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Submission note: A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Engineering and Mathematical Science, College of Science, Health and Engineering, La Trobe University, Bundoora.

Due to the capability to understand the intent of the user’s queries and even complex questions, semantic search returns results that are more precise to compare with traditional, keyword based search systems. The solution, created in this work, allows questioning ontologies in natural language. The main advantages of the solution are capability to question ontologies in different languages and question complex ontologies, when the structure of ontology and the formulation of question do not correspond directly., Semantinės paieškos sistemos ypatingos tuo, kad sugeba suprasti naudotojo pateiktas užklausas bei sudėtingus klausimus. Todėl, palyginti su tradicinėmis, raktiniais žodžiais grindžiamomis sistemomis, jos grąžina tikslesnius rezultatus. Šiame darbe sukurtas sprendimas leidžia vykdyti semantinę paiešką pateikiant klausimus natūralia kalba. Pagrindiniai sukurto metodo privalumai yra galimybė pateikti klausimus skirtingomis kalbomis ir vykdyti užklausas ontologijose, kurių struktūra sudėtinga ir tiesiogiai neatitinka klausimų formuluočių.

The annual International Joint Conference on Rules and Reasoning (RuleML+RR) is an international conference on research, applications, languages and standards for rule technologies, rule-based programming and rule-based systems including production rules systems, logic programming rule engines, as well as business-rule engines and management systems; Semantic Web rule languages and rule standards, including RuleML (e.g., Datalog+ RuleML, Reaction RuleML and LegalRuleML), SWRL, RIF, Common Logic, PRR, decision rules and Decision Model and Notation (DMN), as well as Semantics of Business Vocabulary and Business Rules (SBVR); rule-based Event Processing Languages (EPLs) and technologies; and foundational research on inference rules, transformation rules, decision rules, production rules, and Event-Condition-Action (ECA) rules. In 2017, RuleML+RR joined the efforts of two wellestablished conference series: the International Web Rule Symposia (RuleML) and the Web Reasoning and Rule Systems (RR) conferences, and it is now the leading conference to build bridges between academia and industry in the field of Web rules and its applications, especially as part of the Semantic Technology stack. RuleML+RR is commonly listed together with and related to other major high-impact Artificial Intelligence conferences worldwide, starting with IJCAI in 2011 and 2016, ECAI in 2012, AAAI in 2013, ECAI in 2014, the AI Summit London in 2017, and GCAI in 2018 and 2019. The RuleML symposia and RR conferences have been held since 2002 and 2007, respectively. The RR conferences have been a forum for discussion and dissemination of new results on Web reasoning and rule systems, with an emphasis on rule-based approaches and languages. The RuleML symposia have been devoted to disseminating research, applications, languages, and standards for rule technologies, with attention to both theoretical and practical developments, to challenging new ideas, and to industrial applications. Building on the tradition of both, RuleML and RR, the joint conference series RuleML+RR aims at bridging academia and industry in the field of rules, and at fostering the cross-fertilization between the different communities focused on the research, development, and applications of rule-based systems. RuleML+RR aims at being the leading conference series for all subjects concerning theoretical advances, novel technologies, and innovative applications about knowledge representation and reasoning with rules.

This research has two interrelated aims. The first aim of the research is to present the relationship between the Speed Based Verbal Reasoning (SBVR) subtest, which calculates reasoning speed by adjusting the Anadolu-Sak Intelligence Scale (ASIS) Verbal Reasoning (VR) subtest, and the CAS 2 intelligence scale and RIAS-2 intelligence scale speed subtests. The second aim of the research is to examine the reliability of the SBVR subtest, the item-total correlations and its relationship to the ASIS index scores. The research employed a correlational survey model and the study sample consisted of 211 students aged 6 to 7. The reliability of the SBVR subtest was found to be high (α =.84). However, we found that there is a small level relationship between SBVR and speed variables (rHDSAM-SPS =-.16, rHDSAM-PNM =.18, rHDSAM-ND =.21 and rHDSAM-SNT =.20), a moderate level (r =.35) relationship with the memory capacity index, and a moderate level (r=.31) and significant relationship with the nonverbal potential index. The research demonstrates the discrepancy between the reasoning speed and the perceptual speed., Bu çalışma birbiri ile bağlantılı iki amaca hizmet etmektedir. Araştırmanın ilk amacı, Anadolu-Sak Zekâ Ölçeği (ASİS) Sözel Analojik Muhakeme (SAM) alt testi puanlama yönteminin değiştirilmesiyle oluşturulan ve muhakeme hızını ölçen HDSAM alt testi ile CAS 2 zekâ ölçeği ve RIAS-2 zekâ ölçeğinin algı hızını ölçen alt testleri arasındaki ilişkiyi ortaya koymaktır. Araştırmanın ikinci amacı, HDSAM alt testinin güvenirliğini, madde-toplam korelasyonunu ve ASİS endeks puanlarıyla olan ilişkisini incelemektir. Araştırma ilişkisel tarama modeliyle desenlenmiş ve araştırmanın örneklemini 6 ile 7 yaş arasındaki 211 öğrenci oluşturmuştur. Araştırma bulgularına göre HDSAM alt testinin güvenirliğinin yüksek (α=.84) olduğu görülmüştür. Diğer taraftan HDSAM ile hız değişkenleri arasında küçük (rHDSAM-SPS=-.16, rHDSAM-PNM=.18, rHDSAM-ND=.21 ve rHDSAM-SNT=.20), bellek kapasitesi endeksi ile orta (r=.35), görsel potansiyel endeksi ile orta (r=.31) düzeyde anlamlı ilişki olduğu bulunmuştur. Sonuç olarak araştırma, muhakeme hızıyla algı hızının farklılaştığını literatürdeki korelasyon araştırmaları çerçevesinde desteklemektedir.

In the early phases of the software development process, specifications are mostly written in a natural language rather than formal models, which is not supported by the Model Driven Architecture (MDA). For this reason, the Semantic of Business Vocabulary and Rules (SBVR) is proposed by the Object Management Group to represent the textual specifications in a language comprehensible by both of humans and machines, to facilitate its integration in the MDA lifecycle. However, businesspeople are usually not familiar with SBVR standard. In this paper we present an approach to automatically transform textual business rules to an SBVR model, to facilitate its integration in nowadays information technology infrastructures. Our approach is distinguished from existing works in that it uses an in-depth Natural Language Processing to extract a more comprehensible SBVR model that includes the semantic formulation of each business rule statement, coupled with a Terminological Dictionary of extracted concepts, to which we have added further specifications such as definitions and synonyms. The evaluation of our approach shows that for three sets of business rules statements taken from different domains, we could generate the correct meaning with an average of F1-score exceeding 87%.

Traditional methods lack the necessary or appropriate means for expressing the integrity constraints during the database conceptual modeling stage. At most, integrity constraints are informally documented and then, coded in the application. This leads to late error detection and database inconsistencies due to the incapacity of the domain expert to validate the program code. Thus, it is necessary to express such constraints in a natural and formal way in order to close the gap between modelers and domain experts, and to support the transformation to other languages and models. As a result, we propose a controlled natural language based on Semantics of Business Vocabulary and Business Rules (SBVR) to help modelers and domain experts in the process of writing and validating the constraints that cannot be represented in an Entity-Relationship schema; and the Alloy language to allow a formal specification. Inaddition, all the correspondences between the models and languages are described in order to consistently express the constraints and to lay the foundations of the automatic transformation. Finally, a case study and a usability survey show that the proposal is feasible, without abandoning a traditional and popular approach such as the Entity-Relationship model.

A user's software requirements are represented in natural language or a speech such as English. Translating these requirements into the object-oriented models is a tough process for designers. This article proposes a neoteric approach to generate Unified Modeling Language (UML) class models instantly from software requirement specifications (SRS). Here the authors make use of the Open Natural language processing tool (OpenNLP) for lexical analysis and to generate the necessary parts of speech (POS) tags from these requirement specifications. Then, the Semantics of Business Vocabulary and Rules (SBVR) standard is used to extract the object-oriented elements from the natural language (NL) processed SRS. From this, the authors generate UML class models. The prototype tool can generate accurate models in less time. This automated system for designing object-oriented models from SRS reduces the cost and budget for both the designers and the users.

In the domain of model-driven system engineering, model-to-model (M2M) transformations present a very relevant topic because they may provide much-needed automation capabilities to the whole CASE-supported system development life cycle. Nonetheless, it is observed that throughout the whole development process M2M transformations are spread unevenly, in this respect, the phases of Business Modeling and System Analysis are arguably the most underdeveloped ones. The main novelty and contributions of this paper are the presented set of model-based transformations for extracting well-structured SBVR business vocabularies from visual UML use case models, which utilizes M2M transformation technology based on the so-called drag-and-drop actions. The conducted experiments show that this new development provides the same transformation power while introducing more flexibility to the model development process as compared to our previously developed approach for (semi-)automatic extraction of SBVR business vocabularies from UML use case models.

Semantics of Business Vocabulary and Business Rule (SBVR) were introduced to describe the business process in most formal way. SBVR specify business rules. Semantics of Business Vocabulary and Business Rules is introduced by standard of Object Management Group (OMG) in 2008. Complex business rules are formally defined by Semantics of Business Vocabulary and Business Rules (SBVR). This paper provides a novel approach for translating SBVR specification of software requirements into XML schema. The purpose of this paper is to generate XML from SBVR instead of NL natural language specification because due to informal nature of natural language the generation of XML form NL will be resulted in lesser accuracy. SBVR Bridge the gap between humans and machines as human can understand simple natural language sentences while this natural language has ambiguous nature for machine and IT specialists. The VeTIS tool is used for the transformation purpose. SBVR rules generated as first output and these rules gave as input to transaction editor that extract SBVR vocabulary such as noun concept, fact type etc. In the last step these SBVR elements are replaced by elements that are called tags of XML vocabulary.

Architecting online service applications involves the challenging coordination task of ensuring the collaboration achieves the overall business goal while adhering to the global constraints agreed between the interacting participants. We propose a declarative approach for specifying the underlying service interactions and generating the service choreography required for successful execution. The proposed approach builds on the Semantics of Business Vocabulary and Business Rules (SBVR), an OMG standard for specifying business models in structured English. Our approach can be used to verify i) whether a specific request can be realised in the given choreography, ii) the consistency of the business rules, and iii) domain-specific static constraints in the generated choreography. We describe a mapping from an SBVR model to a relational constraint solver, namely the Alloy Analyzer. The transformation of SBVR into Alloy allows to automatically generate the corresponding service choreography, hence producing an immediate instance of execution that satisfies the constraints of the specification.Architecting online service applications involves the challenging coordination task of ensuring the collaboration achieves the overall business goal while adhering to the global constraints agreed between the interacting participants. We propose a declarative approach for specifying the underlying service interactions and generating the service choreography required for successful execution. The proposed approach builds on the Semantics of Business Vocabulary and Business Rules (SBVR), an OMG standard for specifying business models in structured English. Our approach can be used to verify i) whether a specific request can be realised in the given choreography, ii) the consistency of the business rules, and iii) domain-specific static constraints in the generated choreography. We describe a mapping from an SBVR model to a relational constraint solver, namely the Alloy Analyzer. The transformation of SBVR into Alloy allows to automatically generate the corresponding service choreography, hence producing an ...

Business Rules exists at the core of any Business Organization. For efficient execution of the business system, all the business rules must be in machine-interpretable format. There is an absence of such a system that can convert the business rule sentences into corresponding structured format automatically. We present BRMiner, a system which automatically converts business rules represented as Natural Language sentences to the corresponding SBVR format which is a structured representation that can be further converted to the machine-interpretable format. BRMiner is based on the idea of Open Information Extraction (OIE). We have shown that existing OIE systems are not suitable for SBVR rule formation that leads to the development of a new OIE system BRMiner, with more accurate prediction and additional capabilities. BRMiner uses the state of the art dependency parser to convert an unstructured business rule to the corresponding structured format. We have used internal as well as publically available datasets for our system evaluation and the results are encouraging which we have shown in the paper.

An inconsistent and ambiguous Software Requirement Specification (SRS) document results in an erroneous/failed software project. Hence, it is a serious challenge to handle and process complex and ambiguous requirements. Most of the literature work focuses on detection and resolution of ambiguity in software requirements. Also, there is no standardized way to write unambiguous and consistent requirements. The goal of this research was to generate an ambiguity-less SRS document. This paper presents a new approach to write ambiguity-less requirements. Furthermore, we design a framework for Natural Language (NL) to Controlled Natural Language (CNL) (such as Semantic Business Vocabulary and Rules (SBVR)) transition and develop a prototype. The prototype also generates Resource Description Framework (RDF) representation. The SBVR has a shared meaning concept that minimizes ambiguity, and RDF representation is supported by query language such as SPARQL Protocol and RDF Query Language (SPARQL). The proposed approach can help software engineers to translate NL requirements into a format that is understandable by all stakeholders and also is machine processable. The results of our prototype are encouraging, exhibiting the efficient performance of our developed prototype in terms of usability and correctness.

Service choreography describes the interaction across different participating services capturing the ordering constraints of global message exchanges. Semantics of Business Vocabulary and Rules (SBVR) model, an Object-Oriented Management (OMG) standard is proposed to specify services interaction (service choreographies), The specification focuses on a deontic rule expressing both prohibition and obligation which can be progressively helpful in working with coordinating service interactions. Then Alloy Analyzer, a relational constraint solver, is used to transform a developed service choreographies, an SBVR model into an Alloy model. This transformation allows to generate and to verify, the choreography for the developed SBVR model and the realisability of the generated choreography, automnatically.

In this paper, we describe the method for expressing the semantics of data metamodels using a concept system. The method abstract from metamodels’ syntax and deems to enable the modeler to compare different data metamodels, and express semantics-aware translations between different metamodels. The method is based on the Semantics Of Business Vocabulary And Rules standard. Moreover, the paper describes the Association-Oriented Metamodel as a case study for the method.

In this paper, the authors introduce a novel approach to data metamodel conceptualization called Conceptual Layer of Metamodels. This conceptualization method gives an common conceptual layer based on Semantics of Business Vocabulary and Rules for expressing data metamodels built using different categories and having other characteristics but representing similar semantics. This paper describes abstraction and concretization dependency and covers the metamodeling layer and core modeling concepts layers.

Business rules are used to define or limit certain aspects of business. While they should be understandable for different interested parties, including business analysts, end-users, programmers, or testers, it is also highly recommendable that computers efficiently process them. Business rules can be expressed using different languages (styles), potentially influencing their quality. The main aim of this research is to determine which of the business rule specification notations used at early stages of software development (CIM level) is the best for different groups of recipients. The paper presents the results of the experiment designed to compare the usability of two business rule specification languages: SBVR SP (Polish version of SBVR SE), and RuleSpeak®. These languages have very similar expressiveness and scope of application. Both introduce some restrictions on word order (grammar) and expressions used. Their usability was assessed from three perspectives: effectiveness, efficiency, and satisfaction. The study was conducted on three groups of participants representing various user groups. It occurred that SBVR SP is more useful than RuleSpeak.

In model-driven information systems engineering, model transformations reside at the very core of this paradigm. Indeed, model transformations (in particular, model-to-model, or M2M) are a must-have feature of any modern model-driven approach supported by CASE technology. Model transformations are intended to raise quality of the models under development, and also speed-up the modeling itself by bringing in certain level of automation into the development process. Nevertheless, due to certain objective reasons, the level of such automation is spread unevenly throughout the development process – in this respect, Business Modeling and System Analysis are, arguably, the most underdeveloped phases of the model-driven information systems development life cycle. In this paper, we show how M2M transformation technology was used to extract well-structured business vocabularies and business rules from formal use case models represented through a set of use case diagrams; Object Management Group's (OMG) standards Semantics for Business Vocabulary and Rules (SBVR) and Unified Modeling Language (UML) were used for this purpose. The proposed solution consists of two concurrent approaches, namely, automatic and semi-automatic, which may be used selectively to achieve the best expected result. Basic implementation aspects of the solution integrating both approaches are also briefly presented in the paper. While UML use case models is the main subject in this research, the proposed solution may be adopted for other UML and MOF-based models as well.

Today’s business entities face an ever-growing number of laws and regulations due to recent high profile business scandals and failures. Small and medium scale enterprises (SMSE) in developing countries do not have an efficient compliance checking mechanism to make their business processes compliant with these regulatory standards. This checking mechanism is needed to give the enterprises full assurance of complete adherence to regulatory standards, bodies, or Service Level Agreements. Therefore, a structured and efficient compliance management model is needed to aid SMSE in launching their businesses safely and to ensure business processes fit into the classical regulatory standards. This paper presents a business rules compliance checking model and architecture for SMSEs in developing countries to verify and monitor their business process models at design time and at run time. It involves a systematic compliance requirements classification and analysis that employs a goal based requirement engineering approach prior to design time verification. It also introduces the idea and demonstration of network analysis for runtime business processes monitoring. The business process model will be verified at design time using a Simple PROMELA Interpreter model checker through Linear Temporal Logic rules. The approaches were tested on a financial institution in Nigeria, a developing nation in Africa at the time of this research. In order to ensure that the choice of the requirements analysis approach was efficient, a number of standard metrics for evaluating requirements engineering techniques were used and promising results were obtained. We also carried out a comparative analysis of the proposed approach in this paper with the approaches of previous research papers. This approach proved to be effective in terms of clarity, simplicity, flexibility and expressiveness while reducing incomplete adherence of business processes and enhancing the correctness of the business process.

Business processes compliance monitoring checks whether running business processes comply with involved compliance rules. Business processes in modern enterprise are rarely supported by a single and centralized workflow system, but instead implemented over different applications (e.g., CRM, ERP, WfMS, and legacy systems). The running data (i.e., event) about process executions are scattered across these applications. Under such circumstance, understanding the compliance of running processes entails the compliance monitoring enabling to correlate events from different applications and even different process instances. This paper introduces a framework named as bpCMon for business process compliance monitoring. bpCMon consists of an expressive compliance rule language ECL and a rule system ERS. ECL is a pattern-based formal language for specifying compliance rules of multiple process perspectives, and also allows for describing event-correlation conditions. ERS, generated from compliance rules in ECL, in turn plays as a compliance monitor enabling to correlate events efficiently by means of an indexing structure created from event-correlation conditions. The applicability of bpCMon is demonstrated by experiments on real-world data sets, and the efficiency of bpCMon is illustrated by comparing with related approaches. Overall, bpCMon enables business process compliance monitoring to meet real-world requirements.

This paper presents an approach used for automated generations of software constraints. In this model, the SBVR (Semantics of Business Vocabulary and Rules) based semi-formal representation is obtained from the syntactic and semantic analysis of a NL (Natural Language) (such as English) sentence. A SBVR representation is easy to translate to other formal languages as SBVR is based on higher-order logic like other formal languages such as OCL (Object Constraint Language). The proposed model endows with a systematic and powerful system of incorporating NL knowledge on the formal languages. A prototype is constructed in Java (an Eclipse plug-in) as a proof of the concept. The performance was tested for a few sample texts taken from existing research thesis reports and books.

Enterprises model the behavior of their business to prepare a communication standard for business analysts and to specify requirements to Information Technology (IT) people. The communication gap between IT group and business analysts, who lie on the opposite end of the business spectrum exists due to the different terminologies used in their respective fields regarding the same context. This gap has led to major software failures which prompted the OMG group has come up with a new standard - Semantic of Business Vocabulary and Business Rules (SBVR). Declarative models are provided by SBVR to represent Business Vocabulary and Business Rules which can be understood by everyone working throughout the business spectrum. Each business is governed by business rules which are constrained by the regulation policy set up by the policy guidelines of the organization and government regulations set up on the organization. Business rules are specified in documents like user guides, requirement documents, terms and conditions, do's and don'ts. Typically a Business Analyst interprets the document and manually extracts rules based on his understanding which leads to potential discrepancies, ambiguities and quality issues in the software system. To minimize such errors, in this paper we present an unsupervised approach to automatically extract SBVR vocabularies and rules from domain-specific business documents. We also present our initial results and comparative study with our earlier approach.

Modern Businesses are rapidly growing in complexity and functionalities. To ensure smooth functioning, businesses need to adhere to a set of guidelines and constraints which are efficiently represented by Business Rules(BRs). Due to the large number of inter-dependent BRs, anomalies such as inconsistencies, redundancies and circularities creep in to the rule base, which if not dealt with properly can cause the business to function improperly causing significant damage at multiple levels. Present state of the art methods identify such anomalies in BRs by converting the rules to knowledge representation (Ontology, SMT-LIBv2, etc.) and then running them on solvers. These approaches suffer from certain drawbacks, namely incomplete mappings and scalability of solvers. To overcome these shortcomings, in this paper we propose to represent the Business Rules(BRs) as Decision Diagrams (BDD, SDD, MDD, etc.) and use graph algorithms on top of their canonical representations to identify anomalies. Presently, business rules and processes are treated separately. We model rules as Decision Diagrams(DDs) to integrate with certain graphical representations of business processes (e.g., DCR Graphs, BPMN, etc.), enabling us to efficiently analyze a much more enriched set of business information. We show an initial set of mappings from business rules to Binary Decision Diagrams (BDD's), integrate with processes, identify various anomalies and outline our vision and prospective reach of this approach.

We consider current Design and Engineering Methodology for Organizations (DEMO) Action Rules Specification to be unnecessarily complex and ambiguous. Even while using a “structured English” syntax similar to the one used in SBVR, such specifications are: incomplete while not containing enough ontological information to derive a functional implementation; and complex by containing mostly unneeded specifications. We propose a new meta-model for DEMO’s Action Model in the form of an EBNF syntax which is being implemented in a prototype that directly executes DEMO models as an Information and Workflow System. This prototype includes an action engine that runs DEMO transactions and the enclosed actions specified in our approach. We are currently integrating Blockly in our solution to allow syntactically correct visual programming of our proposed new Action Rule language that includes constructs to evaluate logical conditions, update the state of internal or external information systems, obtain input and provide output (formatted with WYSIWYG template editor) to users, among others.

In requirement engineering (RE) for agile software development, the Class-Responsibility-Collaborator (CRC) models are used as important brainstorming tool. However, manual generation of such CRC models by analyzing the requirements is a difficult and time-consuming task due to ambiguity and informal nature of natural languages-based software requirements. This paper introduces an improved requirement engineering technique based on CRC models that can help in specifying and analyzing software requirements in a better and faster way and curtailing difficulties associated with the traditional RE analysis technique. The proposed technique employs Semantics of Business Vocabulary and Rules (SBVR) to capture and specify software requirements in a controlled natural language. The SBVR representation is processed to extract object-oriented information and map the extracted information to CRC models in both textual and visual form. The proposed approach is implemented as an Eclipse plugin prototype SBVR2CRC as a proof of concept and the results of the experiments validate the effectiveness of the presented approach. Results show that such automated approach not only saves certain time and effort but also assists in generation of better CRC models and simplifies the CRC models based agile software development.

Business rules are the basis of the business logic of the most information systems. They are considered to be the first citizens of the requirements world and are the key element of business and technology models. It is highly recommended the rules to be written in a natural language, which is understandable for all interested parties. The paper presents the actual state of a framework for business rules processing. The framework is capable of serving business rules written in a controlled language (which syntax was inspired with SBVR SE) assuming that the source code is properly instrumented. Unfortunately, there exists a very limited number of solutions enabling processing business rules expressed that way. The proof-of-concept implementation proved the correctness and usefulness of the proposed approach.

Being among the best-selling and most advanced features of model-driven development, model-to-model transformation could help improving one of the most time- and resource-consuming efforts in the process of model-driven information systems engineering, namely, discovery and specification of business vocabularies and business rules within the problem domain. Nonetheless, despite the relatively high levels of automation throughout the whole systems' model-driven development process, business modeling stage remains among the most under re-searched areas throughout the whole process. In this paper, we introduce a novel natural language processing (NLP) technique to one of our latest developments for the automatic extraction of SBVR business vocabularies and business rules from UML use case diagrams. This development remains arguably the most comprehensive development of this kind currently available in public. The experiment provided proof that the developed NLP enhancement delivered even better extraction results compared to the already satisfactory performance of the previous development. This work contributes to the research in the areas of model transformations and NLP within the model-driven development of information systems, and beyond.

An enterprise system operates business by providing various services that are guided by set of certain business rules (BR) and constraints. These BR are usually written using plain Natural Language in operating procedures, terms and conditions, and other documents or in source code of legacy enterprise systems. For implementing the BR in a software system, expressing them as UML use-case specifications, or preparing for Merger & Acquisition (M&A) activity, analysts manually interpret the documents or try to identify constraints from the source code, leading to potential discrepancies and ambiguities. These issues in the software system can be resolved only after testing, which is a very tedious and expensive activity. To minimize such errors and efforts, we propose BuRRiTo framework consisting of automatic extraction of BR by mining documents and source code, ability to clean them of various anomalies like inconsistency, redundancies, conflicts, etc. and able to analyze the functional gaps present and performing semantic querying and searching.

This paper addresses the problem of transforming business specifications written in natural language into formal models suitable for use in information systems development. It proposes a method for transforming controlled natural language specifications based on the Semantics of Business Vocabulary and Business Rules standard. This approach is unique in combining techniques from Model-Driven Engineering (MDE), Cognitive Linguistics, and Knowledge-based Configuration, which allows the reliable semantic processing of specifications and integration with existing MDE tools to improve productivity, quality, and time-to-market in software development. The method first learns the vocabulary of the specification from glossary-like definitions then parses the rules of the specification and outputs the resulting formal SBVR model. Both aspects of the method are tested separately, with the system correctly learning 98% of the vocabulary and correctly interpreting 98% of the rules of an SBVR SE based example. Finally, the proposed method is compared to state-of-the-art approaches for creating formal models from natural language specifications, arguing that it meets the criteria necessary to fulfil the three goals of (1) shifting control of specification to non-technical business experts, (2) reducing the manual effort involved in formalising specifications, and (3) supporting business experts in creating well-formed sets of business vocabularies and rules. HighlightsA method for deep processing of natural language business specifications is proposed.The method is based on Cognitive Linguistics and Knowledge-based Configuration.The method acquires vocabulary from a business glossary and parses business rules.The vocabulary acquisition achieves an accuracy of 96%.The semantic analysis of rules achieves an accuracy of 98%.

International audience; We investigate the use of computer aided molecular design (CAMD) approach for enhancing the properties of existing molecules by modifying their chemical structure to match target property values. The activity of tailoring molecules requires to aggregate knowledge disseminated across the whole chemical enterprise hierarchy, from the manager level to the chemists and chemical engineers, with different backgrounds and perception of what the ideal molecule would be. So, we propose a framework that allows the search to be successful in matching all requirements while capitalizing this knowledge spread among actors with different backgrounds with the help of SBVR (Semantics of Business Vocabulary and Rules) and OCL (Object Constraint Language). In the context of using biomass as the feedstock, we discuss the coupling of CAMD tools with computer aided organic synthesis tools so as to propose enhanced bio-sourced molecule candidates which could be synthesized with eco-friendly pathways. Finally, we evaluate the sustainability of the molecules and of the whole decision-process as well. Specific applications that concern the use of bio-sourced molecules are presented: a case of typical derivatives of chemical platform molecules issued from the itaconic acid to substitute N-methyl-2-pyrrolidone NMP or dimethyl-formamide DMF solvents and a case of derivatives of lipids to be used a biolubricants.

Business firms around the world have been generating enormous amounts of domain-related documents. Most of these firms are adapting semantic Web-based techniques into their software systems. Hence, they want to semantically enrich their documents to enable more meaningful querying or processing of the information in the documents. To impart semantics into these documents, ontologies relevant to the business domain should be used. In this context, to populate the domain ontology with the information from the source documents, a method for semi-automatic learning of extraction rules for populating the ontology is presented and implemented in the rule learning system. In addition to the rule learning system, a framework for separating the business logic from application logic and storing the business rules and extraction rules in external user-friendly format is presented in brief. The rule learning system is mainly developed to be a part of the presented framework, but it can be used as a standalone system to learn any decision or association rules too. The framework uses the rule learning system for learning extraction rules. The main idea behind the work presented is to learn extraction rules to be used by an information extraction system (part of the framework) to populate the domain ontology. The extraction rules learned by the rule learning system can be used with any business rules management system (BRMS) with appropriate wrappers to populate the domain ontology.

Through different viewpoints exposed by an ArchiMate Business Architecture Model, a high level of business abstraction is achieved in the development phase of a software system. This allows to carry out the automation of business rules for payroll loan products placement within the receipt of credit applications. Currently the flexibility of information systems plays a key role as a strategy in the decision-making process in financial institutions. This is because they require a timely response to the changing market demand for products and services for financial consumers. Given the above, information technology management in organizations faces the ongoing challenge of developing software solutions that meet the needs of internal and external customers. This is done by using recurring architectural models that provide safety, maintainability and scalability. In order to make this possible, a business rules automation model that dissociates the business logic from the business rules facilitates, subsequently, a possible change in the proposed rules. In this paper, the architecture of the software system is based on five viewpoints offered by the ArchiMate modeling, which was carried out with the modeling tool Coloso.

This paper deals with formalization of business rules by formal grammars. In our work we focus on methods for high frequency data processing. We process data by using complex event platforms (CEP) which allow to process high volume of data in nearly real time. Decision making process is contained by one level of processing of CEP. Business rules are used for decision making process description. For the business rules formalization we chose matrix grammar. The use of formal grammars is quite natural as the structure of rules and its rewriting is very similar both for the business rules and for formal grammar. In addition the matrix grammar allows to simulate dependencies and correlations between the rules. The result of this work is a model for data processing of knowledge-based decision support system described by the rules of formal grammar. This system will support the decision making in CEP. This solution may contribute to the speedup of decision making process in complex event processing and also to the formal verification of these systems.

A fundamental challenge for enterprises is to ensure compliance of their business processes with imposed compliance rules stemming from various sources, e.g., corporate guidelines, best practices, standards, and laws. In general, a compliance rule may refer to multiple process perspectives including control flow, time, data, resources, and interactions with business partners. On one hand, compliance rules should be comprehensible for domain experts who must define, verify, and apply them. On the other, these rules should have a precise semantics to avoid ambiguities and enable their automated processing. Providing a visual language is advantageous in this context as it allows hiding formal details and offering an intuitive way of modeling the compliance rules. However, existing visual languages for compliance rule modeling have focused on the control flow perspective so far, but lack proper support for the other process perspectives. To remedy this drawback, this paper introduces the extended Compliance Rule Graph language, which enables the visual modeling of compliance rules with the support of multiple perspectives. Overall, this language will foster the modeling and verification of compliance rules in practice.

This paper provides formal semantics for an extension of the Object-Role Modeling approach to support declaration of dynamic rules. Dynamic rules differ from static rules by involving state transitions, rather than simply individual states. This paper restricts application of dynamic rules to single-step transactions, with a previous state (input to the transaction) and a new state (the result of that transaction). These dynamic rules specify an elementary transaction type by indicating which kinds of objects or facts (being added, deleted or updated) are involved. Dynamic rules may declare pre-conditions relevant to the transaction, and a post-condition stating the properties of the new state. In this paper the authors provide such dynamic rules with a formal semantics based on sorted, first-order predicate logic. The key idea underlying their solution is the formalization of dynamic constraints in terms of static constraints on the database transaction history.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.

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.