ML Anthology

Streaming Multi-Context Systems

Minh Dao-Tran, Thomas Eiter
IJCAI 2017 pp. 1000-1007
doi:10.24963/IJCAI.2017/139 /ijcai/2017/daotran2017ijcai-streaming/

Abstract

Multi-Context Systems (MCS) are a powerful framework to interlink heterogeneous knowledge bases under equilibrium semantics. Recent extensions of MCS to dynamic data settings either abstract from computing time, or abandon a dynamic equilibrium semantics. We thus present streaming MCS, which have a run-based semantics that accounts for asynchronous, distributed execution and supports obtaining equilibria for contexts in cyclic exchange (avoiding infinite loops); moreover, they equip MCS with native stream reasoning features. Ad-hoc query answering is NP-complete while prediction is PSpace-complete in relevant settings (but undecidable in general); tractability results for suitable restrictions.

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Cite

Text

Dao-Tran and Eiter. "Streaming Multi-Context Systems." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/139

Markdown

[Dao-Tran and Eiter. "Streaming Multi-Context Systems." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/daotran2017ijcai-streaming/) doi:10.24963/IJCAI.2017/139

BibTeX

@inproceedings{daotran2017ijcai-streaming,
  title     = {{Streaming Multi-Context Systems}},
  author    = {Dao-Tran, Minh and Eiter, Thomas},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {1000-1007},
  doi       = {10.24963/IJCAI.2017/139},
  url       = {https://mlanthology.org/ijcai/2017/daotran2017ijcai-streaming/}
}