Large-scale Parallel Stratified Defeasible Reasoning

We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts

SweetDeal: Representing Agent Contracts With Exceptions using XML Rules, Ontologies, and Process Descriptions

SweetDeal is a rule-based approach to representation of business contracts that enables software agents to create, evaluate, negotiate, and execute contracts with substantial automation and modularity. It builds upon the situated courteous logic programs knowledge representation in RuleML, the emerging standard for Semantic Web XML rules. Here, we newly extend the SweetDeal approach by also incorporating process knowledge descriptions whose ontologies are represented in DAML+OIL (the close predecessor of W3C's OWL, the emerging standard for Semantic Web ontologies), thereby enabling more complex contracts with behavioral provisions, especially for handling exception conditions (e.g., late delivery or non-payment) that might arise during the execution of the contract. This provides a foundation for representing and automating deals about services – in particular, about Web Services, so as to help search, select, and compose them. We give a detailed application scenario of late delivery in manufacturing supply chain management (SCM). In doing so, we draw upon our new formalization of process ontology knowledge from the MIT Process Handbook, a large, previously-existing repository used by practical industrial process designers. Our system is the first to combine emerging Semantic Web standards for knowledge representation of rules (RuleML) with ontologies (DAML+OIL/OWL) with each other, and moreover for a practical e-business application domain, and further to do so with process knowledge. This also newly fleshes out the evolving concept of Semantic Web Services. A prototype (soon public)

Rough Set Semantics for Identity on the Web

Identity relations are at the foundation of many logic-based knowledge representations. We argue that the traditional notion of equality, is unsuited for many realistic knowledge representation settings. The classical interpretation of equality is too strong when the equality statements are re-used outside their original context. On the Semantic Web, equality statements are used to interlink multiple descriptions of the same object, using owl:sameAs assertions. And indeed, many practical uses of owl:sameAs are known to violate the formal Leibniz-style semantics. We provide a more flexible semantics to identity by assigning meaning to the subrelations of an identity relation in terms of the predicates that are used in a knowledge-base. Using those indiscernability-predicates, we define upper and lower approximations of equality in the style of rought-set theory, resulting in a quality-measure for identity relations

Reusing thesauri: documentalists face the semantic web challenge

A practical approach to the development of ontologies from thesauri is presented, using the Methontology methodology, which permits the use of any language developed so far for the implementation of ontologies, as well as semantic web annotations based on these ontologies. This approach is an alternative to SKOS-Core, the World Wide Web Consortium (W3C) approach to publication of thesauri within the semantic web. The advantage of our approach is that it provides significantly more semantic information than a thesaurus (and, hence, than does SKOS-Core). A two-level ontological architecture for domain representation is also described, consisting of a knowledge representation ontology [based on Ranganathan’s (1967) facets] for the representation of high-level relationships in the domain and a domain ontology for the description of a particular field within the domain

A Knowledge-Engine Architecture for a Competence Management Information System

This paper describes the ongoing project to develop a knowledge-engine architecture that is being specified and developed by a Portuguese software development company called Shortcut. The primary goal of this work is create an architecture suitable for use, initially, in a Competence Management System (CMS) but also scalable for later use in more generic forms of Knowledge Management Systems (KMS). In general, Knowledge Management (KM) initiatives promote the management, i.e. the creation, storage and sharing, of knowledge assets within an organization. The practical focus of our work is to support the management of employees’ competencies through using a KM approach to create a web based CMS based on a structured content management infrastructure. The system is designed using an ontology-driven framework that incorporates expert annotations which integrate aspects of less tangible knowledge, such as contextual information with more structured knowledge such as that stored in databases, procedures, manuals, books and reports. The theoretical focus of the work is on the representation of competence-based knowledge resources, such as human capital, skills, heuristics acquired during project development, best practices and lessons-learned. This work should contribute for improving the understanding and analysis of the collective knowledge, skills and competencies that are created through problem solving in day-to-day activities and could act as a meeting point for issues around problem solving in complex organizations and context-based information retrieval