4,415 research outputs found
Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web
In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers
Identification of Design Principles
This report identifies those design principles for a (possibly new) query and transformation
language for the Web supporting inference that are considered essential. Based upon these
design principles an initial strawman is selected. Scenarios for querying the Semantic Web
illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of
the query language to be designed and implemented by the REWERSE working group I4
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
RDF Querying
Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before
Intelligent query answering in rule based systems
AbstractWe propose that in large knowledge bases which are collections of atomic facts and general rules (Horn clauses), the rules should be allowed to occur in the answer for a query. We introduce a new concept of the answer for a query which includes both atomic facts and general rules. We provide a method of transforming rules by relational algebra expressions built from projection, join, and selection and demonstrate how the answers consisting of both facts and general rules can be generated
Computing only minimal answers in disjunctive deductive databases
A method is presented for computing minimal answers in disjunctive deductive
databases under the disjunctive stable model semantics. Such answers are
constructed by repeatedly extending partial answers. Our method is complete (in
that every minimal answer can be computed) and does not admit redundancy (in
the sense that every partial answer generated can be extended to a minimal
answer), whence no non-minimal answer is generated. For stratified databases,
the method does not (necessarily) require the computation of models of the
database in their entirety. Compilation is proposed as a tool by which problems
relating to computational efficiency and the non-existence of disjunctive
stable models can be overcome. The extension of our method to other semantics
is also considered.Comment: 48 page
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