A Defeasible Logic Reasoner for the Semantic Web

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system is called DR-DEVICE and is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper also briefly presents a semantic web broker example for apartment renting.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc. However, the syntax of defeasible logic may appear too complex for many users. In this paper we present a graphical authoring tool for defeasible logic rules that acts as a shell for the DR-DEVICE defeasible reasoning system over RDF metadata. The tool helps users to develop a rule base using the OO-RuleML syntax of DR-DEVICE rules, by constraining the allowed vocabulary through analysis of the input RDF namespaces, so that the user does not have to type-in class and property names. Rule visualization follows the tree model of RuleML. The DR-DEVICE reasoning system is implemented on top of the CLIPS production rule system and builds upon an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules.

Deploying defeasible logic rule bases for the semantic web

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

The Incoterms, international commercial terms, offer a rare example of highly standardised terms for commercial transactions. We present a logical analysis of the Incoterms. Our analysis shows that there is a rich variety of different cases of deontic and defeasible reasoning in the Incoterms. Most of the rules in the Incoterms are expressed in deontic notions such as obligation, permission and right. Also, most of these rules have a defeasible character in the sense that they hold for the normal case, but can be overruled in exceptional circumstances. The results from this logical analysis could contribute to the discussion about the role of deontic and defeasible logics in the representation of and reasoning about legal domains. We also describe how, based on our logical analysis, a legal advisory system for the Incoterms can be implemented in Prolog. This implementation could be further developed into a legal advisory system that could offer advice to commercial users about the choice of the most suitable Incoterms to include in their commercial contracts.

Conflicting rules and rules with exceptions are very common in natural language specification to describe the behaviour of devices operating in a real-world context. This is common exactly because those specifications are processed by humans, and humans apply common sense and strategic reasoning about those rules. In this paper, we deal with the challenge of providing, step by step, a model of energy saving rule specification and processing methods that are used to reduce the consumptions of a system of devices. We argue that a very promising non-monotonic approach to such a problem can lie upon Defeasible Logic. Starting with rules specified at an abstract level, but compatibly with the natural aspects of such a specification (including temporal and power absorption constraints), we provide a formalism that generates the extension of a basic defeasible logic, which corresponds to turned on or off devices.

Over the last twenty years, information integration has received considerable efforts from both industry and academia. Approaches to information integration developed so far can be categorized as follows: (1) first-generation approaches, that require the definition of a global schema and a semantic integration which should be performed upfront (before query execution); (2) second-generation approaches, well illustrated by the dataspace management concept, which promote a pay-asyou-go data integration. The first category has led to well known mediation approaches such as GAV (Global as View), LAV (Local as View), GLAV (Generalized Local As View), BAV (Both As View), and BGLAV (BYU Global-Local-as-View). Approaches pertaining to the second category are geared towards the development of dataspace management systems and are currently gaining a lot of attention. In this chapter we are interested in exploiting both types of approaches in querying conflicting data spread over multiple web sources. To this aim, first we show how an XML-based BGLAV approach can handle these conflicting data sources, then we describe how the same problem can be addressed by using the Multi Fusion Approach (MFA), an approach pertaining to second-generation techniques. Both BGLAV and MFA are illustrated in using genomic data sources accessible through the Web.

Web data are heterogeneous and unstructured, which defines challenges for data crawling, integration and preprocessing. Different studies are data-oriented (i.e. based on the available data) but their results are restricted to their specific data. In contrast, there are various problems prior to identifying what data is needed to solve them, and often multiple data sources are needed. In this context, crawling, integrating and preprocessing data appropriately enables to create datasets for solving such problems. Therefore, this short course addresses these three activities by discussing challenges and practical solutions.

This paper proposes a metadata schema relating to functionality based on Semantic Web technology for the management of the information content of engineering design documents. The schema enables us to annotate web-documents with RDF metadata, which represents devices as having specific functions. The metadata provide a clear and operational semantics for the functional terms in documents. The metadata schema is based on our own functional ontologies, which we believe to provide effective guidelines for consistent functional annotation. These ontologies have been developed over many years and deployed successfully in industry. We then demonstrate a document search system using the metadata, which enables us to retrieve web-documents using the types of function and relationships defined in the schema rather than more superficial terms. Such function-oriented management of information is especially useful in the conceptual design phase. It allows one to find previous cases of the same function in earlier designs and to find related patents. We go on to discuss the following two issues on the interoperability of the functional knowledge. The first is automatic transformation of the functional model in our modeling framework into a chart-style form commonly used for the FMEA (Failure Modes and Effects Analysis) activity using a mapping between our ontology and an FMEA ontology. The second is translation of the functional model between our functional vocabulary and other taxonomy of function, that is, Functional Basis proposed by Stone et al. Another mapping between our functional vocabulary and Functional Basis can be done via a richer, generic reference ontology of functions. This mapping would aim at clarifying ontological differences between functional taxonomies and enabling translation between them.

We present a new approach and a prototype software engine for defeasible reasoning to support automated building code compliance checking. The challenge with formalising defeasible rules in a purely deductive reasoning system is the resulting exponential blow up of the rule set due to the need to explicitly state numerous combinations of exception cases. This leads to a rule set that is brittle, complicated, difficult to understand, and obscures the central point of a given normative provision. Moreover, assessing compliance, to any degree, in the absence of complete information about a building, down to the smallest detail, is very limited in a purely deductive setting in which nothing can be assumed. We empirically evaluate our prototype reasoning engine on an IFC model of a building in Christchurch, New Zealand, for compliance with accessibility criteria from the New Zealand Building Code.

In this paper, the authors present an overall description of their information retrieval system which makes a practical collaboration between Semantic Web and Fuzzy logic in order to have profit from their advantages in the information retrieval domain. Their system is dedicated for kids, for this reason the semantic/fuzzy collaboration materialized must be in the background of the information retrieval process because such category of users cannot certainly control semantic web technologies neither fuzzy logic commands. In this paper, the authors present the different services proposed by their system and how they use Semantic Web and Fuzzy logic to develop it. Evaluation tests of the system using universal measures show clearly its efficiency.

In the semantic web, content is tagged with meaning or semantics to facilitate machine processing and web searching. In general, question answering systems that are built on of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as groundbreaking, top researcher, and tenurable at university x. This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized backward chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed backward chaining ontology reasoning system has better scalability than in-memory reasoning systems.

Motivation The Semantic Web is an endeavor aiming at enriching the existing Web with meta-data and data (and meta-data) processing so as to allow computer systems to actually reason with the data instead of merely rendering it. To this aim, it is necessary to be able to query and update data and meta-data. Existing Semantic Web query languages (like DQL) are special purpose, i.e. they are designed for querying and reasoning with special representations like OWL or RDF, but are not capable of processing generic Web data. On the other hand, the language Xcerpt [1] [2] presented in the poster is a general purpose language that can query any kind of XML data and at the same time, being based on logic programming, provides advanced reasoning capabilities. It could thus serve to implement a wide range of different reasoning formalisms. Likewise, the maintenance and evolution of data on the (Semantic) Web is necessary: the Web is a ”living organism” whose dynamic character requires languages for specifying its evolution. This requirement regards not only updating data from Web resources, but also the propagation of changes on the Web. These issues have not received much attention so far, existing update languages (like XML-RL Update Language [3]) and reactive languages [4] developed for XML data offer the possibility to execute just simple update operations and, moreover, important features needed for propagation of updates on the Web are still missing. The language XChange also presented in the poster builds upon the query language Xcerpt and provides advanced, Web-specific capabilities, such as propagation of changes on the Web (change) and event-based communications between Web sites (exchange).

EVID: A system for interactive defeasible reasoning

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.