CQE in OWL 2 QL: A "Longest Honeymoon" Approach (extended version)
- URL: http://arxiv.org/abs/2207.11155v1
- Date: Fri, 22 Jul 2022 15:51:15 GMT
- Title: CQE in OWL 2 QL: A "Longest Honeymoon" Approach (extended version)
- Authors: Piero Bonatti, Gianluca Cima, Domenico Lembo, Lorenzo Marconi,
Riccardo Rosati, Luigi Sauro, Domenico Fabio Savo
- Abstract summary: We study a dynamic CQE method, namely, we propose to alter the answer to the current query based on the evaluation of previous ones.
We aim at a system that, besides being able to protect confidential data, is maximally cooperative.
- Score: 13.169982133542266
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Controlled Query Evaluation (CQE) has been recently studied in the context of
Semantic Web ontologies. The goal of CQE is concealing some query answers so as
to prevent external users from inferring confidential information. In general,
there exist multiple, mutually incomparable ways of concealing answers, and
previous CQE approaches choose in advance which answers are visible and which
are not. In this paper, instead, we study a dynamic CQE method, namely, we
propose to alter the answer to the current query based on the evaluation of
previous ones. We aim at a system that, besides being able to protect
confidential data, is maximally cooperative, which intuitively means that it
answers affirmatively to as many queries as possible; it achieves this goal by
delaying answer modifications as much as possible. We also show that the
behavior we get cannot be intensionally simulated through a static approach,
independent of query history. Interestingly, for OWL 2 QL ontologies and policy
expressed through denials, query evaluation under our semantics is first-order
rewritable, and thus in AC0 in data complexity. This paves the way for the
development of practical algorithms, which we also preliminarily discuss in the
paper.
Related papers
- Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs [51.33342412699939]
Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs.
Recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries.
We propose an effective Query Instruction Parsing (QIPP) that captures latent query patterns from code-like query instructions.
arXiv Detail & Related papers (2024-10-27T03:18:52Z) - Is Complex Query Answering Really Complex? [28.8459899849641]
We show that the current benchmarks for CQA are not really complex, and the way they are built distorts our perception of progress in this field.
We propose a set of more challenging benchmarks, composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs.
arXiv Detail & Related papers (2024-10-16T13:19:03Z) - S-EQA: Tackling Situational Queries in Embodied Question Answering [48.43453390717167]
We present and tackle the problem of Embodied Question Answering with Situational Queries (S-EQA) in a household environment.
We first introduce a novel Prompt-Generate-Evaluate scheme that wraps around an LLM's output to create a dataset of unique situational queries and corresponding consensus object information.
We report an improved accuracy of 15.31% while using queries framed from the generated object consensus for Visual Question Answering (VQA) over directly answering situational ones.
arXiv Detail & Related papers (2024-05-08T00:45:20Z) - Controlled Query Evaluation through Epistemic Dependencies [7.502796412126707]
We show the expressive abilities of our framework and study the data complexity of CQE for (unions of) conjunctive queries.
We prove tractability for the case of acyclic dependencies by providing a suitable query algorithm.
arXiv Detail & Related papers (2024-05-03T19:48:07Z) - NQE: N-ary Query Embedding for Complex Query Answering over
Hyper-Relational Knowledge Graphs [1.415350927301928]
Complex query answering is an essential task for logical reasoning on knowledge graphs.
We propose a novel N-ary Query Embedding (NQE) model for CQA over hyper-relational knowledge graphs (HKGs)
NQE utilizes a dual-heterogeneous Transformer encoder and fuzzy logic theory to satisfy all n-ary FOL queries.
We generate a new CQA dataset WD50K-NFOL, including diverse n-ary FOL queries over WD50K.
arXiv Detail & Related papers (2022-11-24T08:26:18Z) - DecAF: Joint Decoding of Answers and Logical Forms for Question
Answering over Knowledge Bases [81.19499764899359]
We propose a novel framework DecAF that jointly generates both logical forms and direct answers.
DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks.
arXiv Detail & Related papers (2022-09-30T19:51:52Z) - Counterfactual Variable Control for Robust and Interpretable Question
Answering [57.25261576239862]
Deep neural network based question answering (QA) models are neither robust nor explainable in many cases.
In this paper, we inspect such spurious "capability" of QA models using causal inference.
We propose a novel approach called Counterfactual Variable Control (CVC) that explicitly mitigates any shortcut correlation.
arXiv Detail & Related papers (2020-10-12T10:09:05Z) - 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) - Harvesting and Refining Question-Answer Pairs for Unsupervised QA [95.9105154311491]
We introduce two approaches to improve unsupervised Question Answering (QA)
First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA)
Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA.
arXiv Detail & Related papers (2020-05-06T15:56:06Z) - CQE in Description Logics Through Instance Indistinguishability
(extended version) [0.0]
We study privacy-preserving query answering in Description Logics (DLs)
We derive data complexity results query answering over DL-Lite$_mathcal$$.
We identify a semantically well-founded notion of approximated confidentiality answering for CQE.
arXiv Detail & Related papers (2020-04-24T17:28:24Z)
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.