Reverse Engineering of Temporal Queries Mediated by LTL Ontologies
- URL: http://arxiv.org/abs/2305.01248v2
- Date: Thu, 4 May 2023 19:20:26 GMT
- Title: Reverse Engineering of Temporal Queries Mediated by LTL Ontologies
- Authors: Marie Fortin, Boris Konev, Vladislav Ryzhikov, Yury Savateev, Frank
Wolter, Michael Zakharyaschev
- Abstract summary: In reverse engineering of database queries, we aim to construct a query from a given set of answers and non-answers.
We investigate this query-by-example problem for queries formulated in positive fragments of linear temporal logic over timestamped data.
- Score: 8.244587597395936
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In reverse engineering of database queries, we aim to construct a query from
a given set of answers and non-answers; it can then be used to explore the data
further or as an explanation of the answers and non-answers. We investigate
this query-by-example problem for queries formulated in positive fragments of
linear temporal logic LTL over timestamped data, focusing on the design of
suitable query languages and the combined and data complexity of deciding
whether there exists a query in the given language that separates the given
answers from non-answers. We consider both plain LTL queries and those mediated
by LTL-ontologies.
Related papers
- DAGE: DAG Query Answering via Relational Combinator with Logical Constraints [24.60431781360608]
We propose a query embedding method for DAG queries called DAGE.
DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator.
We show that it is possible to implement DAGE on top of existing query embedding methods.
arXiv Detail & Related papers (2024-10-29T15:02:48Z) - Enhancing Long Context Performance in LLMs Through Inner Loop Query Mechanism [2.919891871101241]
Transformers have a quadratic scaling of computational complexity with input size.
Retrieval-augmented generation (RAG) can better handle longer contexts by using a retrieval system.
We introduce a novel approach, Inner Loop Memory Augmented Tree Retrieval (ILM-TR)
arXiv Detail & Related papers (2024-10-11T19:49:05Z) - UQE: A Query Engine for Unstructured Databases [71.49289088592842]
We investigate the potential of Large Language Models to enable unstructured data analytics.
We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
arXiv Detail & Related papers (2024-06-23T06:58:55Z) - Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA)
We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity.
We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
arXiv Detail & Related papers (2024-03-21T13:52:30Z) - Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs.
SQL for No-Code Access to Relational Databases [2.933060994339853]
We investigate the potential of an alternative query language with simpler syntax and modular specification of complex queries.
The proposed alternative query language is called Query Plan Language (QPL)
We present ways to address the challenge of complex queries in an iterative, user-controlled manner.
arXiv Detail & Related papers (2023-12-22T16:16:15Z) - Decomposing Complex Queries for Tip-of-the-tongue Retrieval [72.07449449115167]
Complex queries describe content elements (e.g., book characters or events), information beyond the document text.
This retrieval setting, called tip of the tongue (TOT), is especially challenging for models reliant on lexical and semantic overlap between query and document text.
We introduce a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.
arXiv Detail & Related papers (2023-05-24T11:43:40Z) - Allies: Prompting Large Language Model with Beam Search [107.38790111856761]
In this work, we propose a novel method called ALLIES.
Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query.
By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly through retrieval.
arXiv Detail & Related papers (2023-05-24T06:16:44Z) - Searching for Better Database Queries in the Outputs of Semantic Parsers [16.221439565760058]
In this paper, we consider the case when, at the test time, the system has access to an external criterion that evaluates the generated queries.
The criterion can vary from checking that a query executes without errors to verifying the query on a set of tests.
We apply our approach to the state-of-the-art semantics and report that it allows us to find many queries passing all the tests on different datasets.
arXiv Detail & Related papers (2022-10-13T17:20:45Z) - 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) - Bounded-Memory Criteria for Streams with Application Time [0.0]
Bounded-memory computability continues to be in the focus of those areas of AI and databases that deal with feasible computations over streams.
This work presents criteria for bounded-memory computability of select-project-join (SPJ) queries over streams with application time.
arXiv Detail & Related papers (2020-07-30T12:05:04Z) - Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers.
We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
arXiv Detail & Related papers (2020-05-24T11:37:22Z)
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.