Parallelisable Existential Rules: a Story of Pieces

In this paper, we consider existential rules, an expressive formalism well adapted to the representation of ontological knowledge, as well as data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.

Compositionality of Rewriting Rules with Conditions

We extend the notion of compositional associative rewriting as recently studied in the rule algebra framework literature to the setting of rewriting rules with conditions. Our methodology is category-theoretical in nature, where the definition of rule composition operations encodes the non-deterministic sequential concurrent application of rules in Double-Pushout (DPO) and Sesqui-Pushout (SqPO) rewriting with application conditions based upon M-adhesive categories. We uncover an intricate interplay between the category-theoretical concepts of conditions on rules and morphisms, the compositionality and compatibility of certain shift and transport constructions for conditions, and thirdly the property of associativity of the composition of rules.

The Application of Mamdani Method for Predicting The Best Portable Computer Based on Hardware and Price

A portable computer is a technology tool widely used among students and students. With a very helpful role as in typing needs, presentations, and math calculations. A variety of carry-on computers that many certainly make one difficult to determine a decent and good portable computer to use. In general, in the process of selecting a portable computer, there is no recognized standard to determine the recommended portable computer level. The purpose in this research is to produce predictive values that will be a reference in supporting decisions in determining a portable computer that complies with hardware component criteria and pricing. This study implemented FIS Mamdani models with the analysis stage of the formation of fuzzy sets, application of implications function, rule composition and defuzification. The result of this research is an output of predictive value based on hardware component inputs and prices that will assist the user in supporting decisions in determining the best carry-on computer and according to what they want.Keywords: Predictions, Fuzzy Inference System, Mamdani methods, portable computers, students.

Sistem Pakar Diagnosa Hipertiroid Menggunakan Certainty Factor dan Logika Fuzzy

Hyperthyroidism is a condition when the function of thyroid gland becomes excessive. The excess function of thyroid gland increases thyroid hormone production which affect body metabolism and physiological activity. This study aims to make an expert system diagnose hyperthyroidism with certainty factor and fuzzy logic. The stages of the process of diagnosing hyperthyroidism including problem identification, needs analysis of symptoms and types of hyperthyroidism, determination of rules, system design, case examples implementation, system testing, and evaluation. Variables used were systolic blood pressure, triiodothyronine (T3) levels, thyroxine (T4) levels, thyroid stimulating hormones (TSH) levels, goiter, tremors, and excessive sweating. All variables are processed using fuzzy logic with fuzzyfication stages, rule determination, min implications, max rule composition, and defuzzyfication which then proceed with certainty factor with sequential CF and CF stages. The system output is diagnosis the condition of hyperthyroidism such as hyperthyroidism, subclinical hyperthyroidism, and normal accompanied by a certainty factor. Based on the evaluation result, the accuracy of the expert system according to expert diagnostics is 86.7%

Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation

Background Deidentification of clinical records is a critical step before their publication. This is usually treated as a type of sequence labeling task, and ensemble learning is one of the best performing solutions. Under the framework of multi-learner ensemble, the significance of a candidate rule-based learner remains an open issue. Objective The aim of this study is to investigate whether a rule-based learner is useful in a hybrid deidentification system and offer suggestions on how to build and integrate a rule-based learner. Methods We chose a data-driven rule-learner named transformation-based error-driven learning (TBED) and integrated it into the best performing hybrid system in this task. Results On the popular Informatics for Integrating Biology and the Bedside (i2b2) deidentification data set, experiments showed that TBED can offer high performance with its generated rules, and integrating the rule-based model into an ensemble framework, which reached an F1 score of 96.76%, achieved the best performance reported in the community. Conclusions We proved the rule-based method offers an effective contribution to the current ensemble learning approach for the deidentification of clinical records. Such a rule system could be automatically learned by TBED, avoiding the high cost and low reliability of manual rule composition. In particular, we boosted the ensemble model with rules to create the best performance of the deidentification of clinical records.

Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation (Preprint)

BACKGROUND Deidentification of clinical records is a critical step before their publication. This is usually treated as a type of sequence labeling task, and ensemble learning is one of the best performing solutions. Under the framework of multi-learner ensemble, the significance of a candidate rule-based learner remains an open issue. OBJECTIVE The aim of this study is to investigate whether a rule-based learner is useful in a hybrid deidentification system and offer suggestions on how to build and integrate a rule-based learner. METHODS We chose a data-driven rule-learner named transformation-based error-driven learning (TBED) and integrated it into the best performing hybrid system in this task. RESULTS On the popular Informatics for Integrating Biology and the Bedside (i2b2) deidentification data set, experiments showed that TBED can offer high performance with its generated rules, and integrating the rule-based model into an ensemble framework, which reached an F1 score of 96.76%, achieved the best performance reported in the community. CONCLUSIONS We proved the rule-based method offers an effective contribution to the current ensemble learning approach for the deidentification of clinical records. Such a rule system could be automatically learned by TBED, avoiding the high cost and low reliability of manual rule composition. In particular, we boosted the ensemble model with rules to create the best performance of the deidentification of clinical records.

PENINGKATKAN KESELAMATAN PELAYARAN DI KOTA BATAM MENGGUNAKAN LOGIKA FUZZY

At present, many accidents occur on shipping, whether from human negligence or from disasters that occur without human knowledge. Then a rule is made before sailing the parties concerned must check everything related to shipping to avoid accidents. Batam is an archipelago that is connected between many islands, so that sea transportation is the main budget in crossing to various islands. Nowadays there are many accidents such as ships sinking, ships colliding and ships burning, this is due to human negligence. The main objective of this research is to improve shipping safety in Batam City, by observing the waves and engine checks when the ship is about to depart the crew must pay attention to this in order to avoid accidents on the cruise that will be used. So that the passenger safety of the ship is guaranteed. In this study using the mamdani method. The Mamdani method looks for the smallest value to the greatest value. In the Fuzzy method there are three operators, OR, AND and NOT operators. This study uses the OR operator. The steps of this research work, 1. Determine the input variable 2, determine the fuzzy set, 3, rule composition, 4, the final result is in the form of defuzification. The results of this study are in the form of a decision-making system in determining the level of maritime shipments and how to prevent them from occurring accidents in the shipping system by taking into account the input variables that have been set such as safety devices, paying attention to waves and always checking ship engines regularly. So that accidents can be avoided. So that the safety of shipping in the city of Batam is more guaranteed.

Valence-changing morphology via lexical rule composition

I describe an analysis of valence-changing verbal morphology implemented as a library extending the LinGO Grammar Matrix customization system. This analysis is based on decomposition of these operations into rule components, which in turn are expressed as lexical rule supertypes that implement specific, isolatable constraints. I also show how common variations of these constraints can be abstracted and parameterized by their axes of variation. I then demonstrate how these constraints can be recomposed in various combinations to provide broad coverage of the typological variation of valence change found in the world’s languages. I evaluate the coverage of this library on five held-out world languages that exhibit these phenomena, achieving 79% coverage and 2% overgeneration.