
The focus of this introduction to this special issue is to draw a picture as comprehensive as possible about various dimensions of inconsistency. In particular, we consider: (1) levels of knowledge at which inconsistency occurs; (2) categories and morphologies of inconsistency; (3) causes of inconsistency; (4) circumstances of inconsistency; (5) persistency of inconsistency; (6) consequences of inconsistency; (7) metrics for inconsistency; (8) theories for handling inconsistency; (9) dependencies among occurrences of inconsistency; and (10) problem domains where inconsistency has been studied. The take-home message is that inconsistency is ubiquitous and handling inconsistency is consequential in our endeavors. How to manage and reason in the presence of inconsistency presents a very important issue in semantic computing, cloud computing, social computing, and many other data-rich or knowledge-rich computing systems.
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], metrics for inconsistency, Knowledge representation, dimensions of inconsistency, Reasoning under uncertainty in the context of artificial intelligence, inconsistency, Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], metrics for inconsistency, Knowledge representation, dimensions of inconsistency, Reasoning under uncertainty in the context of artificial intelligence, inconsistency, Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 16 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
