Functional Requirements for Information Resource Provenance on the Web

HTTP transactions have semantics that can be interpreted in many ways. At a low level, a physical stream of bits is transmitted from server to client. Higher up, those bits resolve into a message with a specific bit pattern. More abstractly, information, regardless of the physical representation, has been transferred. While the mechanisms as- sociated with these abstractions, such as content negotiation, are well established, the semantics behind these abstractions are not. We extend the library science resource model Functional Requirements for Bibli- ographic Resources (FRBR) with cryptographic message and content digests to create a Functional Requirements for Information Resources (FRIR) ontology that is integrated with the W3C Provenance Ontology (PROV-O) to model HTTP transactions in a way that clarifies the many relationships between a given URL and all representations received from its request. Use of this model provides fine-grained provenance explana- tions that are complementary to existing explanations of web resources. Furthermore, we provide a formal explanation of the relationship between HTTP URLs and their representations that conforms with the existing World Wide Web architecture. This establishes the semiotic relationships between different information abstractions, their symbols, and the things they represent.

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Associated Projects

The Inference Web is a Semantic Web based knowledge provenance infrastructure that supports interoperable explanations of sources, assumptions, learned information, and answers as an enabler for trust.

The LOGD project investigates the role of Semantic Web technologies, especially Linked Data, in producing, enhancing and utilizing government data published on Data.gov and other websites. Large portion of government data published on the Web are not necessarily ready for mashups. The Tetherless World Constellation (TWC) is now publishing over 8 billions RDF triples converted from hundreds of government-related datasets from Data.gov and other sources (e.g.

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