Clause Logic Research Articles

Optimizing automated compliance checking with ontology-enhanced natural language processing: Case in the fire safety domain

The fire safety compliance checking (FSCC) plays a crucial role in ensuring the quality of fire engineering design and eliminating inherent fire hazards. It requires an objective and rational interpretation of fire regulations. However, the texts of fire regulations are filled with numerous rules related to spatial limitations, which pose a significant challenge in interpreting them. The current method of interpreting these rules mostly relies on manual translation, which is not efficient. To address this issue, this study proposes an innovative automated framework for interpreting rules by combining ontology technology with natural language processing (NLP). Through the utilization of pre-trained language models (PLMs), concepts and relationships are extracted from sentences, a domain-specific ontology is established, spatial knowledge is transformed into language-agnostic tree structures based on the ontology, and the semantic components of spatial relationships are extracted. The tree structure is then mapped to logical clauses based on semantic consistency, thereby improving the efficiency of interpretation. Experimental results demonstrate that the architecture achieves an F1 score of 86.27 for entity extraction and 81.81 for spatial relationship joint extraction tasks, with an accuracy of 96.26% in the formalization of logical rules, highlighting its proficiency in automatically interpreting fire spatial rules. This study offers technical support to enhance public understanding of fire safety management and fire prevention predictions, thereby promoting the intelligent management of the building safety environment.

Intelligent identification of risks in construction contract clauses based on semantic reasoning

Purpose The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.Design/methodology/approach This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.Findings The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.Originality/value This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.

Complete inference via knowledge Petri nets and resolution rules

A novel inference approach is presented based on knowledge Petri nets and resolution rules. First, a knowledge base is modeled as a knowledge Petri net, where logical clauses are represented by monitor places, called semantic places. Second, a resolution pair is defined to identify a pair of semantic places, which, corresponding to two clauses that can be resolved, can be used to generate a new place in the knowledge Petri net. Such a new place represents an unknown clause implied by the knowledge base. Third, a method is proposed to decide whether a resolution inference is redundant based on the resolution pair. The size of a knowledge Petri net can be reduced, and the inference computation is saved in this way. Fourth, an inference algorithm is proposed employing the resolution pair and knowledge Petri net, proven to be sound and complete. Its computational complexity is polynomial with respect to the number of logical variables and clauses. In addition, another inference algorithm is developed for a given complete knowledge base and a set of newfound clauses. The algorithm is proven to be sound and complete, which offers significantly reduced computational complexity and specifically is linear concerning the number of new clauses. Finally, the wumpus world problem is taken as an example to illustrate and verify the proposed inference algorithms.

Modeling of 3 SAT discrete Hopfield neural network optimization using genetic algorithm optimized K-modes clustering

<p>The discrete Hopfield neural network 3-satisfiability (DHNN-3SAT) model represents an innovative application of deep learning techniques to the Boolean SAT problem. Existing research indicated that the DHNN-3SAT model demonstrated significant advantages in handling 3SAT problem instances of varying scales and complexities. Compared to traditional heuristic algorithms, this model converged to local minima more rapidly and exhibited enhanced exploration capabilities within the global search space. However, the model faced several challenges and limitations. As constraints in SAT problems dynamically increased, decreased, or changed, and as problem scales expanded, the model's computational complexity and storage requirements may increase dramatically, leading to reduced performance in handling large-scale SAT problems. To address these challenges, this paper first introduced a method for designing network synaptic weights based on fundamental logical clauses. This method effectively utilized the synaptic weight information from the original SAT problem within the DHNN network, thereby significantly reducing redundant computations. Concrete examples illustrated the design process of network synaptic weights when constraints were added, removed, or updated, offering new approaches for managing the evolving constraints in SAT problems. Subsequently, the paper presented a DHNN-3SAT model optimized by genetic algorithms combined with K-modes clustering. This model employed genetic algorithm-optimized K-modes clustering to effectively cluster the initial space, significantly reducing the search space. This approach minimized the likelihood of redundant searches and reduced the risk of getting trapped in local minima, thus improving search efficiency. Experimental tests on benchmark datasets showed that the proposed model outperformed traditional DHNN-3SAT models, DHNN-3SAT models combined with genetic algorithms, and DHNN-3SAT models combined with imperialist competitive algorithms across four evaluation metrics. This study not only broadened the application of DHNN in solving 3SAT problems but also provided valuable insights and guidance for future research.</p>

A first polynomial non-clausal class in many-valued logic

The relevance of polynomial classes of formulas to deductive efficiency motivated their further research, and currently, a great number of such classes is known. Nevertheless, they have been exclusively sought in the setting of propositional logic and clausal form, which is of course expressively limiting for real-world applications.Along these lines and towards making tractability applicable beyond propositional clausal logic, firstly, we define theRegular many-valuedHorn Non-Clausal (NC) class, or RH, obtained by suitably amalgamating both regular classes: Horn and NC. Then we show that recognizing whether any NC formula is Horn-NC takes only linear time.Secondly, we demonstrate that the relationship between RH and: (1) its subclass of regular Horn formulas is that syntactically RH subsumes the Horn class but semantically both classes are equivalent; and (2) its superclass of regular non-clausal formulas is that RH contains all non-clausal formulas whose clausal form is Horn.Thirdly, we define Regular Non-Clausal Unit-Resolution, or RURNC, and prove that RURNC is complete for RH and also checks its satisfiability in polynomial time. Altogether, RH is a class recognized and solved polynomially, which shows that our intended goal is reached since RH is many-valued, non-clausal and tractable.Fourthly, based on RH, we characterize extensive classes of Horn-NC-like formulas, super-classes of RH, which are composed of formulas that are logically equivalent to some formula in RH. We furnish syntactical patterns of such Horn-NC-like formulas, being outside RH and whose satisfiability test is also decidable in polynomial time.As RH and RURNC are, both, basic in the DPLL scheme, the most efficient in propositional logic, and can be extended to some other non-classical logics, we argue that they pave the way for efficient non-clausal DPLL-based approximate reasoning.Field:Tractable Approximate Automated Reasoning.

Symbolic knowledge extraction from opaque ML predictors in PSyKE: Platform design & experiments

A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) predictors – such as neural networks – by extracting symbolic knowledge out of them, in the form of either rule lists or decision trees. By acting as a surrogate model, the extracted knowledge aims at revealing the inner working of the black box, thus enabling its inspection, representation, and explanation. Various knowledge-extraction algorithms have been presented in the literature so far. Unfortunately, running implementations of most of them are currently either proofs of concept or unavailable. In any case, a unified, coherent software framework supporting them all – as well as their interchange, comparison, and exploitation in arbitrary ML workflows – is currently missing. Accordingly, in this paper we discuss the design of PSyKE, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. Notably, PSyKE targets symbolic knowledge in logic form, allowing the extraction of first-order logic clauses. The extracted knowledge is thus both machine- and human-interpretable, and can be used as a starting point for further symbolic processing—e.g. automated reasoning.

Regulatory information transformation ruleset expansion to support automated building code compliance checking

The traditional building code compliance checking process mainly relies on design reviewers to review design documents or models manually. The intensive manual effort needed makes this process time-consuming, costly, and error-prone. Automated compliance checking (ACC) could be a promising upgrade of the traditional manual code compliance checking. With a reduced workload on design reviewers, ACC is cheaper, faster, and immune to human errors. To support ACC, building code requirements need to be represented in a computer-processable format to enable automated reasoning, which will in turn allow an automated assessment of the building design's compliance status with building codes. A major limitation of many existing ACC systems/methods is their limited range of checkable building code requirements. To address that, the state of the art uses pattern matching-based rules to transform building code requirements into computable formats automatically, but the ruleset was developed and tested only on a few chapters of building code requirements. An efficient ruleset expansion method is needed to increase its range of checkable building code requirements at a low cost, to bring ACC systems closer to full deployment. In this paper, the authors proposed a new regulatory information transformation ruleset expansion method for expanding an existing ruleset. This method can expand the range of checkable code requirements of ACC systems without significant manual effort. The proposed ruleset expansion method takes an iterative approach to ensure the generality and validity of new pattern matching-based rules and the quality of information transformation results. The expanded ruleset was tested on generating logic clauses from Chapter 5 of the International Building Code 2015. Compared to the baseline ruleset, the expanded ruleset increased the predicate-level precision, recall, and F1-score of the logic clause generation by 10.44%, 25.72%, and 18.02%, to 95.17%, 96.60%, and 95.88%, respectively.

Regularizing deep networks with prior knowledge: A constraint-based approach

Deep Learning architectures can develop feature representations and classification models in an integrated way during training. This joint learning process requires large networks with many parameters, and it is successful when a large amount of training data is available. Instead of making the learner develop its entire understanding of the world from scratch from the input examples, the injection of prior knowledge into the learner seems to be a principled way to reduce the amount of require training data, as the learner does not need to induce the rules from the data. This paper presents a general framework to integrate arbitrary prior knowledge into learning. The domain knowledge is provided as a collection of first-order logic (FOL) clauses, where each task to be learned corresponds to a predicate in the knowledge base. The logic statements are translated into a set of differentiable constraints, which can be integrated into the learning process to distill the knowledge into the network, or used during inference to enforce the consistency of the predictions with the prior knowledge. The experimental results have been carried out on multiple image datasets and show that the integration of the prior knowledge boosts the accuracy of several state-of-the-art deep architectures on image classification tasks.

Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.

An overview of distance and similarity functions for structured data

The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.

Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR.

Background Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems. Objective In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets. Methods The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs. Results In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance. Conclusion Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.

Three-valued bounded model checking with cause-guided abstraction refinement

• SAT-based approach to the verification of concurrent software systems. • Based on three-valued predicate abstraction and a propositional logic encoding. • Cause-guided refinement that derives new predicates from unsatisfied clauses. • Support of fairness constraints for verification of liveness properties. • Fully-automatic verification with iterative abstraction refinement. We present a technique for verifying concurrent software systems via SAT-based three-valued bounded model checking. It is based on a direct transfer of the system to be analysed and a temporal logic property into a propositional logic formula that encodes the corresponding model checking problem. In our approach we first employ three-valued abstraction which gives us an abstract system defined over predicates with the possible truth values true , false and unknown . The state space of the abstract system is then logically encoded. The verification result of the encoded three-valued model checking problem can be obtained via two satisfiability checks, one for an over-approximation of the encoding and one for an under-approximation. True and false results can be immediately transferred to the system under consideration. In case of an unknown result, the current abstraction is too imprecise for a definite outcome. In order to achieve the necessary precision, we have developed a novel cause-guided abstraction refinement procedure. An unknown result always entails a truth assignment that only satisfies the over-approximation, but not the under-approximation. We determine the propositional logic clauses of the under-approximation that are not satisfied under the assignment. These clauses contain unknown as a constant. Each unknown is associated with a cause of uncertainty that refers to missing predicates that are required for a definite model checking result. Our procedure adds these predicates to the abstraction and constructs the encoding corresponding to the refined model checking problem. The procedure is iteratively applied until a definite result can be obtained. We have integrated our novel refinement approach into a SAT-based three-valued bounded model checker. In an experimental evaluation, we show that our approach allows to automatically and quickly reach the right level of abstraction for solving software verification tasks.

Towards Coinductive Theory Exploration in Horn Clause Logic: Position Paper

Coinduction occurs in two guises in Horn clause logic: in proofs of self-referencing properties and relations, and in proofs involving construction of (possibly irregular) infinite data. Both instances of coinductive reasoning appeared in the literature before, but a systematic analysis of these two kinds of proofs and of their relation was lacking. We propose a general proof-theoretic framework for handling both kinds of coinduction arising in Horn clause logic. To this aim, we propose a coinductive extension of Miller et al's framework of uniform proofs and prove its soundness relative to coinductive models of Horn clause logic.

Verbal groups in Manchu: a systemic functional account

This paper investigates the functional grammar of the Manchu language with a principal focus on verbal groups, based on a folktale in Manchu Veritable Records. The verbal group is positioned as the final structural element of a Manchu clause, involved in creating experiential (event, tense, voice), interpersonal (mood, polarity) and logical (clause and verbal group complexing) functions. Some of the functions are performed at group rank by auxiliaries, some at word rank by suffixation on the event verb. The descriptive account also includes the verbal groups’ involvement in embedding. To facilitate descriptions, system networks are drawn up at various stages to capture the paradigmatic oppositions and interdependencies among different systems. Listed as one of the ‘critically endangered’ languages by UNESCO, the Manchu language has not yet been described, recorded or analyzed from the systemic functional perspective. Within the theoretical framework of systemic functional grammar, this paper attempts to account for one particular aspect of Manchu grammar, in a bid to expand the appliability of Systemic Functional Linguistics and make it contribute effectively to the description and preservation of endangered languages.

Proof-relevant Horn Clauses for Dependent Type Inference and Term Synthesis

AbstractFirst-order resolution has been used for type inference for many years, including in Hindley-Milner type inference, type-classes, and constrained data types. Dependent types are a new trend in functional languages. In this paper, we show that proof-relevant first-order resolution can play an important role in automating type inference and term synthesis for dependently typed languages. We propose a calculus that translates type inference and term synthesis problems in a dependently typed language to a logic program and a goal in the proof-relevant first-order Horn clause logic. The computed answer substitution and proof term then provide a solution to the given type inference and term synthesis problem. We prove the decidability and soundness of our method.

Kurdish EFL learners’ conceptual transfer in L2 writing

Abstract
 The phenomenon of language transfer in SLA learning and use is perennial and cannot be silenced easily. In L2 writing, the phenomenon is found to affect the written products sound nonnative and, even ambiguous. It is thought that the transfer occurs at the conceptual and structural level of language use. The present paper examines Kurdish EFL learners’ writing aiming at identifying transfer types, particularly, the negative transfer. To this end, 20 university level English major students argumentative writing are analyzed focusing on the conjuncts and adjuncts to find out any track of L1 concepts. The logical clause relationship of cause-effect was the area of focus. The results of the study showed that L1 concepts have been used in forming the relations between sentences and clauses spelt out by lexical signals of sentence connectors and subordinators. It is also found that L1 concepts transferred into L2 writing. The insights gained from the results of the study reveal that there is a problem, especially the negative influence of L1, which needs to be attended.

On Admissible Consensuses

Consensus-finding is a ubiquitous paradigm in both A.I. and everyday life. Recently, a computational approach to consensus-finding has been introduced in the standard clausal Boolean logic framework. It captures non-contradictory fragments of the information conveyed by different agents such that these fragments do not logically conflict with any of the agents. This kind of consensuses concerns open-minded agents: On one side, these agents accept to drop their own information that contradicts other agents. On the other side, they can accept any information from another agent provided that this latter information does not contradict them. In this study, it is shown that a family of consensuses might not actually be endorsed by these open-minded agents, due to the usual ad-hoc logical representation of knowledge and well-known paradoxes linked to material implication. Accordingly, we trim the set of consensuses by filtering out these undesirable consensuses and keep so-called admissible consensuses, only. A computational approach that is based on the SAT technologies and that delivers one maximal admissible consensus is then investigated.

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types

Proceedings of the First Workshop on Coalgebra, Horn Clause Logic Programming and Types