Provenance for SPARQL queries

Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof

Reason Maintenance - Conceptual Framework

This paper describes the conceptual framework for reason maintenance developed as part of WP2

A Semantic Workflow Mechanism to Realize Experimental Goals and Constraints

Postprin

A Formal Context Representation Framework for Network-Enabled Cognition

Network-accessible resources are inherently contextual with respect to the specific situations (e.g., location and default assumptions) in which they are used. Therefore, the explicit conceptualization and representation of contexts is required to address a number of problems in Network- Enabled Cognition (NEC). We propose a context representation framework to address the computational specification of contexts. Our focus is on developing a formal model of context for the unambiguous and effective delivery of data and knowledge, in particular, for enabling forms of automated inference that address contextual differences between agents in a distributed network environment. We identify several components for the conceptualization of contexts within the context representation framework. These include jurisdictions (which can be used to interpret contextual data), semantic assumptions (which highlight the meaning of data), provenance information and inter-context relationships. Finally, we demonstrate the application of the context representation framework in a collaborative military coalition planning scenario. We show how the framework can be used to support the representation of plan-relevant contextual information

Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension

Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

Declarative if–then rules have proven very useful in many applications of expert sys- tems. They can be managed in deductive databases and evaluated using the well-known forward-chaining approach. For domain-experts, however, the syntax of rules becomes complicated quickly, and already many different knowledge representation formalisms ex- ist. Expert knowledge is often acquired in story form using interviews. In this paper, we discuss its representation by defining domain-specific languages (Dsls) for declarative ex- pert rules. They can be embedded in Prolog systems in internal Dsls using term expan- sion and as external Dsls using definite clause grammars and quasi-quotations – for more sophisticated syntaxes. Based on the declarative rules and the integration with the Prolog-based deductive database system DDbase, multiple rules acquired in practical case studies can be combined, compared, graphically analysed by domain-experts, and evaluated, resulting in an extensi- ble system for expert knowledge. As a result, the actual modeling Dsl becomes executable; the declarative forward-chaining evaluation of deductive databases can be understood by the domain experts. Our Dsl for rules can be further improved by integrating ontologies and rule annotations

Declarative Rules for Annotated Expert Knowledge in Change Management

In this paper, we use declarative and domain-specific languages for representing expert knowledge in the field of change management in organisational psychology. Expert rules obtained in practical case studies are represented as declarative rules in a deductive database. The expert rules are annotated by information describing their provenance and confidence. Additional provenance information for the whole - or parts of the - rule base can be given by ontologies. Deductive databases allow for declaratively defining the semantics of the expert knowledge with rules; the evaluation of the rules can be optimised and the inference mechanisms could be changed, since they are specified in an abstract way. As the logical syntax of rules had been a problem in previous applications of deductive databases, we use specially designed domain-specific languages to make the rule syntax easier for non-programmers. The semantics of the whole knowledge base is declarative. The rules are written declaratively in an extension datalogs of the well-known deductive database language datalog on the data level, and additional datalogs rules can configure the processing of the annotated rules and the ontologies

Constructive Reasoning for Semantic Wikis

One of the main design goals of social software, such as wikis, is to support and facilitate interaction and collaboration. This dissertation explores challenges that arise from extending social software with advanced facilities such as reasoning and semantic annotations and presents tools in form of a conceptual model, structured tags, a rule language, and a set of novel forward chaining and reason maintenance methods for processing such rules that help to overcome the challenges. Wikis and semantic wikis were usually developed in an ad-hoc manner, without much thought about the underlying concepts. A conceptual model suitable for a semantic wiki that takes advanced features such as annotations and reasoning into account is proposed. Moreover, so called structured tags are proposed as a semi-formal knowledge representation step between informal and formal annotations. The focus of rule languages for the Semantic Web has been predominantly on expert users and on the interplay of rule languages and ontologies. KWRL, the KiWi Rule Language, is proposed as a rule language for a semantic wiki that is easily understandable for users as it is aware of the conceptual model of a wiki and as it is inconsistency-tolerant, and that can be efficiently evaluated as it builds upon Datalog concepts. The requirement for fast response times of interactive software translates in our work to bottom-up evaluation (materialization) of rules (views) ahead of time – that is when rules or data change, not when they are queried. Materialized views have to be updated when data or rules change. While incremental view maintenance was intensively studied in the past and literature on the subject is abundant, the existing methods have surprisingly many disadvantages – they do not provide all information desirable for explanation of derived information, they require evaluation of possibly substantially larger Datalog programs with negation, they recompute the whole extension of a predicate even if only a small part of it is affected by a change, they require adaptation for handling general rule changes. A particular contribution of this dissertation consists in a set of forward chaining and reason maintenance methods with a simple declarative description that are efficient and derive and maintain information necessary for reason maintenance and explanation. The reasoning methods and most of the reason maintenance methods are described in terms of a set of extended immediate consequence operators the properties of which are proven in the classical logical programming framework. In contrast to existing methods, the reason maintenance methods in this dissertation work by evaluating the original Datalog program – they do not introduce negation if it is not present in the input program – and only the affected part of a predicate’s extension is recomputed. Moreover, our methods directly handle changes in both data and rules; a rule change does not need to be handled as a special case. A framework of support graphs, a data structure inspired by justification graphs of classical reason maintenance, is proposed. Support graphs enable a unified description and a formal comparison of the various reasoning and reason maintenance methods and define a notion of a derivation such that the number of derivations of an atom is always finite even in the recursive Datalog case. A practical approach to implementing reasoning, reason maintenance, and explanation in the KiWi semantic platform is also investigated. It is shown how an implementation may benefit from using a graph database instead of or along with a relational database