, was found SITETYPE, ARTEFACT, ROLE, EVENT class terms Nesting: [[[Edinburgh] PLACE University] ORG Library] ORG 9
15 Named Entity Recognition 11 categories: ORG, PERSNAME, ROLE, SITETYPE, ARTEFACT, PLACE, SITENAME, ADDRESS, PERIOD, DATE, EVENT Unorthodox ones: EVENT - verb phrases not noun phrases: visited, was found SITETYPE, ARTEFACT, ROLE, EVENT class terms Nesting: [[[Edinburgh] PLACE University] ORG Library] ORG 9
16 Relation Extraction Basic predicate categories: eventrel, haslocation, hasperiod, instanceof, partof, sameas, seealso n-ary eventrel predicate gets split up 11 binary predicates: eventagent, eventagentrole, eventdate, eventpatient, eventplace, haslocation, hasperiod, instanceof, partof, sameas, seealso 10
17 RCAHMS Text with Relations Marked 11
18 Extracted Relations Examples of relations: The Loft haslocation Longhope site hasevent recording recorded haslocation ND recorded haspatient Sub-rectangular cairn RDF subject property object triples 12
19 Results NER Step Precision Recall F-score ADDRESS ARTEFACT DATE EVENT ORG PERIOD PERSNAME PLACE ROLE SITENAME SITETYPE Average
20 Results RE Step Relation Precision Recall F-score Found eventagent ,794 eventagentrole eventdate ,189 eventpatient ,553 eventplace haslocation ,085 hasperiod instanceof partof sameas ,934 seealso Average ,932 14
21 RE Results for Event Relations EVENT category noted as unorthodox but......results are good Additional use for event extraction task: populating RCAHMS relational database fields 15
22 Evaluating the Complete txt2rdf Pipeline 1. Use NE model to tag test set 2. Run RE model over found NE pairs 3. Evaluate against the gold standard new relation pairs are FPs every gold relation missed counts as FN big variation across corpus: measure performance range 16
23 Results for Full Pipeline Hardest data Easiest data P R F P R F eventagent eventagentrole eventdate eventpatient eventplace haslocation hasperiod instanceof partof sameas seealso Average F-score range: 41.96% 73.06%. Average: 57.51% Average precision: 73.35% 17
24 Overview of My Research populating the Semantic Web the Tether System Relation Extraction finding binary relations between NE pairs results Mapping to RDF grounding relations in the wider graph dealing with generic nodes 18
25 Mapping Text Relations to RDF Bea Mill dates from the 19th century Bea Mill hasperiod 19th century :Loc/Sitename#bea+mill :hasperiod : < 18
26 Grounding 1: Linking Text Relations to RCAHMS Sites Bea Mill dates from the 19th century [docid=3402] :Siteid#site3402 :haslocation rdf:type :Loc/Sitename# :Loc/Sitename#bea+mill rdfs:label "Bea Mill" :hasperiod rdf:type :Time/Period# :Time/Period#19th+century rdfs:label "19th century" 19
27 Grounding 2: Connecting to Domain Thesauri "A Neolithic burial monument comprising a stone built chamber within a mound of stones." skos:concept rdf:type skos:scopenote :Classn/Sitetype/monument+%28by+form%29 "CHAMBERED CAIRN" skos:preflabel monthes:topterm :Classn/Sitetype/religious+ritual+and+funerary :Siteid#site123 :hasclassn :Classn/Sitetype#chambered+cairn skos:broader :Classn/Sitetype#burial+cairn :Classn/Sitetype#heel+cairn monthes:prefterm skos:altlabel skos:related :Classn/Sitetype#chambered+tomb :Classn/Sitetype#stalled+cairn :Classn/Sitetype#ring+cairn :Classn/Sitetype#passage+grave "Heel Cairn" "Stalled Cairn" "Passage Grave" :Classn/Sitetype#tomb :Classn/Sitetype#square+cairn 20
28 Grounding 2: Connecting to Domain Thesauri "A Neolithic burial monument comprising a stone built chamber within a mound of stones." skos:concept rdf:type skos:scopenote :Classn/Sitetype/monument+%28by+form%29 "CHAMBERED CAIRN" skos:preflabel monthes:topterm :Classn/Sitetype/religious+ritual+and+funerary :Siteid#site123 :hasclassn :Classn/Sitetype#chambered+cairn skos:broader :Classn/Sitetype#burial+cairn :Classn/Sitetype#heel+cairn monthes:prefterm skos:altlabel skos:related :Classn/Sitetype#chambered+tomb :Classn/Sitetype#stalled+cairn :Classn/Sitetype#ring+cairn :Classn/Sitetype#passage+grave "Heel Cairn" "Stalled Cairn" "Passage Grave" :Classn/Sitetype#tomb :Classn/Sitetype#square+cairn 20
29 Generic vs Specific Nodes Classes SITETYPE, ARTEFACT, ROLE, EVENT Site 123 is a chambered cairn :Siteid#site123 :hasclassn :Classn/Sitetype#chambered+cairn The chambered cairn is in Hoy and Graemsay 21
30 Generic vs Specific Nodes Classes SITETYPE, ARTEFACT, ROLE, EVENT Site 123 is a chambered cairn :Siteid#site123 :hasclassn :Classn/Sitetype#chambered+cairn The chambered cairn is in Hoy and Graemsay :Classn/Sitetype#chambered+cairn67w8 :haslocation :Loc/Place#hoy+and+graemsay 21
31 Mapping Generic Categories Site 101 was visited on 27 April 1969 :Siteid#site101 :Event# :hasevent rdf:type :Event#visited rdfs:subclassof :Event#visited101w117 rdfs:label "Visited" :hasperiod :Time/Date#27+april+1969 rdf:type rdfs:label :Time/Date# "27 April 1969" 22
32 Subclass Labels EVENT subclasses: survey, excavation, find, visit, description, creation, alteration Annotated corpus includes NE subclass labels was+found rdfs:subclassof :Event/Find# visited rdfs:subclassof :Event/Visit# built rdfs:subclassof :Event/Creation# Vocabulary of EVENT subclasses available in graph Extracted text relations can be grounded in EVENT subclasses 23
33 Grounding 3: Placing EVENTs in Subclass Hierarchy :Event/Visit# rdfs:subclassof :Siteid#site101 rdfs:subclassof :Event# :hasevent rdf:type :Event#visited rdfs:subclassof :Event#visited101w117 rdfs:label "Visited" :hasperiod :Time/Date#27+april+1969 rdf:type rdfs:label :Time/Date# "27 April 1969" 24
34 Summary 58% F-score for txt2rdf pipeline (Precision 73%) Extracting structure from text is feasible Class-valued categories: members are class nodes when used generically need unique ids when referring to specific context Using RDF makes integration easy with rest of site data with domain thesauri with further vocabularies in the future 25
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