scholarly journals Consistent Inference for Dialogue Relation Extraction

2021 ◽  
Author(s):  
Xinwei Long ◽  
Shuzi Niu ◽  
Yucheng Li
Keyword(s):  
Performance Improvement ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Order Logic ◽  
Experimental Results ◽  
First Order Logic ◽  
Specific Topic ◽  
Inference Method ◽  
First Order ◽  
Benchmark Datasets

Relation Extraction is key to many downstream tasks. Dialogue relation extraction aims at discovering entity relations from multi-turn dialogue scenario. There exist utterance, topic and relation discrepancy mainly due to multi-speakers, utterances, and relations. In this paper, we propose a consistent learning and inference method to minimize possible contradictions from those distinctions. First, we design mask mechanisms to refine utterance-aware and speaker-aware representations respectively from the global dialogue representation for the utterance distinction. Then a gate mechanism is proposed to aggregate such bi-grained representations. Next, mutual attention mechanism is introduced to obtain the entity representation for various relation specific topic structures. Finally, the relational inference is performed through first order logic constraints over the labeled data to decrease logically contradictory predicted relations. Experimental results on two benchmark datasets show that the F1 performance improvement of the proposed method is at least 3.3% compared with SOTA.

Robot Task Planning Using First-Order Logic

2000 ◽  
Author(s):  
Lei Yan ◽  
K. Krishnamurthy
Keyword(s):  
Inference Engine ◽  
Knowledge Bases ◽  
Order Logic ◽  
First Order Logic ◽  
Task Planning ◽  
Inference Method ◽  
First Order ◽  
Simulation Results ◽  
Robot Task ◽  
Domain Information

Abstract Task planning for a robot operating in an unknown environment using first-order logic is considered in this study. The approach is to use one agent to simulate the robot and a second agent to simulate the environment. Both agents employ knowledge bases and an inference engine. The rules for the knowledge bases are developed using first-order logic and the inference method is based on hyper-resolution. A weighting scheme is used by the robot to decide on the action to be taken. After enough domain information is obtained, a task planner, which is also based on rules, is employed. Simulation results validating the methodology are presented for a robot moving inside a warehouse with four rooms. In this example, the environment is initially unknown to the robot. But after mapping the environment, the robot can efficiently plan tasks such as moving an object from one room to another.

scholarly journals On the Expressiveness of Levesque's Normal Form

2008 ◽  
Vol 31 ◽  
pp. 259-272
Author(s):  
Y. Liu ◽  
G. Lakemeyer
Keyword(s):  
Normal Form ◽  
Knowledge Base ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Method ◽  
Closed World Assumption ◽  
First Order ◽  
Closed World ◽  
Polynomial Size

Levesque proposed a generalization of a database called a proper knowledge base (KB), which is equivalent to a possibly infinite consistent set of ground literals. In contrast to databases, proper KBs do not make the closed-world assumption and hence the entailment problem becomes undecidable. Levesque then proposed a limited but efficient inference method V for proper KBs, which is sound and, when the query is in a certain normal form, also logically complete. He conjectured that for every first-order query there is an equivalent one in normal form. In this note, we show that this conjecture is false. In fact, we show that any class of formulas for which V is complete must be strictly less expressive than full first-order logic. Moreover, in the propositional case it is very unlikely that a formula always has a polynomial-size normal form.

scholarly journals EPR-based k-induction with Counterexample Guided Abstraction Refinement

10.29007/scv7 ◽  
2018 ◽  
Author(s):  
Zurab Khasidashvili ◽  
Konstantin Korovin ◽  
Dmitry Tsarkov
Keyword(s):  
Model Checking ◽  
Bounded Model Checking ◽  
Order Logic ◽  
Experimental Results ◽  
Theorem Prover ◽  
First Order Logic ◽  
Safety Properties ◽  
Abstraction Refinement ◽  
First Order ◽  
Automated Theorem Prover

In recent years it was proposed to encode bounded model checking (BMC) into the effectively propositional fragment of first-order logic (EPR). The EPR fragment can provide for a succinct representation of the problem and facilitate reasoning at a higher level.In this paper we present an extension of the EPR-based bounded model checkingwith k-induction which can be used to prove safety properties of systems overunbounded runs. We present a novel abstraction-refinement approach based onunsatisfiable cores and models (UCM) for BMC and k-induction in the EPR setting.We have implemented UCM refinements for EPR-based BMC and k-induction in a first-order automated theorem prover iProver. We also extended iProver with the AIGER format and evaluated it over the HWMCC'14 competition benchmarks. The experimental results are encouraging. We show that a number of AIG problems can be verified until deeper bounds with the EPR-based model checking.

scholarly journals Evaluating Automated Theorem Provers Using Adimen-SUMO

10.29007/hplh ◽  
2018 ◽  
Author(s):  
Javier Álvez ◽  
Paqui Lucio ◽  
German Rigau
Keyword(s):  
Order Logic ◽  
Experimental Results ◽  
First Order Logic ◽  
First Order ◽  
Theorem Provers ◽  
Small Set

We report on the results of evaluating the performance automated theorem provers using \ADIMENSUMO{}. The evaluation follows the adaptation of the methodology based on competency questions \cite{GrF95} to the framework of first-order logic, which is presented in \cite{ALR15}, and is applied to \ADIMENSUMO{} \cite{ALR12}. The set of competency questions used for this evaluation has been semi-automatically generated from a small set of semantic patterns and the mapping of \WORDNET{} to \SUMO{}, also introduced in \cite{ALR15}. Our experimental results demonstrate that improved versions of the proposed set of competency questions could be really valuable for the development of automated theorem provers.

scholarly journals Compiling Input* FO(·) inductive definitions into tabled prolog rules for IDP3

2013 ◽  
Vol 13 (4-5) ◽  
pp. 691-704 ◽  
Author(s):  
JOACHIM JANSEN ◽  
ALBERT JORISSEN ◽  
GERDA JANSSENS
Keyword(s):  
Order Logic ◽  
Experimental Results ◽  
First Order Logic ◽  
Partial Functions ◽  
First Order ◽  
Inductive Definitions ◽  
Input Structure ◽  
Model Generator ◽  
Unit Propagation

AbstractFO(·)IDP3extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and usesXSBProlog to compute their interpretation. The interpretation of these predicates is then added to the input structure, their definitions are removed from the theory and further processing is done by the standard IDP3 system. Experimental results show the effectiveness of our method.

First-Order Logic Reasoning Support for the Semantic Web

2009 ◽  
Vol 19 (12) ◽  
pp. 3091-3099 ◽  
Author(s):  
Gui-Hong XU ◽  
Jian ZHANG
Keyword(s):  
Semantic Web ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

Boolean-valued structures

2018 ◽  
Author(s):  
Tim Button ◽  
Sean Walsh
Keyword(s):  
Model Theory ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Rules ◽  
Mathematical Concepts ◽  
Semantic Framework ◽  
First Order ◽  
Standard Models ◽  
Boolean Valued Model ◽  
Semantic Values

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

scholarly journals GAMES AND CARDINALITIES IN INQUISITIVE FIRST-ORDER LOGIC

2021 ◽  
pp. 1-28
Author(s):  
IVANO CIARDELLI ◽  
GIANLUCA GRILLETTI
Keyword(s):  
Order Logic ◽  
First Order Logic ◽  
First Order

scholarly journals Extensions in graph normal form

2020 ◽  
Author(s):  
Michał Walicki
Keyword(s):  
Normal Form ◽  
Fixed Points ◽  
Propositional Logic ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Special Cases

Abstract Graph normal form, introduced earlier for propositional logic, is shown to be a normal form also for first-order logic. It allows to view syntax of theories as digraphs, while their semantics as kernels of these digraphs. Graphs are particularly well suited for studying circularity, and we provide some general means for verifying that circular or apparently circular extensions are conservative. Traditional syntactic means of ensuring conservativity, like definitional extensions or positive occurrences guaranteeing exsitence of fixed points, emerge as special cases.

Relational Chase Procedures Interpreted as Resolution with Paramodulation1

1991 ◽  
Vol 15 (2) ◽  
pp. 123-138
Author(s):  
Joachim Biskup ◽  
Bernhard Convent
Keyword(s):  
Alternative Interpretation ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Problem ◽  
First Order ◽  
Inference Problems ◽  
The One ◽  
The Relationship ◽  
Theoretical Foundations ◽  
Insight Into

In this paper the relationship between dependency theory and first-order logic is explored in order to show how relational chase procedures (i.e., algorithms to decide inference problems for dependencies) can be interpreted as clever implementations of well known refutation procedures of first-order logic with resolution and paramodulation. On the one hand this alternative interpretation provides a deeper insight into the theoretical foundations of chase procedures, whereas on the other hand it makes available an already well established theory with a great amount of known results and techniques to be used for further investigations of the inference problem for dependencies. Our presentation is a detailed and careful elaboration of an idea formerly outlined by Grant and Jacobs which up to now seems to be disregarded by the database community although it definitely deserves more attention.

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