Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics

Authors: Oliver Fernández Gil, Anni-Yasmin Turhan6340-6348

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Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper we extend an approach for answering RPQs over graph databases that uses weighted transducers to approximate paths from the query in two ways. The first extension is to answering approximate conjunctive two-way regular path queries (C2RPQs) over graph databases and the second is to answering C2RPQs over ELH and DL-Lite R ontologies. We provide results on the computational complexity of the underlying reasoning problems and devise approximate query answering algorithms.
Researcher Affiliation Academia Oliver Fern andez Gil, Anni-Yasmin Turhan Institute of Theoretical Computer Science, TU Dresden, Germany
Pseudocode Yes Algorithm 1 Answering 2RPQs under approx. semantics. Input: K=(T , A), T, q=R(x, z), a, b Ind(A) and µ N. Output: yes iff (a, b, ηa,b) cert T(q, K) and ηa,b µ. ... Algorithm 2 Deciding τ-entailment for b C2RPQs. Input: A b C2RPQ q = R1(t1, t 1) . . . Rp(tp, t p), K = (T , A), a d T T, a p-ary combining function f and µ N. Output: yes iff ((), η) ans T,f(q, K) and η µ.
Open Source Code No No explicit statement or link was found indicating that the authors have released open-source code for the methodology described in this paper.
Open Datasets No The paper focuses on theoretical contributions, algorithm design, and computational complexity analysis in Description Logics. It does not conduct experiments with datasets, therefore no dataset availability information is provided.
Dataset Splits No The paper focuses on theoretical contributions and algorithm design. It does not involve empirical validation with dataset splits.
Hardware Specification No The paper focuses on theoretical results and algorithm design, not empirical experiments. Therefore, no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and describes algorithms and complexity results. It does not mention any specific software dependencies with version numbers for implementation or execution.
Experiment Setup No The paper is theoretical and describes algorithms and computational complexity. It does not include an experimental setup section with hyperparameters, training configurations, or other system-level settings for empirical evaluation.