Automatic Rule Extraction Research Articles

A manually annotated corpus in French for the study of urbanization and the natural risk prevention

Land artificialization is a serious problem of civilization. Urban planning and natural risk management are aimed to improve it. In France, these practices operate the Local Land Plans (PLU – Plan Local d’Urbanisme) and the Natural risk prevention plans (PPRn – Plan de Prévention des Risques naturels) containing land use rules. To facilitate automatic extraction of the rules, we manually annotated a number of those documents concerning Montpellier, a rapidly evolving agglomeration exposed to natural risks. We defined a format for labeled examples in which each entry includes title and subtitle. In addition, we proposed a hierarchical representation of class labels to generalize the use of our corpus. Our corpus, consisting of 1934 textual segments, each of which labeled by one of the 4 classes (Verifiable, Non-verifiable, Informative and Not pertinent) is the first corpus in the French language in the fields of urban planning and natural risk management. Along with presenting the corpus, we tested a state-of-the-art approach for text classification to demonstrate its usability for automatic rule extraction.

REUSE OF INFORMATION BASED ON THE INTERPRETATION OF KNOWLEDGE

Recently, much attention has been paid to the creation of knowledge bases that contain millions of facts about various objects of the real world. One of the key aspects of knowledge management is the reuse of previously acquired knowledge. The subject of research is the processes of knowledge reuse and the creation of software systems based on knowledge bases. Knowledge interpretation is one approach to knowledge reuse, which consists in deriving new knowledge based on existing facts in the knowledge base. The purpose of the study is to increase the efficiency of knowledge reuse in software systems based on knowledge bases due to automatic rule extraction. To achieve the goal, the following tasks were solved: approaches to structuring the facts available in the database were considered, a qualitative analysis of the possibility of applying automatic methods of rule construction and derivation was carried out. The task of predicting the connection between a pair of entities, which determines the presence of a relationship for facts, is considered. A generalized approach to the presentation of facts is proposed, which allows the use of efficient rule-finding algorithms. The following methods are used to solve the given problem: the algebra of finite predicates and predicate operations for knowledge representation, methods for predicting the connection between a pair of entities based on representative learning for automatically obtaining rules. The following results were obtained: an approach to rule formation was considered, which allows structuring existing facts as a set of binary predicates and applying automatic methods of rule construction and derivation. It is concluded that the limitation of knowledge reuse is the structure of the knowledge base and the software used to support it. The article formulates the principles of building specific concentrator predicates for the representation of attributes, which allows generalizing the predicate representation of facts and applying automatic methods of rule extraction, which increases the efficiency of knowledge reuse. Conclusions: the application of the method and mechanism of identification based on predicate operations and specific predicates, which automatically extracts attributes from the knowledge base, together with the quality assessment of the derived rules, made it possible to propose a generalized approach for presenting facts and use effective rule search algorithms, which allows to increase the efficiency of reuse knowledge in software systems.

EnviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways.

Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions. We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule's strengths for both automatic extraction of optimized reactions rules and automated updating thereof. enviRule code is freely available at https://github.com/zhangky12/enviRule.

A deep learning-based CEP rule extraction framework for IoT data

With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined rules mostly created by experts. However, especially in heterogeneous IoT environments, it may become extremely hard to define these rules manually due to complex and dynamic nature of data. Defining rules requires accurate knowledge of relevant events by utilizing temporal dependencies and relations among attributes of events. Therefore, there is a need for an automatic rule extraction system which is capable of generating rules automatically from unlabeled IoT data. This paper proposes a generalized framework for automatic CEP rule extraction with the help of deep learning (DL) methods. The proposed framework contains two main phases which are data labeling and automatic rule extraction phases. We compare several DL methods with each other and regression-based methods to evaluate the proposed framework. In addition, several rule mining methods are employed in the automatic rule extraction phase in a comparative manner. The reconstruction error and prediction success rate of our framework are evaluated using an air quality dataset collected from a smart city application. The results indicate that the proposed framework is able to generate meaningful and accurate rules for unlabeled IoT data.

A Novel Structure of Highly Interpretable Fuzzy Rules Extraction

Introduction: Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.Material and Methods: In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.Results: The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.Conclusion: The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.

Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams

Discovery, specification and proper representation of various aspects of business knowledge plays crucial part in model-driven information systems engineering, especially when it comes to the early stages of systems development. Being among the most applicable and advanced features of model-driven development, model transformation could help improving one of the most time- and resource-consuming efforts in this process, namely, discovery and specification of business vocabularies and business rules within the problem domain. One of our latest developments in this area was the solution for the automatic extraction of SBVR business vocabularies and business rules from UML use case diagrams, which was arguably one of the most comprehensive developments of this kind currently available in public. In this paper, we present an enhancement to our previous development by introducing a novel natural language processing component to it. This enhancement provides more advanced extraction capabilities (such as recognition of entities, entire noun and verb phrases, multinary associations) and better quality of the extraction results compared to our previous solution. The main contributions presented in this paper are pre- and post-processing algorithms, and two extraction algorithms using custom-trained POS tagger. Based on the related work findings, it is safe to state that the presented solution is novel and original in its approach of combining together M2M transformation of UML and SBVR models with natural language processing techniques in the field of model-driven information systems engineering.

Automatic Extraction of Engineering Rules From Unstructured Text: A Natural Language Processing Approach

Abstract Manufacturers use cloud manufacturing platforms to offer their services. The literature has suggested a semantic web-based cloud manufacturing framework, in which engineering knowledge is modeled using structured syntax. Translating engineering rules to semantic rules by human is a painstaking task and prone to mistakes. We present a scheme that treats converting engineering knowledge into semantic rules as a machine translation task and uses neural machine translation techniques to carry out the conversion.

Automatic mapping of atoms across both simple and complex chemical reactions

Mapping atoms across chemical reactions is important for substructure searches, automatic extraction of reaction rules, identification of metabolic pathways, and more. Unfortunately, the existing mapping algorithms can deal adequately only with relatively simple reactions but not those in which expert chemists would benefit from computer’s help. Here we report how a combination of algorithmics and expert chemical knowledge significantly improves the performance of atom mapping, allowing the machine to deal with even the most mechanistically complex chemical and biochemical transformations. The key feature of our approach is the use of few but judiciously chosen reaction templates that are used to generate plausible “intermediate” atom assignments which then guide a graph-theoretical algorithm towards the chemically correct isomorphic mappings. The algorithm performs significantly better than the available state-of-the-art reaction mappers, suggesting its uses in database curation, mechanism assignments, and – above all – machine extraction of reaction rules underlying modern synthesis-planning programs.

Interpretable Machine Learning for Mobile Notification Management

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, for this reason, they are considered disruptive and potentially annoying, as well. PrefMiner is a novel interruptibility management solution that learns users' preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. PrefMiner aims at being intelligible and interpretable for users, i.e., not just a "black box" solution, by suggesting rules to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.

Granular Computing Models and Methods Based on the Spatial Granulation

Automatic data processing and extraction of rules from large datasets has gained considerable interest during the last years. Several approaches have been proposed, mainly based both statistical and fuzzy sets approaches. In this paper, we propose a new view to the approaches to represent the large datasets and to find rules in one. That makes use of so-called Information Granulation and Computing with Words methods. The basic Ideas and principles of Granular Computing have been studied explicitly or implicitly in many fields in isolation. With the recently renewed and fast growing interest, it is time to extract the commonality from a diversity of fields and to study systematically and formally the domain independent principles of Granular Computing in a unified model. A framework of granular computing can be improved by applying the inherited principles of Space Granulation and Computing with Shapes. In this paper, we examine a framework from new perspectives of granular computing, based on Space Granulation and Computing with Shapes for the granulated data processing and problem solving.

A three-way approach for learning rules in automatic knowledge-based topic models

Topic modeling aims to uncover hidden thematic structures in a collection of documents by representing them as a set of topics. Automatic knowledge-based topic models are recently introduced to meet the demands of processing large-scale text collections. They are based on automatic extraction of rules from multiple domain corpuses. Generally, the extracted rules are large in number and some thresholds are used to select only a small number of useful rules. There are two shortcomings in this for selecting important rules. Firstly, they are based on fixed thresholds for extracting rules from all domain corpuses. Secondly, the thresholds are predefined or explicitly set by expert opinions and are not based on automated mechanisms. In this article, we address these shortcomings by considering a three-way approach based on rules having strong positive associations, rules having strong negative associations and rules having weak associations. A pair of thresholds defines and controls the three-way partitioning of the rules. It is argued that the domain specific and automated selection of thresholds in the three-way framework may be approached from the viewpoint of a tradeoff between the quantity of rules and the quality of rules. We apply the game-theoretic rough set (GTRS) model to implement this tradeoff. Algorithms using the GTRS are introduced for automatically determining the thresholds. Experimental results on Chen2014 dataset suggest an average improvement of 52.82 points in topic coherence by increasing the quantity of rules to 17.93%.

An automatic rules extraction approach to support OSA events detection in an mHealth system.

Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.

An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems

Supervisory Control and Data Acquisition (SCADA) systems are a core part of industrial systems, such as smart grid power and water distribution systems. In recent years, such systems become highly vulnerable to cyber attacks. The design of efficient and accurate data-driven anomaly detection models become an important topic of interest relating to the development of SCADA-specific Intrusion Detection Systems (IDSs) to counter cyber attacks. This paper proposes two novel techniques: (i) an automatic identification of consistent and inconsistent states of SCADA data for any given system, and (ii) an automatic extraction of proximity detection rules from identified states. During the identification phase, the density factor for the k-nearest neighbours of an observation is adapted to compute its inconsistency score. Then, an optimal inconsistency threshold is calculated to separate inconsistent from consistent observations. During the extraction phase, the well-known fixed-width clustering technique is extended to extract proximity-detection rules, which forms a small and most-representative data set for both inconsistent and consistent behaviours in the training data set. Extensive experiments were carried out both on real as well as simulated data sets, and we show that the proposed techniques provide significant accuracy and efficiency in detecting cyber attacks, compared to three well-known anomaly detection approaches.

A Multi-intelligent Agent Architecture for Knowledge Extraction: Novel Approaches for Automatic Production Rules Extraction

In this paper, multi-intelligent agent architecture has been proposed for automatic knowledge extraction from its resources (domain experts and text documents). The extracted knowledge should be stored in a knowledge base to be used later by knowledge-based systems. This article aims to produce an effective knowledge base by cooperation between expert mining and text mining techniques. Firstly, we are constructing an Expert Mining Intelligent Agent (EMIA) able to interview with domain experts for mining problem solving knowledge as production rules in a specific diagnosis domain. It is also responsible for extracting the patterns or linguistic expressions and save it in a conceptual database. Secondly, we are constructing a Text Mining Intelligent Agent (TMIA) capable of extracting production rules from a text document corpus. The achievement of that extraction can be performed by a text document categorization based on a traditional term weighting scheme (TF-IDF) and using the Stanford parser to analyze and produce a parsing tree for each sentence in that document. Then, the TMIA looks for all causal words and takes them as separation words to generate patterns and sub-patterns based on the conceptual database. Finally, the TMIA stores those patterns and sub-patterns in a pre-formatted template and displays it to a domain expert for a modification process to construct accurate production rule.

An in-depth analysis on traffic flooding attacks detection and system using data mining techniques

Recently, as network traffic flooding attack such as DoS and DDoS have posed devastating threats on network services, rapid detection, and semantic analysis are the major concern for secure and reliable network services. In addition, in a recent issue of the safety and comfort of vehicles and communication technologies for service is required. We propose a traffic flooding attack detection and an in-depth analysis system that uses data mining techniques. In this paper we (1) designed and implemented a system that detects traffic flooding attacks. Then, it executes classification by attack type and it uses SNMP MIB information based on C4.5 algorithm; (2) conducted a semantic interpretation that extracts and analyzes the rules of execution mechanism that are additionally provided by C4.5; (3) performed an in-depth analysis on the attack patterns and useful knowledge inherent in their data by type, utilizing association rule mining. Classification by attack and attack type based on C4.5 and association rules, automatic rule extraction and semantic in-depth interpretation, which are proposed in this paper, provide a positive possibility to add momentum towards the development of new methodologies for intrusion detection systems as well as to support establishing policies for intrusion detection and response systems.

Differential Evolution for automatic rule extraction from medical databases

In this paper, a new approach based on Differential Evolution (DE) for the automatic classification of items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically extracts explicit knowledge from the database under the form of IF-THEN rules containing AND-connected clauses on the database variables. Each DE individual codes for a set of rules. For each class more than one rule can be contained in the individual, and these rules can be seen as logically connected in OR. Furthermore, all the classifying rules for all the classes are found all at once in one step. DEREx is thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The major contribution of this paper is that DEREx is the first classification tool in literature that is based on DE and automatically extracts sets of IF-THEN rules without the intervention of any other mechanism. In fact, all other classification tools based on DE existing in literature either simply find centroids for the classes rather than extracting rules, or are hybrid systems in which DE simply optimizes some parameters whereas the classification capabilities are provided by other mechanisms. For the experiments eight databases from the medical domain have been considered. First, among ten classical DE variants, the most effective of them in terms of highest classification accuracy in a ten-fold cross-validation has been found. Secondly, the tool has been compared over the same eight databases against a set of fifteen classifiers widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be the best performing tool in terms of highest classification accuracy. Also statistical analysis has confirmed that DEREx is the best classifier. When compared to the other rule-based classification tools here used, DEREx needs the lowest average number of rules to face a problem, and the average number of clauses per rule is not very high. In conclusion, the tool here presented is preferable to the other classifiers because it shows good classification accuracy, automatically extracts knowledge, and provides users with it under an easily comprehensible form.

Rule extraction and application in dynamic tone sequences

Event Abstract Back to Event Rule extraction and application in dynamic tone sequences Alexandra Bendixen1* and E. Schröger1 1 University of Leipzig, Germany Many events in the auditory environment occur in a predictable manner, such as the sound of a train passing by, which gradually changes in location and intensity. We investigated how the auditory system extracts such predictable event behavior. Predictability was simulated by rules embedded in tone sequences. We developed a dynamic experimental protocol to create an environment in which rules constantly emerge and vanish. Participants were presented with continuous series of tones containing regular and irregular sequences in unpredictable succession. In three experiments, we incorporated rules of increasing complexity into this protocol. In successive conditions, participants were instructed to a) ignore the tones (passive paradigm), b) perform a task related to the tones but not to the rules (distraction paradigm), and c) attend the rules and respond to rule violations (detection paradigm). These different tasks provide an insight into the consequences of rule extraction on attentional orienting and behavior. Participants’ behavioral and electrophysiological responses to rule-conforming and rule-violating events in the sequences were recorded and analyzed depending on the length of the preceding regular sequence. Processing modulations as a function of the rule-conformance of an event indicate the detection of the underlying rule. In order to reveal the cortical areas involved, the obtained event-related potentials (MMN, N2b, P3a, P3b, RON) were analyzed topographically (SCD) and tomographically (VARETA). Results reveal that rules were extracted equally fast whether participants did or did not pay attention to the tones and rules. This underlines the automatic character of the involved brain mechanism. The rapidity of rule extraction was influenced by rule complexity, indicating a flexible adaptation of the auditory system to the plausibility of each rule. Furthermore, the automatic detection of rule-violating events elicited involuntary attentional orienting. Depending on the current task, this facilitated the intentional detection of deviations (detection paradigm) or caused interference with other mental processes to enable a relevance check of the unexpected event (distraction paradigm). The immediate application of just emerging rules illustrates that automatic rule extraction in the auditory system has an important functional role for the adaptive behavior of the organism. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Turkey, 1 Sep - 5 Sep, 2008. Presentation Type: Oral Presentation Topic: Change Detection Citation: Bendixen A and Schröger E (2008). Rule extraction and application in dynamic tone sequences. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.154 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 05 Dec 2008; Published Online: 05 Dec 2008. * Correspondence: Alexandra Bendixen, University of Leipzig, Leipzig, Germany, bendixen@psychologie.uni-leipzig.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alexandra Bendixen E. Schröger Google Alexandra Bendixen E. Schröger Google Scholar Alexandra Bendixen E. Schröger PubMed Alexandra Bendixen E. Schröger Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A knowledge discovery method

The paper concentrates on one direction of Knowledge Discovery in Data Bases and Data Mining, known as automatic rule extraction from data sets.The rule extraction algorithm has been implemented in Dyalog APL. The paper presents APL functions and tests made on artificial data sets.The real life problem solution is described as an application of implemented APL code. The problem is to find automatically rules to describe the corrosion rate of steel in sodium as a function of alloy additions. The input data are experimental data of corrosion rate measured for different steel samples. The output is a set of IF-THEN rules, which describe the dependence of corrosion rate on alloy additions. An expert in corrosion has validated the rules and an example of real "discovery" has been mentioned.

Automatic Extraction of Rules for AnaphoraResolution of Japanese Zero Pronouns in Japanese–English Machine Translation from Aligned Sentence Pairs

This paper proposes a method to extract rules for the anaphora resolution of Japanese zero pronouns in Japanese–English MT from aligned sentence pairs. After aligned sentence pairs unsuitable for rule extraction because of analysis errors or free translations are automatically rejected, zero pronouns in the Japanese sentences and the English translation equivalents of their antecedents are extracted from the remaining Japanese and English aligned sentence pairs using ten hand-developed alignment rules. This method identifies all Japanese zero pronouns whose translation equivalents are not explicitly expressed in an English sentence, this method identifies these as unalignable. Then, resolution rules for the remaining zero pronouns are automatically extracted using the aligned pairs, equivalent word/phrase pairs extracted from the aligned sentence pairs, and the syntactic and semantic structures of the Japanese sentences. This method was implemented in a Japanese–English MT system, ALT-J/E. 98.4% of all pairs were automatically aligned correctly in a window test, and 94.0% in a blind test. Furthermore, extracted rules for zero pronouns with deictic references created automatically from sentence pairs correctly resolved 99.0% of the zero pronouns in a window test and 85.0% of the zero pronouns in a blind test.

Grammatical Inference using an Adaptive Recurrent Neural Network

In this study, we proposed an adaptive recurrent neural network that is capable of inferring a regular grammar, and at the same time of extracting the underlying grammatical rules emulated by a finite-state automata. Our proposed network adapts from an initial analog phase, which has good training behavior, to a discrete phase for automatic rule extraction. A modified objective function is proposed to accomplish the discretisation process as well as logic learning. Comparison on learning Tomita grammars shows that our network has a significant advantage over other approaches.