Generating literature-based knowledge discoveries in life sciences using relationship associations

Steven B. Kraines, Weisen Guo, Daisuke Hoshiyama, Haruo Mizutani, Toshihisa Takagi

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

The life sciences have been a pioneering discipline for the field of knowledge discovery, since the literaturebased discoveries by Swanson three decades ago. Existing literature-based knowledge discovery techniques generally try to discover hitherto unknown associations of domain concepts based on associations that can be established from the literature. However, scientific facts are more often expressed as specific relationships between concepts and/or entities that have been established through scientific research. A pair of relationships that predicate the specific way in which one concept relates to another can be associated if one of the concepts from each relationship can be determined to be semantically equivalent; we call this a "relationship association". Then, by making the same assumption of the transitivity of association used by Swanson and others, we can generate a hypothetical relationship association by combining two relationship associations that have been extracted from a knowledge base. Here we describe an algorithm for generating potential knowledge discoveries in the form of new relationship associations that are implied but not actually stated, and we test the algorithm against a corpus of almost 5000 relationship associations that we have extracted in previous work from 392 semantic graphs representing research articles from MEDLINE.

Original languageEnglish
Title of host publicationKDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Pages35-44
Number of pages10
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2010 - Valencia, Spain
Duration: 25 Oct 201028 Oct 2010

Conference

ConferenceInternational Conference on Knowledge Discovery and Information Retrieval, KDIR 2010
CountrySpain
CityValencia
Period25/10/1028/10/10

Fingerprint

Data mining
Semantics

Cite this

Kraines, S. B., Guo, W., Hoshiyama, D., Mizutani, H., & Takagi, T. (2010). Generating literature-based knowledge discoveries in life sciences using relationship associations. In KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (pp. 35-44) https://doi.org/10.5220/0003068100350044
Kraines, Steven B. ; Guo, Weisen ; Hoshiyama, Daisuke ; Mizutani, Haruo ; Takagi, Toshihisa. / Generating literature-based knowledge discoveries in life sciences using relationship associations. KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. 2010. pp. 35-44
@inproceedings{f3575cf64fcb4dc386ecb6ca3d86f1dd,
title = "Generating literature-based knowledge discoveries in life sciences using relationship associations",
abstract = "The life sciences have been a pioneering discipline for the field of knowledge discovery, since the literaturebased discoveries by Swanson three decades ago. Existing literature-based knowledge discovery techniques generally try to discover hitherto unknown associations of domain concepts based on associations that can be established from the literature. However, scientific facts are more often expressed as specific relationships between concepts and/or entities that have been established through scientific research. A pair of relationships that predicate the specific way in which one concept relates to another can be associated if one of the concepts from each relationship can be determined to be semantically equivalent; we call this a {"}relationship association{"}. Then, by making the same assumption of the transitivity of association used by Swanson and others, we can generate a hypothetical relationship association by combining two relationship associations that have been extracted from a knowledge base. Here we describe an algorithm for generating potential knowledge discoveries in the form of new relationship associations that are implied but not actually stated, and we test the algorithm against a corpus of almost 5000 relationship associations that we have extracted in previous work from 392 semantic graphs representing research articles from MEDLINE.",
author = "Kraines, {Steven B.} and Weisen Guo and Daisuke Hoshiyama and Haruo Mizutani and Toshihisa Takagi",
year = "2010",
doi = "10.5220/0003068100350044",
language = "English",
isbn = "9789898425287",
pages = "35--44",
booktitle = "KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",

}

Kraines, SB, Guo, W, Hoshiyama, D, Mizutani, H & Takagi, T 2010, Generating literature-based knowledge discoveries in life sciences using relationship associations. in KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. pp. 35-44, International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010, Valencia, Spain, 25/10/10. https://doi.org/10.5220/0003068100350044

Generating literature-based knowledge discoveries in life sciences using relationship associations. / Kraines, Steven B.; Guo, Weisen; Hoshiyama, Daisuke; Mizutani, Haruo; Takagi, Toshihisa.

KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. 2010. p. 35-44.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Generating literature-based knowledge discoveries in life sciences using relationship associations

AU - Kraines, Steven B.

AU - Guo, Weisen

AU - Hoshiyama, Daisuke

AU - Mizutani, Haruo

AU - Takagi, Toshihisa

PY - 2010

Y1 - 2010

N2 - The life sciences have been a pioneering discipline for the field of knowledge discovery, since the literaturebased discoveries by Swanson three decades ago. Existing literature-based knowledge discovery techniques generally try to discover hitherto unknown associations of domain concepts based on associations that can be established from the literature. However, scientific facts are more often expressed as specific relationships between concepts and/or entities that have been established through scientific research. A pair of relationships that predicate the specific way in which one concept relates to another can be associated if one of the concepts from each relationship can be determined to be semantically equivalent; we call this a "relationship association". Then, by making the same assumption of the transitivity of association used by Swanson and others, we can generate a hypothetical relationship association by combining two relationship associations that have been extracted from a knowledge base. Here we describe an algorithm for generating potential knowledge discoveries in the form of new relationship associations that are implied but not actually stated, and we test the algorithm against a corpus of almost 5000 relationship associations that we have extracted in previous work from 392 semantic graphs representing research articles from MEDLINE.

AB - The life sciences have been a pioneering discipline for the field of knowledge discovery, since the literaturebased discoveries by Swanson three decades ago. Existing literature-based knowledge discovery techniques generally try to discover hitherto unknown associations of domain concepts based on associations that can be established from the literature. However, scientific facts are more often expressed as specific relationships between concepts and/or entities that have been established through scientific research. A pair of relationships that predicate the specific way in which one concept relates to another can be associated if one of the concepts from each relationship can be determined to be semantically equivalent; we call this a "relationship association". Then, by making the same assumption of the transitivity of association used by Swanson and others, we can generate a hypothetical relationship association by combining two relationship associations that have been extracted from a knowledge base. Here we describe an algorithm for generating potential knowledge discoveries in the form of new relationship associations that are implied but not actually stated, and we test the algorithm against a corpus of almost 5000 relationship associations that we have extracted in previous work from 392 semantic graphs representing research articles from MEDLINE.

UR - http://www.scopus.com/inward/record.url?scp=78651433172&partnerID=8YFLogxK

U2 - 10.5220/0003068100350044

DO - 10.5220/0003068100350044

M3 - Conference contribution

SN - 9789898425287

SP - 35

EP - 44

BT - KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

ER -

Kraines SB, Guo W, Hoshiyama D, Mizutani H, Takagi T. Generating literature-based knowledge discoveries in life sciences using relationship associations. In KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. 2010. p. 35-44 https://doi.org/10.5220/0003068100350044