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Über dieses Buch

This book constitutes the refereed proceedings of the 8th International RuleML Symposium, RuleML 2014, co-located with the 21st European Conference on Artificial Intelligence, ECAI 2014, held in Prague, Czech Republic, in August 2014.

The 17 full and 6 short papers presented together with 3 keynote talks were carefully reviewed and selected from 48 submissions. The papers cover the following topics: semantic web rule languages and standards, rule engines, formal and operational semantics and rule-based systems, the relation between natural language and rules, automation of business rules generation from existing data, and aspects related to legal rules and norms for web and corporate environments.

Inhaltsverzeichnis

Frontmatter

Keynote Talks

Reaction RuleML 1.0 for Rules, Events and Actions in Semantic Complex Event Processing

Reaction RuleML is a standardized rule markup language for the representation and interchange of reaction rules. This paper gives an introduction to the core knowledge representation mechanisms of Reaction RuleML 1.0 such as multi-sorted signatures and their interpretation, action primitives for knowledge updates, interchange and testing, order-sorted external type systems and the Reaction RuleML metamodel, scopes and mode declarations, semantic profiles, imports of documents, modules and messages. These mechanisms form the basis for an adequate treatment of rules, events and actions, as needed in Semantic Complex Event Processing (SCEP), such as, interchange, translation and testing based on the intended semantics defined in semantic profiles; modularization and distribution of knowledge interfaces with their signatures defining, e.g., complex event detection patterns; closed scoped reasoning on top of scoped modules with dynamic constructive views on meta knowledge; transactional complex actions; conversation based message interchange for Question Answering (Q&A) and rule-based agent architectures such as RuleResponder, etc.
Adrian Paschke

Regular Track

A Logical Characterization of a Reactive System Language

Typical reactive system languages are programmed by means of rules of the form if antecedent then consequent. However, despite their seemingly logical character, hardly any reactive system languages give such rules a logical interpretation. In this paper, we investigate a simplified reactive system language KELPS, in which rules are universally quantified material implications, and computation attempts to generate a model that makes the rules true.
The operational semantics of KELPS is similar to that of other reactive system languages, and is similarly incomplete. It cannot make a rule true by making its antecedent false, or by making its consequent true whether or not its antecedent becomes true. In this paper, we characterize the reactive models computed by the operational semantics. Informally speaking, a model is reactive if every action in the model is an instance of an action in the consequent of a rule whose earlier conditions are true.
Robert Kowalski, Fariba Sadri

On Using Semantically-Aware Rules for Efficient Online Communication

The ever growing number of communication channels not only enables a broader outreach for organizations, but also makes it more difficult for them to manage a very large number of channels and adapted content efficiently. Thus, finding the right channels to disseminate some content and adapting this content to specific channel requirements are real challenges for sharing information both efficiently and effectively. In this work, we present a rule-based system that addresses these challenges by decoupling the information to be shared from the actual channels where it is published. We propose semantic models to characterize and integrate various information sources and channels. A set of independent rules then interrelates these models, specifying the concrete publication workflow and content adaptation required. Furthermore, we evaluate our rule-based system using two different use cases, discussing the added value that the defined rules provide to this scenario and how they contribute to overcoming the identified challenges effectively.
Zaenal Akbar, José María García, Ioan Toma, Dieter Fensel

Conceptual Model Interoperability: A Metamodel-driven Approach

Linking, integrating, or converting conceptual data models represented in different modelling languages is a common aspect in the design and maintenance of complex information systems. While such languages seem similar, they are known to be distinct and no unifying framework exists that respects all of their language features in either model transformations or inter-model assertions to relate them. We aim to address this issue using an approach where the rules are enhanced with a logic-based metamodel. We present the main approach and some essential metamodel-driven rules for the static, structural, components of ER, EER, UML v2.4.1, ORM, and ORM2. The transformations for model elements and patterns are used with the metamodel to verify correctness of inter-model assertions across models in different languages.
Pablo Rubén Fillottrani, C. Maria Keet

On Verifying Reactive Rules Using Rewriting Logic

Rule-based programming has been gaining a lot of interest in the industry lately, through the growing use of rules to model the behavior of software systems. A demand for verifying and analyzing rule based systems has thus emerged. In this paper we propose a methodology, based on rewriting logic specifications written in CafeOBJ, for reasoning about structural errors of systems whose behavior is expressed in terms of reactive rules and verifying safety properties within the same framework. We present our approach through a simple but illustrative example of an e-commerce web site.
Katerina Ksystra, Nikos Triantafyllou, Petros Stefaneas

Using Rules to Develop a Personalized and Social Location Information System for the Semantic Web

In this work, the design and implementation of an innovative context-aware location based social networking service is presented. The proposed system, called “Geosocial SPLIS”, utilizes Semantic Web technologies to deliver personalized information to the end user. It addresses some drawbacks of knowledge-based personalization systems and aims to provide a collaborative knowledge creation platform for other systems. To achieve this, it a) collects data from external sources such as Google Places API and Google+ b) adopts the schema.org ontology to represent people and places profiles, c) provides a web editor for adding rules (modeling user preferences and group-targeted place offers) at run time, d) uses RuleML and Jess rules to represent these rules, e) combines at run-time the above to match user context with up to date information, presented on Google Maps and f) matches user’s preferences with those of his/her nearby friends to present POI’s that are suitable to all of them. All data and rules are stored in the Sesame RDF triple store in order to be shared among various systems.
Iosif Viktoratos, Athanasios K. Tsadiras, Nick Bassiliades

Checking Termination of Logic Programs with Function Symbols through Linear Constraints

Enriching answer set programming with function symbols makes modeling easier, increases the expressive power, and allows us to deal with infinite domains. However, this comes at a cost: common inference tasks become undecidable. To cope with this issue, recent research has focused on finding trade-offs between expressivity and decidability by identifying classes of logic programs that impose limitations on the use of function symbols but guarantee decidability of common inference tasks. Despite the significant body of work in this area, current approaches do not include many simple practical programs whose evaluation terminates. In this paper, we present the novel class of rule-bounded programs. While current techniques perform a limited analysis of how terms are propagated from an individual argument to another, our technique is able to perform a more global analysis, thereby overcoming several limitations of current approaches. We also present a further class of cycle-bounded programs where groups of rules are analyzed together. We show different results on the correctness and the expressivity of the proposed techniques.
Marco Calautti, Sergio Greco, Cristian Molinaro, Irina Trubitsyna

A Datalog + Plus RuleML 1.01 Architecture for Rule-Based Data Access in Ecosystem Research

Rule-Based Data Access (RBDA) enables automated reasoning over a knowledge base (KB) as a generalized global schema for the data in local (e.g., relational or graph) databases reachable through mappings. RBDA can semantically validate, enrich, and integrate heterogeneous data sources. This paper proposes an RBDA architecture layered on Datalog+ RuleML, and uses it for the ΔForest case study on the susceptibility of forests to climate change. Deliberation RuleML 1.01 was mostly motivated by Datalog customization requirements for RBDA. It includes Datalog+ RuleML 1.01 as a standard XML serialization of Datalog+, a superlanguage of the decidable Datalog±. Datalog+ RuleML is customized into the three Datalog extensions Datalog[∃], Datalog[=], and Datalog[\(\bot\)] through MYNG, the RuleML Modular sYNtax confiGurator generating (Relax NG and XSD) schemas from language-feature selections. The ΔForest case study on climate change employs data derived from three main forest monitoring networks in Switzerland. The KB includes background knowledge about the study sites and design, e.g., abundant tree species groups, pure tree stands, and statistical independence among forest plots. The KB is used to rewrite queries about, e.g., the eligible plots for studying a particular species group. The mapping rules unfold our newly designed global schema to the three given local schemas, e.g. for the grade of forest management. The RBDA/ΔForest case study has shown the usefulness of our approach to Ecosystem Research for global schema design and demonstrated how automated reasoning can become key to knowledge modeling and consolidation for complex statistical data analysis.
Harold Boley, Rolf Grütter, Gen Zou, Tara Athan, Sophia Etzold

A Hybrid Diagnosis Approach Combining Black-Box and White-Box Reasoning

We study model-based diagnosis and propose a new approach of hybrid diagnosis combining black-box and white-box reasoning. We implemented and compared different diagnosis approaches including the standard hitting set algorithm and new approaches using answer set programming engines (DLV, Potassco) in the application of Euler/X toolkit, a logic-based toolkit for alignment of multiple biological taxonomies. Our benchmarks show that the new hybrid diagnosis approach runs about twice fast as the black-box diagnosis approach of the hitting set algorithm.
Mingmin Chen, Shizhuo Yu, Nico Franz, Shawn Bowers, Bertram Ludäscher

Multi-valued Argumentation Frameworks

In this paper we explore how the seminal Dung’s abstract argumentation framework can be extended to handle arguments containing gradual concepts. We allow arguments to have a degree of truth associated with them and we investigate the degree of truth to which each argument can be considered accepted, rejected and undecided by an abstract argumentation semantics. We propose a truth-compositional recursive computation, and we discuss examples using the major multi-valued logics such as Godel’s, Zadeh’s and Łukasiewicz’s logic. The findings are a contribution in the field of non-monotonic approximate reasoning and they also represent a well-grounded proposal towards the introduction of gradualism in argumentation systems.
Pierpaolo Dondio

Incomplete and Uncertain Data Handling in Context-Aware Rule-Based Systems with Modified Certainty Factors Algebra

Context-aware systems make use of contextual information to adapt their functionality to current environment state, or user needs and habits. One of the major problems concerning them is the fact, that there is no warranty that the contextual information will be available, nor certain at the time when the reasoning should be performed. This may be due to measurement errors, sensor inaccuracy, or semantic ambiguities of modeled concepts. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. However, handling uncertainties in highly dynamic, mobile environments still requires more consideration. In this paper we perform comparison of application of different uncertainty modeling approaches to mobile context-aware environments. We also present an exemplary solution based on modified certainty factors algebra and logic-based knowledge representation for solving uncertainties caused by the imprecision of context-providers.
Szymon Bobek, Grzegorz J. Nalepa

The Hardness of Revising Defeasible Preferences

Non-monotonic reasoning typically deals with three kinds of knowledge. Facts are meant to describe immutable statements of the environment. Rules define relationships among elements. Lastly, an ordering among the rules, in the form of a superiority relation, establishes the relative strength of rules. To revise a non-monotonic theory, we can change either one of these three elements. We prove that the problem of revising a non-monotonic theory by only changing the superiority relation is a NP-complete problem.
Guido Governatori, Francesco Olivieri, Simone Scannapieco, Matteo Cristani

From Guidelines to Practice: Improving Clinical Care through Rule-Based Clinical Decision Support at the Point of Care

Healthcare Information Technology (HIT) is a dynamically evolving industry due to continuous advancements in healthcare technologies. This necessitates the availability of highly dynamic applications that accommodate frequent changes in business logic. The automation of Clinical Decision Support (CDS) in particular is most liable to changes in health business logic or rules. In terms of system’s architecture, there is a need to separate business logic and rules from the implementation/functionality of the Electronic Health Record (EHR) application, providing processes and rules as reusable components. We propose an architecture utilizing rule-based technologies to facilitate Decision Support to promptly adapt business logic changes, that are reflected immediately in application behavior. This allows real-time and robust CDS for the physician at point of care. Our rule-based implementation (Business Process Modelling Notation (BPMN)+Rules) was successfully used to emulate Clinical workflows, using as an example, the NICE Lung Cancer Clinical Guideline (CG121) as a test scenario.
Ayesha Aziz, Salvador Rodriguez, Chris Chatwin

Rules and Human Language Technology

Requirement Compound Mining and Analysis

In this paper, we motivate and develop the linguistic characteristics of requirement compounds which are major types of business rules. The discourse structures that further refine or elaborate requirements are also analyzed. An implementation is then presented. It is carried out in Dislog on the <TextCoop> platform. Dislog allows high level specifications in logic that allow fast and easy prototyping at a high level of linguistic adequacy. Elements of an indicative evaluation are provided.
Juyeon Kang, Patrick Saint-Dizier

Semi-automated Vocabulary Building for Structured Legal English

Structured English has been applied as computational independent language for defining business vocabularies and business rules, e.g., in the context of OMG’s Semantics and Business Vocabulary Representation (SBVR). It allows non-technical domain experts to engineer knowledge in natural language, but with an underlying semi-formal semantics which eases the automation of machine transformation into formal knowledge representations and logic-based machine interpretation. We adapt this approach to the legal domain in order to support legal domain experts in their task to build legal vocabularies and legal rules in Structured English from legal texts. In this paper we contribute with a semi-automated vocabulary and rule development process which is supported by automated suggestions of legal concepts computed by a semantic legal text analysis. We implement a proof-of-concept in the KR4IPLaw tool, which enables legal domain experts to represent their knowledge in Structured English. We evaluate the proposed approach on the basis of use cases in the domain of IP and patent law.
Shashishekar Ramakrishna, Adrian Paschke

Basics for a Grammar Engine to Verbalize Logical Theories in isiZulu

The language isiZulu is the largest in South Africa by numbers of first language speakers, yet, it is still an underresourced language. In this paper, we approach the grammar piecemeal from a natural language generation approach, and viewed from a potential utility for verbalizing OWL ontologies as a tangible use case. The elaborate rules of the grammar show that a grammar engine and dictionary is essential even for basic verbalizations in OWL 2 EL. This is due to, mainly, the 17 noun classes with embedded semantics and the agglutinative nature of isiZulu. The verbalization of basic constructs requires merging a prefix with a noun and distinguishing an ‘and’ between a list and linking clauses.
C. Maria Keet, Langa Khumalo

Formal Rule Representation and Verification from Natural Language Requirements Using an Ontology

The development of a system is usually based on shared and accepted requirements. Hence, to be largely understood by the stakeholders, requirements are often written in natural language (NL). However, checking requirements completeness and consistency requires having them in a formal form. In this article, we focus on user requirements describing a system behaviour, i.e. its behavioural rules. We show how to transform behavioural rules identified from NL requirements and represented within an OWL ontology into the formal specification language Maude. The OWL ontology represents the generic behaviour of a system and allow us to bridge the gap between informal and formal languages and to automate the transformation of NL rules into a Maude specification.
Driss Sadoun, Catherine Dubois, Yacine Ghamri-Doudane, Brigitte Grau

Learning (Business) Rules from Data

Learning Business Rules with Association Rule Classifiers

The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide on using discovered rules in the Drools BRMS and on setting the ARC parameters. Experiments performed with modified CBA on several UCI datasets indicate that data coverage rule pruning keeps the number of rules manageable, while not adversely impacting the accuracy. The best results in terms of overall accuracy are obtained using minimum support and confidence thresholds. Disjunction between attribute values seem to provide a desirable balance between accuracy and rule count, while negated literals have not been found beneficial.
Tomáš Kliegr, Jaroslav Kuchař, Davide Sottara, Stanislav Vojíř

Interpreting Web Shop User’s Behavioral Patterns as Fictitious Explicit Rating for Preference Learning

We consider applications of user preference rule learning in marketing. We chose rules because of human-understandability. We chose fuzzy logic because it enables to order items for recommendation. In this paper we introduce a rule based system equivalent to the Fagin-Lotem-Naor preference system. We show a multi-user version, introduce induction and compare it to several methods for learning user preference. The methods are based, first, on interpreting e-shop user’s behavioral patterns collected by scripts as fictitious explicit rating. After this we use this (fictitious) explicit rating for content based preference learning.
Our main motivation is on recommending for small or medium-sized e-commerce portals. Due to high competition, users of these portals are not too loyal and e.g. refuse to register or provide any/enough explicit feedback. Furthermore, products such as tours, cars or furniture have very low average consumption rate preventing us from tracking unregistered user between two consecutive purchases. Recommending on such domains proves to be very challenging, yet interesting research task. As a test bed, we have conducted several off-line experiments with real user data from travel agency website confirming competitiveness of our method.
Ladislav Peska, Peter Vojtas

Learning Association Rules from Data through Domain Knowledge and Automation

An approach to automated data mining with association rules based on domain knowledge is introduced. Association rules are understood as interesting pairs of general Boolean attributes. Items of domain knowledge corresponding to various relations of non-Boolean attributes are used to formulate reasonable analytical questions. Particular items of knowledge are mapped to sets of association rules which can be considered their consequences. The sets of consequences are then used to interpret sets of association rules resulting from a data mining procedure.
Jan Rauch, Milan Šimůnek

Using Discriminative Rule Mining to Discover Declarative Process Models with Non-atomic Activities

Process discovery techniques try to generate process models from execution logs. Declarative process modeling languages are more suitable than procedural notations for representing the discovery results deriving from logs of processes working in dynamic and low-predictable environments. However, existing declarative discovery approaches aim at mining declarative specifications considering each activity in a business process as an atomic/instantaneous event. In spite of this, often, in realistic environments, process activities are not instantaneous; rather, their execution spans across a time interval and is characterized by a sequence of states of a transactional lifecycle. In this paper, we investigate how to use discriminative rule mining in the discovery task, to characterize lifecycles that determine constraint violations and lifecycles that ensure constraint fulfillments. The approach has been implemented as a plug-in of the process mining tool ProM and validated on synthetic logs and on a real-life log recorded by an incident and problem management system called VINST in use at Volvo IT Belgium.
Mario Luca Bernardi, Marta Cimitile, Chiara Di Francescomarino, Fabrizio Maria Maggi

Legal Rules and Norms

Modeling Obligations with Event-Calculus

Time plays an important role in norms. In this paper we start from our previously proposed classification of obligations, and point out some shortcomings of Event Calculus (EC) to represent obligations. We propose an extension of EC that avoids such shortcomings and we show how to use it to model the various types of obligations.
Mustafa Hashmi, Guido Governatori, Moe Thandar Wynn

A Process for Knowledge Transformation and Knowledge Representation of Patent Law

Automated support to model and reason based on such modeled legal norms using expert systems, for its use scenarios such as court-fillings or argumentation has increasingly become a subject of interest in last few decades. The core problem in all such automation is removing the vagueness embedded within legal texts/sections and this vagueness is due to the pragmatics involved. As of today, we believe, it is impossible for a system to handle any such problems dealing with legal pragmatics. This work proposes a process which acts a bridge between a legal practitioner can and a knowledge modeler wherein, a legal practitioner provides the legal information pertaining to a section in a simpler form as required by the modeler. We also propose several knowledge representation formats to represent the information at each layer of the proposed process. Additionally during the course of the paper, we propose a mapping scheme from legal norms in natural language format to Controlled Natural Language (CNL) format and finally to a platform independent rule representation format.
Shashishekar Ramakrishna, Adrian Paschke

Legal Responsibility for the Acts of Others: A Logical Analysis

This paper offers a logical analysis of two cases where legal responsibility may emerge for the acts of others: (a) reflex responsibility, and (b) responsibility in the negotiorum gestio doctrine. The current contribution works within a fresh multi-modal system where the new operators are introduced for denoting intentions and actions in the interest of other agents, and the objectively ideal sets of actions for agents.
Clara Smith, Erica Calardo, Antonino Rotolo, Giovanni Sartor

Backmatter

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