2,340 research outputs found
Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality
Materialisation precomputes all consequences of a set of facts and a datalog
program so that queries can be evaluated directly (i.e., independently from the
program). Rewriting optimises materialisation for datalog programs with
equality by replacing all equal constants with a single representative; and
incremental maintenance algorithms can efficiently update a materialisation for
small changes in the input facts. Both techniques are critical to practical
applicability of datalog systems; however, we are unaware of an approach that
combines rewriting and incremental maintenance. In this paper we present the
first such combination, and we show empirically that it can speed up updates by
several orders of magnitude compared to using either rewriting or incremental
maintenance in isolation.Comment: All proofs contained in the appendix. 7 pages + 4 pages appendix. 7
algorithms and one table with evaluation result
Towards a Rule Interchange Language for the Web
This articles discusses rule languages that are needed for a a
full deployment of the SemanticWeb. First, it motivates the need for such
languages. Then, it presents ten theses addressing (1) the rule and/or
logic languages needed on the Web, (2) data and data processing, (3)
semantics, and (4) engineering and rendering issues. Finally, it discusses
two options that might be chosen in designing a Rule Interchange Format
for the Web
A knowledge-based system design/information tool
The objective of this effort was to develop a Knowledge Capture System (KCS) for the Integrated Test Facility (ITF) at the Dryden Flight Research Facility (DFRF). The DFRF is a NASA Ames Research Center (ARC) facility. This system was used to capture the design and implementation information for NASA's high angle-of-attack research vehicle (HARV), a modified F/A-18A. In particular, the KCS was used to capture specific characteristics of the design of the HARV fly-by-wire (FBW) flight control system (FCS). The KCS utilizes artificial intelligence (AI) knowledge-based system (KBS) technology. The KCS enables the user to capture the following characteristics of automated systems: the system design; the hardware (H/W) design and implementation; the software (S/W) design and implementation; and the utilities (electrical and hydraulic) design and implementation. A generic version of the KCS was developed which can be used to capture the design information for any automated system. The deliverable items for this project consist of the prototype generic KCS and an application, which captures selected design characteristics of the HARV FCS
End-to-End Differentiable Proving
We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules.Comment: NIPS 2017 camera-ready, NIPS 201
The Galileo PPS expert monitoring and diagnostic prototype
The Galileo PPS Expert Monitoring Module (EMM) is a prototype system implemented on the SUN workstation that will demonstrate a knowledge-based approach to monitoring and diagnosis for the Galileo spacecraft Power/Pyro subsystems. The prototype will simulate an analysis module functioning within the SFOC Engineering Analysis Subsystem Environment (EASE). This document describes the implementation of a prototype EMM for the Galileo spacecraft Power Pyro Subsystem. Section 2 of this document provides an overview of the issues in monitoring and diagnosis and comparison between traditional and knowledge-based solutions to this problem. Section 3 describes various tradeoffs which must be considered when designing a knowledge-based approach to monitoring and diagnosis, and section 4 discusses how these issues were resolved in constructing the prototype. Section 5 presents conclusions and recommendations for constructing a full-scale demonstration of the EMM. A Glossary provides definitions of terms used in this text
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