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This is a [[event:2013-09|LegalHack@OKCon 2013]] challenge

====== Linked Product Liability Data (LPLD) ======

This project aims to build a Linked Data repository containing information about regimes in product liability.

=== Hackathon goals ===

It is a proof of concept. We would like to investigate and define a systematic approach to collect the data, if possible in an automatic way. 

=== Some possible practical steps ===

  - An existing standard legal ontology has to be found
  - The ontology is then extended with the needed concepts in order to be able to model the different legal theories used to establish a link between plaintiff and defendant.
  - (optional): a vizualisation could be done to offer a didactic overview of what product liability is 
  - A [[http://dydra.com/alogean/lpld|triple store]] is set. 
  - Starting from the defined ontology, the triple store is then populated with  information about different applicable legal product liability theories. This is done by venue (country, state or province) and other criteria (e.g. type of product) and could look like this: 

^ Jurisdiction   ^ Subcontext ^ Valid as of ^ Strict applicable ^ Type of negligence  ^ Comment ^
| Germany        | -          | -           | yes  | ?                       |         |
| Germany        | chemical   | -           | no   | ?                       |         |
| Germany        | Food       |  2010       | yes  | ?                       |         |
| North Carolina | -          |             | yes  | contributory negligence |         |
| ...            | ...        | ....        | ...  | ...                     | ...     |

  - Bonus points: determine anticipated changes to applicable legal theories

==== Data sources ====

  * TBD

==== Team ====

  * [[user:alog|Antoine Logean]]

==== Links ====

  * http://en.wikipedia.org/wiki/Product_liability
  * http://lawlibrary.hamline.edu/businesslaw
  * https://www.gov.uk/product-liability-and-safety-law
 
  
{{tag>status:concept legal needs:dev needs:design needs:data needs:expert}}