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Über dieses Buch

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2015, held in Buenos Aires, Argentina, in July 2015, associated with IJCAI 2015, the 24th International Joint Conference on Artificial Intelligence. The 9 revised full papers presented were carefully reviewed and selected from 10 submissions. The papers feature current research involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques. They address the following topics: argumentation; conceptual graphs; RDF; and representations of constraint satisfaction problems.

Inhaltsverzeichnis

Frontmatter

Designing a Knowledge Representation Tool for Subject Matter Structuring

Relying on pedagogical theories of subject matter structuring and presentation, this paper focuses on the design of a knowledge representation tool for the scheming and organization of educational materials. The idea originates from the Educational Concept Maps model - logical and abstract annotation system, developed with the aim of guaranteeing the reusability of both teaching materials and knowledge structures; in fact, the knowledge structure could be reused for design of different courses according to the learner target. A sequence of concepts characterizing the subject matter under design (lesson or entire course) define a teaching/learning path through the map. It represents the output of the design process of lesson plan, which could be imported in a text-editor, in a LCMS, or presented as web pages. The final goal is to develop a tool assisting the teacher in the daily design of lesson plans via a pliable structured model of domain knowledge.
Giovanni Adorni, Frosina Koceva

Aligning Experientially Grounded Ontologies Using Language Games

Ontology alignment is essential to enable communication in a multi-agent system where agents have heterogeneous ontologies. We use language games as a decentralised iterative ontology alignment solution in a multi-agent system where ontologies are grounded in measurements taken in a dynamic environment. Rather than attempting to ground ontologies through physical interaction, we design language game strategies that involve exchanging descriptions of the environment as graph patterns and interpreting descriptions using graph matching. These methods rely on structural similarity as evidence for ontology alignment. We compare various language game strategies with respect to communication overhead and alignment success and provide preliminary results which show that ontology alignment using language games that rely on descriptions alone can result in perfect alignments with only modest communication overhead. However, this requires that environmental dynamics are reasoned about when providing descriptions.
Michael Anslow, Michael Rovatsos

An Overview of Argumentation Frameworks for Decision Support

Several forms of argumentation frameworks have been used to support decision-making: these frameworks allow at the same time a graphical representation of decision problems as well as an automatic evaluation of the goodness of decisions. We overview several such uses of argumentation frameworks and discuss future directions of research, including cross-fertilisations amongst them.
Lucas Carstens, Xiuyi Fan, Yang Gao, Francesca Toni

Learning Bayesian Networks with Non-Decomposable Scores

Modern approaches for optimally learning Bayesian network structures require decomposable scores. Such approaches include those based on dynamic programming and heuristic search methods. These approaches operate in a search space called the order graph, which has been investigated extensively in recent years. In this paper, we break from this tradition, and show that one can effectively learn structures using non-decomposable scores by exploring a more complex search space that leverages state-of-the-art learning systems based on order graphs. We show how the new search space can be used to learn with priors that are not structure-modular (a particular class of non-decomposable scores). We also show that it can be used to efficiently enumerate the \(k\)-best structures, in time that can be up to three orders of magnitude faster, compared to existing approaches.
Eunice Yuh-Jie Chen, Arthur Choi, Adnan Darwiche

Combinatorial Results on Directed Hypergraphs for the SAT Problem

Directed hypergraphs have already been shown to unveil several combinatorial inspired results for the SAT problem. In this paper we approach the SAT problem by searching a transversal of the directed hypergraphs associated to its instance. We introduce some particular clause orderings and study their influence on the backtrack process, exhibiting a new subclass of CNF for which SAT is polynomial. Based on unit resolution and a novel dichotomous search, a new DPLL-like algorithm and a renaming-based combinatorial approach are proposed. We then investigate the study of weak transversals in this setting and reveal a new degree of a CNF formula unsatisfiability and a structural result about unsatisfiable formulae.
Cornelius Croitoru, Madalina Croitoru

Conceptual Graphs for Formally Managing and Discovering Complementary Competences

The capture, the structuring of the expertise or the competences of an “object” (lie a business partner, an employee and even a software component or a Web service) are of very crucial interest in many application domains, like cooperative and distributed applications as well as in cooperative e_business applications and in human resource managment. The work that is described in this paper concerns the advertising, the classification and the discovry of competences. The foundings of the proposals that are described here after are a formal representation of competences using conceptual graphs and the use of operations on conceptual graphs for competence discovery and their possible composition.
Nacer Boudjlida, Badrina Guessoum-Boumezoued

Subjective Networks: Perspectives and Challenges

Subjective logic is a formalism for reasoning under uncertain probabilistic information, with an explicit treatment of the uncertainty about the probability distributions. We introduce subjective networks as graph-based structures that generalize Bayesian networks to the theory of subjective logic. We discuss the perspectives of the subjective networks representation and the challenges of reasoning with them.
Magdalena Ivanovska, Audun Jøsang, Lance Kaplan, Francesco Sambo

RDF-SQ: Mixing Parallel and Sequential Computation for Top-Down OWL RL Inference

The size and growth rate of the Semantic Web call for querying and reasoning methods that can be applied over very large amounts of data. In this paper, we discuss how we can enrich the results of queries by performing rule-based reasoning in a top-down fashion over large RDF knowledge bases.
This paper focuses on the technical challenges involved in the top-down evaluation of the reasoning rules. First, we discuss the application of well-known algorithms in the QSQ family, and analyze their advantages and drawbacks. Then, we present a new algorithm, called RDF-SQ, which re-uses different features of the QSQ algorithms and introduces some novelties that target the execution of the OWL-RL rules.
We implemented our algorithm inside the QueryPIE prototype and tested its performance against QSQ-R, which is the most popular QSQ algorithm, and a parallel variant of it, which is the current state-of-the-art in terms of scalability. We used a large LUBM dataset with ten billion triples, and our tests show that RDF-SQ is significantly faster and more efficient than the competitors in almost all cases.
Jacopo Urbani, Ceriel Jacobs

Bring User Interest to Related Entity Recommendation

Most existing approaches to query recommendation focus on query-term or click based analysis over the user session log or click-through data. For entity query, however, finding the relevant queries from these resources is far from trivial. Entity query is a special kind of short queries that commonly appear in image search, video search or object search. Focusing on related entity recommendation, this paper proposes to collect rich related entities of interest from a large number of entity-oriented web pages. During the collection, we maintain a large-scale and general-purpose related entity network (REN), based upon a special co-occurrence relation between the related entity and target entity. Benefiting from the REN, we can easily incorporate various types of related entity into recommendation. Different ranking methods are employed to recommend related and diverse entities of interest. Extensive experiments are conducted to assess the recommendation performance in term of Accuracy and Serendipity. Experimental results show that the REN is a good recommendation resource with high quality of related entities. For recommending related entity, the proposed REN-based method achieves good performance compared with a state-of-the-art relatedness measurement and two famous recommendation systems.
Zhongyuan Wang, Fang Wang, Ji-Rong Wen, Zhoujun Li

Backmatter

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