Simon Polovina

Conceptual Structures: Knowledge Architectures for Smart Applications

Invited Papers.- An Introduction to Conceptual Graphs.- Trikonic Inter-Enterprise Architectonic.- Hypermedia Discourse: Contesting Networks of Ideas and Arguments.- Dynamic Epistemic Logic and Knowledge Puzzles.- Peirce on Icons and Cognition.- Conceptual Graphs.- Using Cognitive Archetypes and Conceptual Graphs to Model Dynamic Phenomena in Spatial Environments.- A Datatype Extension for Simple Conceptual Graphs and Conceptual Graphs Rules.- A Knowledge Management Optimization Problem Using Marginal Utility in a Metric Space with Conceptual Graphs.- Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise.- An Inferential Approach to the Generation of Referring Expressions.- A Conceptual Graph Description of Medical Data for Brain Tumour Classification.- A Conceptual Graph Based Approach to Ontology Similarity Measure.- A Comparison of Different Conceptual Structures Projection Algorithms.- A Conceptual Graph Approach to Feature Modeling.- From Conceptual Structures to Semantic Interoperability of Content.- Formal Concept Analysis.- Faster Concept Analysis.- The Design Space of Information Presentation: Formal Design Space Analysis with FCA and Semiotics.- Reducing the Representation Complexity of Lattice-Based Taxonomies.- An FCA Perspective on n-Distributivity.- Towards a Semantology of Music.- Analysis of the Publication Sharing Behaviour in BibSonomy.- The MILL - Method for Informal Learning Logistics.- Bilingual Word Association Networks.- Using FCA for Encoding Closure Operators into Neural Networks.- Conceptual Structures.- Arc Consistency Projection: A New Generalization Relation for Graphs.- Mining Frequent Closed Unordered Trees Through Natural Representations.- Devolved Ontology for Smart Applications.- Historical and Conceptual Foundation of Diagrammatical Ontology.- Learning Common Outcomes of Communicative Actions Represented by Labeled Graphs.- Belief Flow in Assertion Networks.- Conceptual Fingerprints: Lexical Decomposition by Means of Frames - a Neuro-cognitive Model.- Constants and Functions in Peirces Existential Graphs.- Revelator Game of Inquiry: A Peircean Challenge for Conceptual Structures in Application and Evolution.- Short Papers.- Helping System Users to Be Smarter by Representing Logic in Transaction Frame Diagrams.- Quo Vadis, CS? - On the (non)-Impact of Conceptual Structures on the Semantic Web.- A Framework for Analyzing and Testing Overlapping Requirements with Actors in Conceptual Graphs.- Implementation of SPARQL Query Language Based on Graph Homomorphism.- Cooperative CG-Wrappers for Web Content Extraction.- Conceptual Graphs and Ontologies for Information Retrieval.- Representation Levels Within Knowledge Representation.- Supporting Lexical Ontology Learning by Relational Exploration.- Characterizing Implications of Injective Partial Orders.- DVDSleuth: A Case Study in Applied Formal Concept Analysis for Navigating Web Catalogs.- Navigation in Knowledge-Based System for Helpdesk Based on FCA.- Functorial Properties of Formal Concept Analysis.- Towards an Ontology to Conceptualize Solution Analysis Tasks in CSCL Environments.

Conceptual Structures for Discovering Knowledge

This book constitutes the proceedings of the 19th International Conference on Conceptual Structures, ICCS 2011, held in Derby, UK, in July 2011. The 18 full papers and 4 short papers presented together with 12 workshop papers were carefully reviewed and selected for inclusion in the book. The volume also contains 3 invited talks. ICCS focuses on the useful representation and analysis of conceptual knowledge with research and business applications. It advances the theory and practice in connecting the users conceptual approach to problem solving with the formal structures that computer applications need to bring their productivity to bear. Conceptual structures (CS) represent a family of approaches that builds on the successes of artificial intelligence, business intelligence, computational linguistics, conceptual modelling, information and Web technologies, user modelling, and knowledge management. Two of the workshops contained in this volume cover CS and knowledge discovery in under-traversed domains and in task specific information retrieval. The third addresses CD in learning, teaching and assessment.

From concepts to agents: towards a framework for multi-agent system modelling

Whilst tools assist the various tasks required to develop a multi-agent system (MAS), yet there still remains a gap between the generation of MAS models and program code. AUML development has enabled MAS designs to be specified in detail, including the complexities of agent communication protocols, which was a shortcoming of the Unified Modelling Language (UML) standard. However, the creation of MAS designs using AUML still requires a significant amount of design expertise on the part of the designer. We describe an approach to the development of a complex healthcare information system that defines specific steps along the path to MAS implementation. In particular we explore the use of conceptual knowledge modelling techniques by means of conceptual graphs and a transactions-based architecture for model verification during requirements gathering, together with a translation to AUML for design specification, proposing a framework to extend existing AOSE methodologies.

A mapping from conceptual graphs to formal concept analysis

A straightforward mapping from Conceptual Graphs (CGs) to Formal Concept Analysis (FCA) is presented. It is shown that the benefits of FCA can be added to those of CGs, in, for example, formally reasoning about a system design. In the mapping, a formal attribute in FCA is formed by combining a CG source concept with its relation. The corresponding formal object in FCA is the corresponding CG target concept. It is described how a CG, represented by triples of the form source-concept, relation, target-concept, can be transformed into a set of binary relations of the form (target-concept, source-concept ∧ relation) creating a formal context in FCA. An algorithm for the transformation is presented and for which there is a software implementation. The approach is compared to that of Wille. An example is given of a simple University Transaction Model (TM) scenario that demonstrates how FCA can be applied to CGs, combining the power of each in an integrated and intuitive way.

Visualising Computational Intelligence Through Converting Data into Formal Concepts

Formal Concept Analysis (FCA) is an emerging data technology that complements collective intelligence such as that identified in the Semantic Web by visualising the hidden meaning in disparate and distributed data. The paper demonstrates the discovery of these novel semantics through a set of FCA open source software tools FcaBedrock and In-Close that were developed by the authors. These tools add computational intelligence by converting data into a Boolean form called a Formal Context, prepare this data for analysis by creating focused and noise-free sub-Contexts and then analyse the prepared data using a visualisation called a Concept Lattice. The Formal Concepts thus visualised highlight how data itself contains meaning, and how FCA tools thereby extract data’s inherent semantics. The paper describes how this will be further developed in a project called CUBIST, to provide in-data-warehouse visual analytics for RDF-based triple stores.

Transaction agent modelling: from experts to concepts to multi-agent systems

Whilst the Multi-Agent System (MAS) paradigm has the potential to enable complex heterogeneous information systems to be integrated, there is a need to represent and specify the nature of qualitative conceptual transactions in order that they are adequately comprehended by a goal-directed MAS. Using the Transaction Agent Model (TrAM) approach we examine the use of Conceptual Graphs to model an extension to an existing MAS in the community healthcare domain, whereby the existing agent capabilities are augmented with a robust set of behaviours that provide emergency healthcare management. We illustrate how TrAM serves to enrichen the requirements gathering process, whilst also supporting the definition and realisation of quantitative measures for the management of qualitative transactions.

Enhancing the initial requirements capture of multi-agent systems through conceptual graphs

A key purpose of Multi-Agent Systems (MAS) is to assist humans make better decisions given the vast and disparate information that global systems such as the Web have enabled. The resulting popularity of Agent-Oriented Software Engineering (AOSE) thus demands a methodology that facilitates the development of robust, scalable MAS implementations that recognise real-world semantics. Using an exemplar in the Community Healthcare domain and Conceptual Graphs (CG), we describe an AOSE approach that elicits the hitherto hidden requirements of a system much earlier in the software development lifecycle, whilst also incorporating model-checking to ensure robustness. The resulting output is then available for translation into Agent Oriented Unified Modelling Language (AUML) and further developed using the agent development toolkit of choice.

Environmental Scanning and Knowledge Representation for the Detection of Organised Crime Threats

ePOOLICE aims at developing an efficient and effective strategic early warning system that utilises environmental scanning for the early warning and detection of current, emergent and future organised crime threats. Central to this concept is the use of environmental scanning to detect ‘weak signals’ in the external environment to monitor and identify emergent and future threats prior to their materialization into tangible criminal activity. This paper gives a brief overview of the application of textual concept extraction and categorization, and the Semantic Web technologies Formal Concept Analysis and Conceptual Graphs as part of the systems technological architecture, describing their benefits in aiding effective early warning.

A Transactions Pattern for Structuring Unstructured Corporate Information in Enterprise Applications

It is known that 80-85% of all corporate information remains unstructured. As such, many enterprises rely on information systems that cause them to risk transactions that are based on lack of information (errors of omission) or misleading information (errors of commission). To address this concern, the fundamental business concept of monetary transactions is extended to include qualitative business concepts. A Transaction Model (TM) is accordingly identified that provides a structure for these unstructured but vital aspects of business transactions. By highlighting how unstructured information can be integrated into transactions, the TM provides businesses with a much more balanced view of the transactions they engage in or to discover novel transactions that they might have otherwise missed. A simple example is provided that illustrates this integration and reveals a key missing element. This discovery points to a transactions pattern that can be used to ensure that all the parties (or agents) in a transaction are identified, as well as capturing unstructured and structured information into a coherent framework. In support of the TM as a pattern, more examples of its use in a variety of domains are given. A number of enterprise applications are suggested such as in multi-agent systems, document text capture, and knowledge management.

A Transaction-Oriented Architecture for Enterprise Systems

Many enterprises risk business transactions based on information systems that are incomplete or misleading, given that 80-85% of all corporate information remains outside of their processing scope. It highlights that the bulk of information is too unstructured for these systems to process, but must be taken into account if those systems are to provide effective support. Computer technology nonetheless continues to become more and more predominant, illustrated by SAP A.G. recognising that 65-70% of the worlds transactions are run using their technology. Using SAP as an illustrative case study, and by bringing in the benefits of technologies such as Service-Oriented Architecture SOA, Business Process Management BPM, Enterprise Architecture Frameworks EA and Conceptual Structures, a practical roadmap is identified to a Transaction-Oriented Architecture TOA that is predicated on the Transaction Concept. This concept builds upon the Resources-Events-Agents REA modelling pattern that is close to business reality. Enterprise systems can thus better incorporate that missing 80-85% of hitherto too-unstructured information thereby allowing enterprise systems vendors such as SAP, their competitors, customers, suppliers and partners to do an ever better job with the worlds transactions.