On Limited Conjunctions and Partial Features in Parameter-Tractable Feature Logics
Authors: Stephanie McIntyre, Alexander Borgida, David Toman, Grant Weddell2995-3002
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a new parameterized member of this family called CFDIkc, which adds the ability to use conjunctions on the left-hand side of subsumptions... The remaining technical contributions of this paper are to show that each of the following problems are parameter-tractable in k: 1. (parameter diagnosis) given an arbitrary CFDI TBox T and integer k, determining if T is a CFDIkc TBox; 2. (concept satisfiability) given a CFDIkc TBox and concept C, determining if C is satisfiable; 3. (knowledge base consistency) determining if a given CFDIkc knowledge base is consistent; and 4. (query answering in OBDA) computing the certain answers for conjunctive queries over a given CFDIkc knowledge base. |
| Researcher Affiliation | Academia | 1Cheriton School of Computer Science, University of Waterloo, Canada 2Department of Computer Science, Rutgers University, NJ, U.S.A. |
| Pseudocode | Yes | Figure 3: ABox Completion Rules for Completion T (A) and Figure 4: Query Rewriting Rules for { y | ψ} Fold T (Q). |
| Open Source Code | No | The paper is theoretical and does not mention any source code release. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with a dataset. Figure 2 is an illustrative example, not a dataset used for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |