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CAA 2011 Beijing SEA: A Framework for Interactive Querying,Visualisation and Statistical Analysis of Linked            Arc...
Talk outline   CAA 2011 BeijingOutline                             Context: Tracing Networks                             M...
Context   CAA 2011 BeijingTracing Networks     Investigates the network of contacts across and beyond     the Mediterranea...
Context   CAA 2011 BeijingTracing Networks               Monika Solanki
Context   CAA 2011 BeijingTracing Networks     Archaeologists study a wide range of material objects.     By tracking them...
Motivation   CAA 2011 BeijingMotivation: Archaeological perspective  Key Barriers to adopting Semantic Web technologies   ...
Motivation   CAA 2011 BeijingMotivation: Computer Science perspective     To increase the uptake and usage of semantically...
Motivation   CAA 2011 BeijingCase study: Human representations  Human representations, identities and social relations in ...
Motivation   CAA 2011 BeijingHuman representations    The analysis generates a large volume of data    Along with details ...
Motivation   CAA 2011 BeijingHuman representations: Informal queries  Example 1:  “Find images of riders who appear on obj...
Motivation   CAA 2011 BeijingHuman representations: Informal queries  Example 2:  “Find all objects which have images of i...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingSemantic Explorer for Archaeology     A web application     RESTf...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingSEA: Architecture                            Monika Solanki
SEA: Semantic Explorer for Archaeology    CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and ...
SEA: Semantic Explorer for Archaeology    CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and ...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingBuilding the query using SEA                            Monika So...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingHuman Representation  “Find images of riders who appear on object...
Sub query Part 1  PREFIX tnh:<http://www.tracingnetworks.ac.uk/              ontology/human_representation.owl#>  PREFIX r...
SEA: Semantic Explorer for Archaeology    CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and ...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingSEA: Visualiser Component    Three visualisation modules.    Quer...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingVisualising the query                            Monika Solanki
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingVisualising the query results: Google earth                      ...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingVisualising the query results                             Monika ...
SEA: Semantic Explorer for Archaeology   CAA 2011 BeijingSEA: RESTful API    The SEA REST API corresponds to a set of serv...
Related work   CAA 2011 BeijingClosely related work     D2RQ: Berlin     Virtuoso: Open Link Software     STAR: Glamorgan,...
Related work   CAA 2011 BeijingGrand vision: The TN-LOD cloud        Tracing Networks through Linked Open Data            ...
Conclusions   CAA 2011 BeijingConclusions    Little work has been done so far in the Semantic web    community that can mo...
Future work   CAA 2011 BeijingFuture work     Implement a user-friendly graphical modeling environment     for the languag...
Acknowledgements     CAA 2011 BeijingAcknowledgements Computer Science    Prof Jose Fiadeiro    Yi Hong Archaeology    Pro...
CAA 2011 BeijingMany Thanks!!!  Monika Solanki
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SEA: A Framework for Interactive Querying, Visualisation and Statistical Analysis of Linked Archaeological Datasets

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Transcript of "SEA: A Framework for Interactive Querying, Visualisation and Statistical Analysis of Linked Archaeological Datasets"

  1. 1. CAA 2011 Beijing SEA: A Framework for Interactive Querying,Visualisation and Statistical Analysis of Linked Archaeological Datasets Monika Solanki m.solanki@mcs.le.ac.uk Department of Computer Science Joint work with Yi Hong Department of Computer Science Katharina Rebay-Salisbury School of Archaeology and Ancient History University of Leicester, UK Monika Solanki
  2. 2. Talk outline CAA 2011 BeijingOutline Context: Tracing Networks Motivation Case study Semantic Explorer for Archaeology Conclusions and Future work Demo Monika Solanki
  3. 3. Context CAA 2011 BeijingTracing Networks Investigates the network of contacts across and beyond the Mediterranean region, between the late bronze age and the late classical period (c.1500-c.200 BCE) by interrogating material objects Seven archaeological case studies fully integrated with computer science projects http://www.tracingnetworks.org/ Monika Solanki
  4. 4. Context CAA 2011 BeijingTracing Networks Monika Solanki
  5. 5. Context CAA 2011 BeijingTracing Networks Archaeologists study a wide range of material objects. By tracking them at every stage of their production, distribution, use, and consumption across a large geographical region, over a long time period, they can trace the links between the people who made, used, and taught others to make them. The Chaîne opératoire Cross-craft interaction Monika Solanki
  6. 6. Motivation CAA 2011 BeijingMotivation: Archaeological perspective Key Barriers to adopting Semantic Web technologies The most time-consuming part of an archaeological investigation is the post-excavation analysis. There is a lack of tools and platforms that provide an integrated environment for interactive querying, visualisation and statistical analysis Traditional search and retrieval mechanisms generally provided “Google” style keyword search or “Library” style drop down search. They assume knowledge of controlled vocabularies, terminology and structure of the underlying ontological schemas. Monika Solanki
  7. 7. Motivation CAA 2011 BeijingMotivation: Computer Science perspective To increase the uptake and usage of semantically rich archaeological data, it needs to be openly available and accessible by humans and applications. An integrated view of diverse data sources is innovative and of immense potential value for the archaeological community. There is therefore a mileage in combining the task of archiving, querying and analysing the data within a single framework. Archaeological data is fragmentary. Inferencing capabilities of reasoners can be used to extract implicit knowledge and contribute to their existing knowledge bases to complete the fragments. Monika Solanki
  8. 8. Motivation CAA 2011 BeijingCase study: Human representations Human representations, identities and social relations in the Late Bronze and Iron Age of Central Europe The scope: examining and analysing human representations on a range of object types and in a range of materials, such as bronze and pottery. The project utilises details such as gestures and postures, dress and associated objects as keys to understanding how identity and new understandings of society are communicated. Raw data is collected through examining objects from published literature or in museum collections. Monika Solanki
  9. 9. Motivation CAA 2011 BeijingHuman representations The analysis generates a large volume of data Along with details of the human representation on objects, the data recorded also includes images of these objects. We have developed a vocabulary that defines various concepts and relationships of interest in the domain of human representation as captured in these images. Using the ontology we generated linked datasets from the raw data. We are currently linking to DBpedia and Geonames, however we are also on the lookout for datasets closely related to archaeology with which we can link in the future. Monika Solanki
  10. 10. Motivation CAA 2011 BeijingHuman representations: Informal queries Example 1: “Find images of riders who appear on objects found in Austria where the altitude of the excavation site is 500 meters above sea level. I would also like to know the statistical distribution of the material and the technologies used for the production of these objects. I would like to visualise the results as a pie chart and see the distribution of the sites where these objects were found on Google Earth”. Monika Solanki
  11. 11. Motivation CAA 2011 BeijingHuman representations: Informal queries Example 2: “Find all objects which have images of individuals in the orant gesture who are wearing a triangular dress, earrings and who carry a vessel on their head, where the vessel is supported by their left hand. I would also like to know the statistical distribution of the gender of these individuals according to the country in which the objects were found. I would like to visualise the results as a tree map and see the distribution of the sites where these objects were found on Google Map”. Monika Solanki
  12. 12. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSemantic Explorer for Archaeology A web application RESTful APIs for programmatically accessing the TN-LOD cloud Interactive and global querying of linked datasets Data visualisations using user defined perspectives Statistical analysis using bespoke criteria provided by archaeologists at runtime Monika Solanki
  13. 13. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: Architecture Monika Solanki
  14. 14. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and an inference engine Aggregates the input data as RDF triples Generates several sub queries each of which correspond to a specific task Formalises the query in SPARQL, includes any constraints Provides an interface through which the SPARQL query generated by aggregating the triples can be edited Monika Solanki
  15. 15. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and an inference engine Queries can be specified intuitively Utilises the WordNet dictionary “Natural Language Query Summariser” Records user preferences: statistical analysis, visualisation Monika Solanki
  16. 16. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingBuilding the query using SEA Monika Solanki
  17. 17. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingHuman Representation “Find images of riders who appear on objects found in Austria where the altitude of the excavation site is 500 meters above sea level. I would also like to know the statistical distribution of the material and the technologies used for the production of these objects. I would like to visualise the results as a pie chart and see the distribution of the sites where these objects were found on Google Earth”. Part 1 Find images of riders who appear on objects found in Austria where the altitude of the excavation site is 500 meters above sea level. Part 2 I would also like to know the statistical distribution of the material and the technologies used for the production of these objects. Monika Solanki
  18. 18. Sub query Part 1 PREFIX tnh:<http://www.tracingnetworks.ac.uk/ ontology/human_representation.owl#> PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?individual ?object ?site ?country ?abbr ?type ?tech ?image ?altitude ?material WHERE{ ?individual rdf:type tnh:Individual. ?individual tnh:appearOn ?object. ?object tnh:isFoundAtSite ?site. ?site tnh:isLocatedInCountry ?country. ?country tnh:hasCountryAbbr ?abbr. ?object tnh:has1stObjectType thn:rider. ?object tnh:hasImageLink ?image. ?site tnh:hasAltitude ?altitude. FILTER (?altitude>=500). FILTER (?abbr="AT"). } LIMIT 3000
  19. 19. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: Query Component Query builder, a SPARQL/SQWRL endpoint and an inference engine Includes an option to specify any reasoning rules. A rule-based inferencing component specified to support deductive reasoning. SWRL or Jena inferencing rules used to derive implicit statements from existing archaeological knowledge bases Monika Solanki
  20. 20. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: Visualiser Component Three visualisation modules. Queries generated by the user Convert the SPARQL triple patterns to GraphML The visualiser is interactive and allows a user to expand/collapse nodes in the graph. Search for a specific node in the graph. Query Results: linked data, markers on the Google Earth/Google maps. Statistical analysis: commonly used statistical analysis models. Monika Solanki
  21. 21. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingVisualising the query Monika Solanki
  22. 22. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingVisualising the query results: Google earth Monika Solanki
  23. 23. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingVisualising the query results Monika Solanki
  24. 24. SEA: Semantic Explorer for Archaeology CAA 2011 BeijingSEA: RESTful API The SEA REST API corresponds to a set of services simply accessible through HTTP calls. The SEA API employs content negotiation to decide whether the result should be encoded in RDF/XML (default), JSON or plain text. We have been inspired by the linked data APIs published by the data.gov.uk. The APIs do not provide support for PUT/POST request. They are meant to provide a read only access layer to the data repositories. The SEA API layer can also act as a proxy over a SPARQL endpoint. This allows a user to specify a sparql query as a query parameter. Monika Solanki
  25. 25. Related work CAA 2011 BeijingClosely related work D2RQ: Berlin Virtuoso: Open Link Software STAR: Glamorgan, English Heritage STELLAR: Glamorgan, English Heritage TRANSLATION: Southampton Monika Solanki
  26. 26. Related work CAA 2011 BeijingGrand vision: The TN-LOD cloud Tracing Networks through Linked Open Data Monika Solanki
  27. 27. Conclusions CAA 2011 BeijingConclusions Little work has been done so far in the Semantic web community that can motivate archaeologists to adopt their technologies to manage and analysis data. An exploratory attempt to reconstruct the Chaîne opératoire using the principles of linked open data. A transformation framework for migrating large volumes of archaeological data stored in RDBs to ontology based data sets on the Semantic Web. SEA: A unified framework that allows archaeologists with basic knowledge of Semantic Web technologies to “explore” their datasets through interactive querying, visualisation and analysis. Monika Solanki
  28. 28. Future work CAA 2011 BeijingFuture work Implement a user-friendly graphical modeling environment for the language in GMF (Graphical Modeling Framework) to allow easy creation and editing of transformation rules. Extend the query interface so that it allows archaeologists to specify ranking heuristics for the search results. Extend the visualisation interface by providing a faceted browser that allows the archaeologist to visualise query results along several facets. Augment the support provided for inference making. Keeping a close eye on the linked data cloud for any relevant archaeological datasets that may eventually be published so that we can link to it. Monika Solanki
  29. 29. Acknowledgements CAA 2011 BeijingAcknowledgements Computer Science Prof Jose Fiadeiro Yi Hong Archaeology Prof Lin Foxhall Katharina Rebay-Salisbury Monika Solanki
  30. 30. CAA 2011 BeijingMany Thanks!!! Monika Solanki
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