Graph-based approaches to debugging and revision of terminologies in DL-Lite

Abstract

In this paper, we deal with the problem of debugging and revision of incoherent terminologies. Ontology debugging aims to provide the explanation of the causes of incoherence and ontology revision aims to eliminate the incoherence. For this purpose, we propose the graph-based approaches to deal with the debugging and revision of terminologies for a family of lightweight ontology languages, DL-Lite. First of all, we transform DL-Lite ontologies to graphs. To deal with the problem of ontology debugging, we calculate the minimal incoherence-preserving subsets (MIPS) of an ontology by computing the minimal incoherence-preserving path-pairs (MIPP) based on the transformed graph. To deal with the problem of ontology revision, we propose the notion of revision state which separates the terminology of an ontology into two disjoint sets: the set of wanted axioms and the set of unwanted axioms. We further define a revision operator based on the revision state. Afterwards, two revision algorithms are proposed to instantiate the revision operator: one is based on a scoring function, and the other one is based on a hitting set tree. We implement these algorithms and conduct experiments of ontology debugging and ontology revision on several adapted real ontologies. The experimental results of ontology debugging show that our approach of calculating MIPS based on graph is efficient and outperforms the state of the art. The experimental results of ontology revision show that the algorithm based on a scoring function is more efficient than the algorithm based on a hitting set tree.

Resolving Logical Contradictions in Description Logic Ontologies Based on Integer Linear Programming

“…This algorithm corresponds to HstSwoop in our experiments. The work in [30] provides a graph-based algorithm to compute conflicts and then apply the HST algorithm to find solutions.…”

“…Obviously, the solution consisting of all removed axioms could not be minimal. The work in [30] also provides a scoring function to select axioms for revising an ontology. There are several works that deal with inconsistency or incoherence for the tasks of ontology learning [12], [23] and ontology versions [13].…”

Ontology Alignment Repair Through 0-1 Programming

“…So, define Degree(cn) = |{cn |(cn, cn ) ∈ AE}|, where |Set| represents the number of elements in Set. The influence of the axiom on the depth of ontology knowledge can be calculated by (10).…”

“…This method can calculate all the irredundant strategies according to the conflict sets, so it is widely used in solving the best repair strategy. The papers [5]- [10] choose the repair strategy by comparing the merits of the minimum hit sets. These methods introduce different evaluation system of axiom and calculates the weight of the axioms to evaluate and guide the selection of the repair strategy.…”

Discriminative predicate path mining for fact checking in knowledge graphs

“…Discriminative Path Generation. Although meta paths have been used in many methods such as similarity search, clustering, semi-supervised learning, and link prediction [12-14, 26, 27], these algorithms either require human annotated meta paths [15] or enumerate all possible meta paths in the graph. Recently efforts have been made to meta path discovery and association rule mining in concept graphs [16], but most of the approaches have their own limitations.…”