Ontology-Based Query Answering for Probabilistic Temporal Data
Authors: Patrick Koopmann2903-2910
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate ontology-based query answering for data that are both temporal and probabilistic... We present a framework that allows to represent temporal probabilistic data, and introduce a query language with which complex temporal and probabilistic patterns can be described. Specifically, this language combines conjunctive queries with operators from linear time logic as well as probability operators. We analyse the complexities of evaluating queries in this language in various settings. |
| Researcher Affiliation | Academia | Patrick Koopmann Institute for Theoretical Computer Science Technische Universit at Dresden, Germany firstname.lastname@tu-dresden.de |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses 'Example 1' to illustrate the structure of a temporal probabilistic knowledge base, but it does not mention or provide access to any public datasets used for training or empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with datasets, and therefore, it does not provide information on training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not discuss any hardware used for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup, hyperparameters, or system-level training settings. |