Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world-knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page

LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur

A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

Defining the selective mechanism of problem solving in a distributed system.

Distribution and parallelism are historically important approaches for the implementation of artificial intelligence systems. Research in distributed problem solving considers the approach of solving a particular problem by sharing the problem across a number of cooperatively acting processing agents. Communicating problem solvers can cooperate by exchanging partial solutions to converge on global results. The purpose of this research programme is to make a contribution to the field of Artificial Intelligence by developing a knowledge representation language. The project has attempted to create a computational model using an underlying theory of cognition to address the problem of finding clusters of relevant problem solving agents to provide appropriate partial solutions, which when put together provide the overall solution for a given complex problem. To prove the validity of this approach to problem solving, a model of a distributed production system has been created. A model of a supporting parallel architecture for the proposed distributed production problem solving system (DPSS) is described, along with the mechanism for inference processing. The architecture should offer sufficient computing power to cope with the larger search space required by the knowledge representation, and the required faster methods of processing. The inference engine mechanism, which is a combination of task sharing and result sharing perspectives, is distinguished into three phases of initialising, clustering and integrating. Based on a fitness measure derived to balance the communication and computation for the clusters, new clusters are assembled using genetic operators. The algorithm is also guided by the knowledge expert. A cost model for fitness values has been used, parameterised by computation ration and communication performance. Following the establishment of this knowledge representation scheme and identification of a supporting parallel architecture, a simulation of the array of PEs has been developed to emulate the behaviour of such a system. The thesis reports on findings from a series of tests used to assess its potential gains. The performance of the DPSS has been evaluated to verify the validity of this approach by measuring the gain in speed of execution in a parallel environment as compared with serial processing. The evaluation of test results shows the validity of the proposed approach in constructing large knowledge based systems

Parsing with parallelism : a spreading-activation model of inference processing during text understanding

The past decade of reseatch in Natural Language Processing has universally recognized that, since natural language input is almost always ambiguous with respect to its pragmatic implications, its syntactic parse, and even its lexical analysis (i.e., choice of correct word-sense for an ambiguous word), processing natural language input requires decisions about word meanings, syntactic structure, and pragmatic inferences. The lexical, syntactic, and pragmatic levels of inferencing are not as disparate as they have often been treated in both psychological and artificial intelligence research. In fact, these three levels of analysis interact to form a joint interpretation of text.ATLAST (A Three-level Language Analysis SysTem) is an implemented integration of human language understanding at the lexical, the syntactic, and the pragmatic levels. For psychological validity, ATLAST is based on results of experiments with human subjects. The ATLAST model uses a new architecture which was developed to incorporate three features: spreading activation memory, two-stage syntax, and parallel processing of syntax and semantics. It is also a new framework within which to interpret and tackle unsolved problems through implementation and experimentation