From Ontologies to Information Extraction and Back

Transcription

1 From Ontologies to Information Extraction and Back LAMA SAEEDA D E PA R T M E N T O F C Y B E R N E T I C S A N D A R T I F I C I A L I N T E L L I G E N C E FA C U LT Y O F E L E C T R I C A L E N G I N E E R I N G C Z E C H T E C H N I C A L U N I V E R S I T Y I N P R A G U E 11/10/2017 LAMA SAEEDA - FEL, CVUT 1

2 Content IE and OBIE Motivation Information extraction and Ontology learning Applications Aviation safety Dataset exploration Conclusion 11/10/2017 LAMA SAEEDA - FEL, CVUT 2

3 Information extraction and ontologybased information extraction Flight had a prolonged loss of communication over Swiss territory. Zurich radar informed at about loss of contact and that also no contact on 121,5 MHz could be established. Geneva informed that ABC1234 has contacted them. Length of loss of comm. is approx. 11 minutes. ufo:trope ufo:object ufo:event UFO ontology aso:location aso:call-sign Aviation-Safety ontology A little semantics goes a long way 11/10/2017 LAMA SAEEDA - FEL, CVUT 3

4 Ontology-based information extraction Challenges: Availability of domain ontology Lack of complete knowledge graph that would serve all purposes (DBpedia) Ontology is essential in IE from textual corpus, and enriching the domain ontology will definitely enhance the process! 11/10/2017 LAMA SAEEDA - FEL, CVUT 4

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6 The iterative methodology Ontology learning Textual corpus Domain ontology Semantic annotation More extracted information leads to a richer ontology Richer ontology leads to extracting more information 11/10/2017 LAMA SAEEDA - FEL, CVUT 6

7 Information extraction Text pre processing Extraction Pre-processing, Rule-based / Machine removing Learning-based stop words, POS extraction tagging 11/10/2017 LAMA SAEEDA - FEL, CVUT 7

8 Ontology learning Terms Concepts Relations Axioms 11/10/2017 LAMA SAEEDA - FEL, CVUT 8

9 Ontology learning Terms Concepts Taxonomic relations Non-taxonomic relations Axioms statistic methods Text pre-processing X POS tagging X Sentence parsing X Latent semantic X Cooccurrence X X Clustering X X Term subsumption X Association rules Linguistic methods Seed words X Semantic lexicon X X X Sub-categorization frames X X Syntactic structure X X Dependency analysis X X Semantic templates X X Lexico-syntactic paterns X X Axiom templates X Logical methods Logical inference X X Inductive Logic X 11/10/2017 LAMA SAEEDA - FEL, CVUT 9

10 The iterative methodology 11/10/2017 LAMA SAEEDA - FEL, CVUT 10

11 Text pre processing Extraction methods Unseen text documents To be annotated Input Information Extraction Input Annotated textual corpus Input Ontology Learning Input Seed ontology Domain expert (user) Terms Concepts Relations Axioms 11/10/2017 LAMA SAEEDA - FEL, CVUT 11

12 Information extraction and semi-structured ontology learning iterative methodology - Example Doc1: Rice, a city in Minnesota Person Doc2: Mitch Fielder is mayor of Rice Doc3: Krnačova, mayor of Prague Location Politician Doc4: Tong wrote Rice City has-mayor Mayor Rice has-mayor M.Fielder 11/10/2017 LAMA SAEEDA - FEL, CVUT 12

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14 OBIE from aviation safety reports - Motivation Reporting Tool 11/10/2017 LAMA SAEEDA - FEL, CVUT 14

15 OBIE from aviation safety reports - Motivation Initial report Text Runway incursion caused by inadequate situational awareness and bad weather encounters Report analysis results weather encounter runway incursion 11/10/2017 LAMA SAEEDA - FEL, CVUT 15

16 OBIE from aviation safety reports - Motivation 11/10/2017 LAMA SAEEDA - FEL, CVUT 16

17 System users Semantic Annotations Aviation Domain experts Suggest automatically generated form to the user User input initial aviation safety report Textual aviation safety corpus Ontology Learning Aviation safety Ontology (ASO) Reporting tool Compliant with Generated form Generate form Report model Information extraction and ontology learning from aviation safety reports 11/10/2017 LAMA SAEEDA - FEL, CVUT 17

18 OBIE from aviation safety reports - Motivation Initial report Text Runway incursion caused by inadequate situational awareness and bad weather encounters Report analysis results weather encounter runway incursion 11/10/2017 LAMA SAEEDA - FEL, CVUT 18

19 In-flight shutdown, flight no. TVP7266, KTW-BOJ. Emergency landing in SOF. ENG 2 LOW OIL PRESS, low quantity, FADEC automatically stop the engine. DBpedia Spotlight Apache Stanbol Customized Techniques flight no ENG 2 LOW OIL PRESS engine FADEC ABC1234 In-flight shutdown, flight no. ABC1234, KTW-BOJ. Emergency landing in SOF. ENG 2 LOW OIL PRESS, low quantity, FADEC automatically stop the engine. KTW-BOJ RESTful web service SNAPSHOT-fd/rest/analyze Aviation safety Ontology (ASO) OBIE from aviation safety reports Extraction pipeline 11/10/2017 LAMA SAEEDA - FEL, CVUT 19

20 OBIE from aviation safety reports - Evaluation GATE (General Architecture for Text Engineering) 80 high quality annotated reports Recall With aviation vocabulary without aviation vocabulary with Aviation vocabulary Precision without aviation vocabulary 11/10/2017 LAMA SAEEDA - FEL, CVUT 20

21 OBIE from aviation safety reports Next steps Ontology is essential for information extraction Incompleteness of the ontology Experimenting with various ontology learning techniques and tools Creating the ontology learning pipeline 11/10/2017 LAMA SAEEDA - FEL, CVUT 21

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23 Temporal Knowledge extraction - Motivation Linked open data cloud Data size vs. data quality Dataset exploration Finding useful dataset without prior knowledge is difficult Temporal information Dataset management Temporal filters for search VoID descriptions could be enriched with a temporal dimension Source: "Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. 11/10/2017 LAMA SAEEDA - FEL, CVUT 23

24 Temporal Knowledge extraction - Motivation Structured data no defined schema Dataset summaries: Describe Explore Source: "Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. 11/10/2017 LAMA SAEEDA - FEL, CVUT 24

25 Temporal knowledge extraction - Temporal analysis Temporal data structure in datasets Structured data: e.g. xsd:date, xsd:time, xsd:gyear is-temporal Semi-structured: e.g. dcterms: description, dcterms: title has-temporal-potential 11/10/2017 LAMA SAEEDA - FEL, CVUT 25

26 Temporal knowledge extraction - Temporal analysis Temporal data in Czech Linked Open Data Cloud (Czech LOD) 11/10/2017 LAMA SAEEDA - FEL, CVUT 26

27 Temporal knowledge extraction - Temporal analysis Temporal data in Linked Open Data Vocabularies (LOV) Source 11/10/2017 LAMA SAEEDA - FEL, CVUT 27

28 Temporal knowledge extraction - Temporal analysis Dataset-based vs vocabulary-based CzLOD prov-o goodrelations schema.org dcterms Czech LOD has-temporal-potential Czech LOD is-temporal LOV has-temporal-potential LOV is-temporal is-temporal: Structured has-temporal-potential: semi-structured 11/10/2017 LAMA SAEEDA - FEL, CVUT 28

29 Temporal knowledge extraction - Temporal analysis SUTime (temporal taggers with F-measure score in TempEval-3) + extend the default rule files for SUTime { ruletype : "time, pattern : /dd/mm/yyyy/} 22/10/2017 { ruletype : "time, pattern : /dd-mm-yyyy/} {ruletype : "time, pattern : /[0-9]{1, 4}.?yyyy Sb /} 253/1992 Sb 11/10/2017 LAMA SAEEDA - FEL, CVUT 29

30 Temporal knowledge extraction - Temporal Descriptors Ontology (TDO) Temporal data in Czech Linked Open Data Cloud (Czech LOD) 11/10/2017 LAMA SAEEDA - FEL, CVUT 30

31 Temporal knowledge extraction - Temporal Descriptors Ontology (TDO) 11/10/2017 LAMA SAEEDA - FEL, CVUT 31

32 Temporal knowledge extraction Evaluation & results Temporal coverage of dataset DCAT voc dct:temporal (startdate, enddate) Temporal descriptor considers only structured temporal data Our approach consider actual data (structured + unstructured) 15 datasets: 46.6% of the cases matching (some cases there is no presence of TI description) 46% of the cases we were able to extract more accurate range e.g. Dataset ds:vavai/evaluation/2009 has no structured temporal info. 11/10/2017 LAMA SAEEDA - FEL, CVUT 32

33 Temporal knowledge extraction Conclusion Improve the temporal knowledge representation of the datasets Better exploring experience for users. Timeline visualization as a future work 11/10/2017 LAMA SAEEDA - FEL, CVUT 33

34 Conclusion Support the information extraction process Incomplete ontology Iterative approach Information extraction pipeline Ontology learning pipeline Temporal information extraction Evaluate the iterative approach to other approaches Evaluate involving experts (time, effort) Reduced with advanced iterations? 11/10/2017 LAMA SAEEDA - FEL, CVUT 34