Annotated RDF

Abstract

Real-world use of RDF requires the ability to transparently represent and explain metadata associated with RDF triples. For example, when RDF triples are extracted automatically by information extraction programs, there is a need to represent where the triples came from, what their temporal validity is, and how certain we are that the triple is correct. Today, there is no theoretically clean and practically scalable mechanism that spans these different needs - reification is the only solution propose to date, and its implementations have been ugly. In this paper, we present Annotated RDF (or aRDF for short) in which RDF triples are annotated by members of a partially ordered set (with bottom element) that can be selected in any way desired by the user. We present a formal declarative semantics (model theory) for annotated RDF and develop algorithms to check consistency of aRDF theories and to answer queries to aRDF theories. We show that annotated RDF supports users who need to think about the uncertainty, temporal aspects, and provenance of the RDF triples in an RDF database. We develop a prototype aRDF implementation and show that our algorithms work efficiently even on real world data sets containing over 10 million triples

Contextualized knowledge repositories for the Semantic Web

“…, A n }, let us define: One may object that for encoding a set of attributes with structured value-sets in the meta knowledge, such a powerful formalism as DL may be unnecessary. Several examples using simply ordered sets are found in the literature [19,5,20]. We have however intentionally chosen DL, as this brings the option to employ reasoning also at the meta level.…”

Practical RDF Schema Reasoning with Annotated Semantic Web Data

“…Termination is guaranteed in these circumstances by our own results on fuzzy logic programming [5], and are far from trivial since an infinitely-valued lattice is being used. In fact, one of the distinctions between the work [26] and [22], is that the former is restricted to finite annotation lattices while the latter allows infinite ones. A particularly striking example of the problems that can occur is the use of real-valued product t-norm which can generate "new" values from existing ones, contrasting to the minimum t-norm that can only return existing annotations in the asserted data.…”

“…The need to extend this data with meta-information like trust, provenance and confidence [26,22,3] imposed new requirements and extensions to the Resource Description Framework (Schema) [19] to handle annotations appropriately. Briefly, an annotation v from a suitable mathematical structure is added to the ordinary triples (s p o) obtaining (s p o) : v, annotating with v the statement that subject s is related via property p to object o.…”

“…Briefly, an annotation v from a suitable mathematical structure is added to the ordinary triples (s p o) obtaining (s p o) : v, annotating with v the statement that subject s is related via property p to object o. The general semantics of this RDFS extension has been recently addressed [21,22] improving the initial work of [26], but only a memory-based Prolog implementation is available.…”

Interactive Exploration of Fuzzy RDF Knowledge Bases

“…Note that various approaches have been proposed recently for annotating triples with a degree of truth and giving a semantics to such annotations, e.g. [16,18], and can be used as the representation framework. Among the various possible approaches for exploiting fuzzy degrees at the interaction level, we propose one based on intervals, leading to a simple and intuitive dialog.…”