This volumecontainsthe proceedingsof ICCS 2008, the 16thInternationalC- ference on Conceptual Structures (ICCS). The focus of the ICCS conference is the representation and analysis of conceptual knowledge. ICCS brings together researchers to explore novel ways that Conceptual Structures can be used. ConceptualStructuresaremotivated byC.S. Peirce'sExistentialGraphsand were popularized by J.F. Sowa in the 1980s. Over 16 years ICCS has increased its scope to include innovations from a range of theories and related Conceptual Structure practices, among them formal concept analysis and ontologies. The- fore, ICCS presents a family of Conceptual Structure approaches that build on techniques derived from arti?cial intelligence, knowledge representation, applied mathematics and lattice theory, computational linguistics, conceptual modeling, intelligent systems and knowledge management. This volume's title -Knowledge Visualization and Reasoning - is intended to highlight the shared origins of Conceptual Structures with other visual forms of reasoning. J. Howse's invited survey paper "Diagrammatic Reasoning Systems" sets the scene for this theme, and severalother papers in the volume extend and reinforce these connections. The regular papers in this LNAI volume are split between theoretical and appliedcontributions.ICCShas traditionsinpracticalsystems sothe conference includestheone-dayConceptualStructuresToolInteroperabilityWorkshop(CS- TIW 2008) - published as a separate proceedings in the CEUR-WS. Both ICCS 2008 workshop and conference programhighlight results achieved with a variety of Conceptual Structures-based software.

This book constitutes the proceedings of the 22th International Conference on Conceptual Structures, ICCS 2016, held in Annecy, France, in July 2016. The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They are organized around the following topical sections: time representation; graphs and networks; formal concept analysis; ontologies and linked data.