The Information Artifact Ontology is an ontology in the domain of information entities. Core to the definition of what it is to be an information entity is the claim that an information entity must be `about' something, which is encoded in an axiom expressing that all information entities are about some entity. This axiom comes into conflict with ontological realism, since many information entities seem to be about non-existing entities, such as hypothetical molecules. We discuss this problem in the context of diagrams of molecules, a kind of information entity pervasively used throughout computational chemistry. We then propose a solution that recognizes that information entities such as diagrams are expressions of diagrammatic languages. In so doing, we not only address the problem of classifying diagrams that seem to be about non-existing entities but also allow a more sophisticated categorisation of information entities.

3D city models - which represent in 3 dimensions the geometric elements of a city - are increasingly used for an intended wide range of applications. Such uses are made possible by using semantically enriched 3D city models and by presenting such enriched 3D city models in a way that allows decision-making processes to be carried out from the best choices among sets of objectives, and across issues and scales. In order to help in such a decision-making process we have defined a framework to find the best visualization technique(s) for a set of potentially heterogeneous data that have to be visualized within the same 3D city model, in order to perform a given task in a specific context. We have chosen an ontology-based approach. This approach and the specification and use of the resulting ontology of 3D visualization techniques are described in this paper.

A useful step towards better interpretation and analysis of the usage patterns is to formalize the semantics of the resources that users are accessing in the Web. We focus on this problem and present an approach for the semantic formalization of usage logs, which lays the basis for eective techniques of querying expressive usage patterns. We also present a query answering approach, which is useful to nd in the logs expressive patterns of usage behavior via formulation of semantic and temporal-based constraints. We have processed over 30 thousand user browsing sessions extracted from usage logs of DBPedia and Semantic Web Dog Food. All these events are formalized semantically using respective domain ontologies and RDF representations of the Web resources being accessed. We show the eectiveness of our approach through experimental results, providing in this way an exploratory analysis of the way users browse theWeb of Data.

Novel research in the field of Linked Data focuses on the problem of entity summarization. This field addresses the problem of ranking features according to their importance for the task of identifying a particular entity. Next to a more human friendly presentation, these summarizations can play a central role for semantic search engines and semantic recommender systems. In current approaches, it has been tried to apply entity summarization based on patterns that are inherent to the regarded data. The proposed approach of this paper focuses on the movie domain. It utilizes usage data in order to support measuring the similarity between movie entities. Using this similarity it is possible to determine the k-nearest neighbors of an entity. This leads to the idea that features that entities share with their nearest neighbors can be considered as significant or important for these entities. Additionally, we introduce a downgrading factor (similar to TF-IDF) in order to overcome the high number of commonly occurring features. We exemplify the approach based on a movie-ratings dataset that has been linked to Freebase entities.

Informledge System (ILS) is a knowledge network with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. In this paper, we aim to put forward the link dynamics involved in intelligent processing of information in ILS. There has been advancement in knowledge management field which involve managing information in databases from a single domain. ILS works with information from multiple domains stored in distributed way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with the concept under consideration, KNNs store the processed information linking concepts and processors leading to the appropriate processing of information.

Currently, the majority of matchers are able to establish simple correspondences between entities, but are not able to provide complex alignments. Furthermore, the resulting alignments do not contain additional information on how they were extracted and formed. Not only it becomes hard to debug the alignment results, but it is also difficult to justify correspondences. We propose a method to generate complex ontology alignments that captures the semantics of matching algorithms and human-oriented ontology alignment definition processes. Through these semantics, arguments that provide an abstraction over the specificities of the alignment process are generated and used by agents to share, negotiate and combine correspondences. After the negotiation process, the resulting arguments and their relations can be visualized by humans in order to debug and understand the given correspondences.

While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understand- ing of space, using which it can infer where to perform planned tasks in a manner that mimics human behav- ior. We assess the situation awareness of rescue work- ers during a simulated USAR scenario and use this as an empirical basis to build our robot’s spatial model. Based upon this spatial model, we present “functional map- ping” as an approach to identify regions in the USAR environment where planned tasks are likely to be opti- mally achievable. The system is deployed and evaluated in a simulated rescue scenario.

Successfully accomplishing everyday manipulation tasks requires robots to have substantial knowledge about the objects they interact with, the environment they operate in as well as about the properties and effects of the actions they perform. Often, this knowledge is implicitly contained in manually written control programs, which makes it hard for the robot to adapt to newly acquired information or to re-use knowledge in a different context. By explicitly representing this knowledge, control decisions can be formulated as inference tasks which can be sent as queries to a knowledge base. This allows the robot to take all information it has at query time into account to generate answers, leading to better flexibility, adaptability to changing situations, robustness, and the ability to re-use knowledge once acquired. In this paper, we report on our work towards a practical and grounded knowledge representation and inference system. The system is specifically designed to meet the challenges created by using knowledge processing techniques on autonomous robots, including specialized inference methods, grounding of symbolic knowledge in the robot's control structures, and the acquisition of the different kinds of knowledge a robot needs.