Collaborative Working Environment and Ontology
From TracingNetworksWiki
"Collaborative Working Environment and Ontology" is a sub project of Tracing Networks Programme
This research aspect concerns the conceptual, logical and technological infrastructure integrating and supporting all sub-projects. Two more ambitious goals besides the physical infrastructure needed for storing and sharing data are:
- to provide a logical infrastructure to support classification and analysis/interpretation of data. Precisely, an ontology of concepts will be defined for use in repositories of data/elements together with meta-models for tools used by sub-projects to interact. The ontology will offer a uniform representation of data and findings of the other sub-projects together with versatile tools through which unforeseen relationships among heterogeneous datasets may emerge semi-automatically.
- to support technically the different teams in using the tools and interacting through them. This environment should ensure the future collaboration of teams and enable future research by others.We anticipate the use of this environment will induce changes in the methodology used in the community of archaeologists by enabling them effectively and efficiently to capture, code, and analyse context-rich data in situ with (almost) seamless collaboration, independent of time and distance barriers, with experts of a multidisciplinary team, thus contributing to the evolution of the discipline overall.
- Data Collection
Distributed teams are normally unable to use stable connection while working in the fields, in this case, real time update strategy is unimplementable. The idea is to provide a generic importer which synchronises the local database with centralised RDB whenever there is available connection. In this step, all custom-defined DB schema and records remain unchanged after transferring to the centralised RDB. This can be achieved by using an SQL importor, another common approach for data collection is to use XML interchanged as REST, SOA, Web Services or simple RSS feed. However, they are also subject to connectivity.
- Knowledge Representation and Concept Mapping
As the prerequisite of data integration and aggregation, as an extension of CIDOC Conceptual Reference Model, a RDF/OWL domain-specific ontology (e.g. archaeological ontology, biological ontology etc.) will be defined by computer science researcher in collaboration with domain experts (e.g. archaeologist, biologist etc). The ontology will offer a uniform representation of various data collected from multiple teams. Once the ontology is defined, the next step is to define the mapping between Non-RDF database and ontology-based database with a Relational-RDF mapping language such as D2RQ or Virtuoso.
- Context Query and Reasoning
By applying the mapping script defined in the previous step, an ontology-based database will be created so data are kept as RDF triples instead of records in the table. DL-reasoners such as FaCT++ will be applied here to classify the instances. Application can then access triple stores using primitive RDF query language SPARQLin conjunction with deductive rule-based reasoner such as Jena reasoner to discover implicit knowledge behind these data.
- Visualization
The last step is to visualise the ontological data and query results. For each RDF subject-predicate-object triple, subject/object are mapped to vertices while predicate mapped to a directed edge between its subject and object. Rich Internet application tools such as Adobe Flex IDE can be used to show RDF graph as interactive diagram instead of a static image so that user can easily navigate from one node to another to explore the interrelationship among them.
Flex TN ontology browser:FlexOntVisualizer
Online Loomweights Image Database: link
Collaborative Tracing Networks CRM Editor: link
