1. Digging Up Data: The Archaeotools project, Faceted Classification and Natural Language Processing in an archaeological context. Stuart Jeffrey, Julian Richards, Fabio Ciravegna Stuart Jeffrey, Julian Richards, Fabio Ciravegna, Stewart Waller, Sam Chapman, Ziqi ZhangTony Austin. Stewart Waller, Sam Chapman, Ziqi Zhang, Tony Austin. UK e-Science All Hands Meeting, Edinburgh, 9 th September 2008

2. AHRC-EPSRC-JISC eScience research grants scheme: AIM: To allow archaeologists to discover, share and analyse datasets and legacy publications which have hitherto been very difficult to integrate into existing digital frameworks BUILDS UPON: Common Information Environment Enhanced Geospatial browser PARTNERS: Natural Language Processing Research Group, Department of Computer Science, University of Sheffield Joint Information Systems Committee

3. Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media).Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media). Workpackage 2 – Natural language processing /Data-mining of Grey Literature; plus taggingWorkpackage 2 – Natural language processing /Data-mining of Grey Literature; plus tagging Workpackage 3 – Data-mining of Historic Literature; plus geoXwalkWorkpackage 3 – Data-mining of Historic Literature; plus geoXwalk Three distinct Workpackages:

4. Datasets include: –National Monuments Records (Scotland, Wales, England) –Excavation Index (EH) –Archive Holdings –Local Authority Historic Environment Records Thesauri include: –Thesaurus of Monuments Types (TMT) –Thesaurus of Object Types –MIDAS Period list –UK Government list of administrative areas, County, District, Parish (CDP) – Not MIDAS

5. Oracle RDBMS MIDAS XML Record Information Extraction RDF Resource Knowledge triple store XML Docs of Thesaurus Query User Interface Information Extraction When, Where, What ontologies as entries to faceted index Input

11. “WHAT” Records that have no subject information Records that use terms not found in TMT, so these records cannot be indexed (6,442 unique terms) Records (1,001,407) 19,269 records (2%) Records (1,001,407) 101,507 records (10.1%)

12. “WHEN” Records that have no temporal information Records that use period terms not found in MIDAS so these records cannot be indexed (457 types of irresolvable dates) Records (1,001,407) 292,793 records (29.2%) Records (1,001,407) 114,505 (11.4%) 1066, 1001-1100,11 th Centuary, C11, 11C, Eleventh Century

13. “WHERE” Records that have no spatial information Records that use terms not found in CDP, so these records cannot be indexed. Records (1,001,407) 11,126(1.1%) Records (1,001,407) 245,601 records (24.5%)

15. linear

16. Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media).Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media). Workpackage 2 – Natural language processing /Data-mining of Grey Literature; plus taggingWorkpackage 2 – Natural language processing /Data-mining of Grey Literature; plus tagging Workpackage 3 – Data-mining of Historic Literature; plus geoXwalkWorkpackage 3 – Data-mining of Historic Literature; plus geoXwalk Three distinct Workpackages:

20. XML tagging of semantic content CIDOC: CRM

21. Information Extraction in Archaeotools What (subject) Where (place name) When (temporal info) Grid reference (easting and northing) Report title Report creator Report publisher Report publisher contact Report publication date Event date Bibliography & references

22. Example annotations in highlighted colours are positive examples Un-annotated texts are negative examples Features of this annotation: first_letter_capitalised: true word_found_in_gazetteer: true preceded_by: the followed_by: period

23. Rule based systems are good for extracting information that match with simple patterns, and/or occur in regular contexts, thus are applied to: Grid reference (easting and northing) Report title* Report creator* Report publisher* Report publication date* Report publisher contact Bibliography & references Machine Learning is good for extracting information that can not be matched by patterns, or occur irregularly with contexts, or are large amount, thus is applied to: What (subject) Where (place name) When (temporal info) Event date

24. Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media).Workpackage 1 - Advanced Faceted Classification /Geo-spatial browser – 1m+ records; 4 primary facets (What, Where, When and Media). Workpackage 2 – Natural language processing /Data-mining of Grey Literature; plus taggingWorkpackage 2 – Natural language processing /Data-mining of Grey Literature; plus tagging Workpackage 3 – Data-mining of Historic Literature; plus geoXwalkWorkpackage 3 – Data-mining of Historic Literature; plus geoXwalk Three distinct Workpackages:

27. http://ads.ahds.ac.uk/project/archaeotools /