Ontology-informed Lattice Reduction Using the Discrimination Power IndexCitation formats

Standard

Ontology-informed Lattice Reduction Using the Discrimination Power Index. / Quboa, Qudamah; Behnaz, Ali; Mehandjiev, Nikolay; Rabhi, Fethi.

24th International Conference on Conceptual Structures (ICCS2019). Springer Nature, 2019.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Quboa, Q, Behnaz, A, Mehandjiev, N & Rabhi, F 2019, Ontology-informed Lattice Reduction Using the Discrimination Power Index. in 24th International Conference on Conceptual Structures (ICCS2019). Springer Nature. https://doi.org/10.1007/978-3-030-23182-8_12

APA

Quboa, Q., Behnaz, A., Mehandjiev, N., & Rabhi, F. (2019). Ontology-informed Lattice Reduction Using the Discrimination Power Index. In 24th International Conference on Conceptual Structures (ICCS2019) Springer Nature. https://doi.org/10.1007/978-3-030-23182-8_12

Vancouver

Quboa Q, Behnaz A, Mehandjiev N, Rabhi F. Ontology-informed Lattice Reduction Using the Discrimination Power Index. In 24th International Conference on Conceptual Structures (ICCS2019). Springer Nature. 2019 https://doi.org/10.1007/978-3-030-23182-8_12

Author

Quboa, Qudamah ; Behnaz, Ali ; Mehandjiev, Nikolay ; Rabhi, Fethi. / Ontology-informed Lattice Reduction Using the Discrimination Power Index. 24th International Conference on Conceptual Structures (ICCS2019). Springer Nature, 2019.

Bibtex

@inproceedings{1204fca3797e43d3a2fd5dc64900997c,
title = "Ontology-informed Lattice Reduction Using the Discrimination Power Index",
abstract = "The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.",
keywords = "FCA, Semantic structures, Lattice reduction",
author = "Qudamah Quboa and Ali Behnaz and Nikolay Mehandjiev and Fethi Rabhi",
year = "2019",
month = jun,
day = "19",
doi = "10.1007/978-3-030-23182-8_12",
language = "English",
booktitle = "24th International Conference on Conceptual Structures (ICCS2019)",
publisher = "Springer Nature",
address = "United States",

}

RIS

TY - GEN

T1 - Ontology-informed Lattice Reduction Using the Discrimination Power Index

AU - Quboa, Qudamah

AU - Behnaz, Ali

AU - Mehandjiev, Nikolay

AU - Rabhi, Fethi

PY - 2019/6/19

Y1 - 2019/6/19

N2 - The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.

AB - The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extraction of structure from a large number of instances usually leads to a lattice which is too complex for practical use. Algorithms to reduce the lattice exist. However, these mainly rely on the lattice structure and are agnostic about any prior knowledge about the domain. In contrast, this paper uses existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.

KW - FCA

KW - Semantic structures

KW - Lattice reduction

U2 - 10.1007/978-3-030-23182-8_12

DO - 10.1007/978-3-030-23182-8_12

M3 - Conference contribution

BT - 24th International Conference on Conceptual Structures (ICCS2019)

PB - Springer Nature

ER -