tory [24]. This explicit meaning provides machines a richer landscape for supporting humans in their information discovery and sharing activities. However, while links are typed on the Semantic Web, the meaning of the type is still primarily based on human interpretation. Granted this meaning is identified by a URI, however, for the machine, there exists no meaning, just symbols that it has been "hardwired" to handle [41].

"Machine usable content presumes that the machine knows what to do with information on the Web. One way for this to happen is for the machine to read and process a machine-sensible specification of the semantics of the information. This is a robust and very challenging approach, and largely beyond the current state of the art. A much simpler alternative is for the human Web application developers to hardwire the knowledge into the software so that when the machine runs the software, it does the correct thing with the information." [41]
Because relationships among resources are only denoted by a URI, many issues arise around the notion of *context*. Context-senstive algorithms have been developed to deal with problems such as term disambiguation [30], naming conflicts [39], and ontology integration [43, 40]. The cause of such problems is the fact that statements, by themselves, ignore the situatedness that defines the semantics of such assertions [18]. Similar criticism directed towards the issue of semantics has appeared in other specialized literature [38, 47, 49].

Sheth et. al. [37] have framed this issue well and have provided a clear distinction between the various levels of meaning on the Semantic Web.5

- *Implicit* semantics reside within the minds of humans as a collective consensus and as such, are not explicitly recorded in some machine processable medium.

- *Formal* semantics are in a machine-readable format in the form of an ontology and are primarily used for human consumption and for machine hardwiring.

- *Soft* semantics are extracted from probabilistic and fuzzy reasoning mechanisms supporting degree of membership and certainty.
The model proposed in this article primarily falls within the domain of soft semantics. Simply put, the purpose of the model is to supplement a statement with other statements. These other statements, while being part of the RDF
graph itself, serve to contextualize the original statement.

"Contextualization is a word first used in sociolinguistics to refer to the use of language and discourse to signal relevant aspects of an interactional or communicative situation."6 The supplementary statements serve to expose the relevant aspects of the interaction between a subject and an object that are tied by a relationship defined by a predicate. With respect to the example of the statement
(lanl:marko, foaf:knows, ucla:apepe), supplementary statements help to 5 A similar presentation is also presented in [41].

6 Wikipedia (http://en.wikipedia.org/wiki/Contextualization).