Data Complexity in Expressive Description Logics With Path Expressions
- URL: http://arxiv.org/abs/2406.07095v1
- Date: Tue, 11 Jun 2024 09:37:51 GMT
- Title: Data Complexity in Expressive Description Logics With Path Expressions
- Authors: Bartosz Bednarczyk,
- Abstract summary: We investigate the data complexity of the satisfiability problem for the expressive description logic ZOIQ (a.k.a. ALCHb Self reg OIQ) over quasi-forests.
This completes the data complexity landscape for decidable fragments of ZOIQ, and reproves known results on decidable fragments of OWL2 (SR family)
- Score: 7.832189413179361
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate the data complexity of the satisfiability problem for the very expressive description logic ZOIQ (a.k.a. ALCHb Self reg OIQ) over quasi-forests and establish its NP-completeness. This completes the data complexity landscape for decidable fragments of ZOIQ, and reproves known results on decidable fragments of OWL2 (SR family). Using the same technique, we establish coNEXPTIME-completeness (w.r.t. the combined complexity) of the entailment problem of rooted queries in ZIQ.
Related papers
- RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs [12.846097618151951]
We develop a dataset for LLMs Complex Reasoning over Textual Knowledge Graphs (RiTeK) with a broad topological structure coverage.
We synthesize realistic user queries that integrate diverse topological structures, annotated information, and complex textual descriptions.
We introduce an enhanced Monte Carlo Tree Search (CTS) method, which automatically extracts relational path information from textual graphs for specific queries.
arXiv Detail & Related papers (2024-10-17T19:33:37Z) - Seek and Solve Reasoning for Table Question Answering [49.006950918895306]
This paper improves Table-based Question Answering (TQA) performance by leveraging Large Language Models' reasoning capabilities.
Inspired by how humans solve TQA tasks, we propose a Seek-and-seek pipeline that instructs the LLM to first seek relevant information and then answer questions.
We present a compact single-stage TQA-solving prompt distilled from the pipeline.
arXiv Detail & Related papers (2024-09-09T02:41:00Z) - Meta Operator for Complex Query Answering on Knowledge Graphs [58.340159346749964]
We argue that different logical operator types, rather than the different complex query types, are the key to improving generalizability.
We propose a meta-learning algorithm to learn the meta-operators with limited data and adapt them to different instances of operators under various complex queries.
Empirical results show that learning meta-operators is more effective than learning original CQA or meta-CQA models.
arXiv Detail & Related papers (2024-03-15T08:54:25Z) - Learning temporal formulas from examples is hard [2.8851756275902476]
We study the problem of learning linear temporal logic (LTL) formulas from examples.
We show that the learning problem is NP-complete, both for the full logic and for almost all of its fragments.
arXiv Detail & Related papers (2023-12-26T21:16:19Z) - When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities.
Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity.
Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
arXiv Detail & Related papers (2023-08-29T17:22:39Z) - Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability.
We develop a new dataset containing ten new types of queries with features that have never been considered.
Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
arXiv Detail & Related papers (2023-04-14T11:35:35Z) - Successive Prompting for Decomposing Complex Questions [50.00659445976735]
Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting.
We introduce Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution.
Our best model (with successive prompting) achieves an improvement of 5% absolute F1 on a few-shot version of the DROP dataset.
arXiv Detail & Related papers (2022-12-08T06:03:38Z) - Query2Particles: Knowledge Graph Reasoning with Particle Embeddings [49.64006979045662]
We propose a query embedding method to answer complex logical queries on knowledge graphs with missing edges.
The answer entities are selected according to the similarities between the entity embeddings and the query embedding.
A complex KG query answering method, Q2P, is proposed to retrieve diverse answers from different areas over the embedding space.
arXiv Detail & Related papers (2022-04-27T11:16:08Z) - From Conjunctive Queries to Instance Queries in Ontology-Mediated
Querying [17.79631575141597]
We consider ontology-mediated queries based on expressive description logics of the ALC family and (unions) of conjunctive queries.
Our results include exact characterizations of when such a rewriting is possible and tight complexity bounds for deciding rewritability.
arXiv Detail & Related papers (2020-10-22T16:40:59Z) - A tetrachotomy of ontology-mediated queries with a covering axiom [1.749935196721634]
Our concern is the problem of efficiently determining the data complexity of answering queries mediated by description and their optimal rewritings to standard database queries.
We focus on Boolean conjunctive-mediated queries called disjunctive sirups (or d-sirups)
Some d-sirups only have exponential-size resolution features, some only double-exponential-size positive existential existential-rewritings and single-exprecursive datalog rewritings.
arXiv Detail & Related papers (2020-06-07T14:47:07Z) - When is Ontology-Mediated Querying Efficient? [10.971122842236024]
We study the evaluation of ontology-mediated queries over relational databases.
We provide a characterization of the classes of OMQs that are tractable in combined complexity.
We also study the complexity of deciding whether a given OMQ is equivalent to an OMQ of bounded tree width.
arXiv Detail & Related papers (2020-03-17T16:32:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.