Abstract
It is well known that manually formalizing a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, many researchers have developed algorithms and systems to help automate the process. Among them are systems that incorporate text corpora in the knowledge acquisition process. Here, we provide a novel method for unsupervised bottom-up ontology generation. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. To illustrate our approach, we provide three examples generating ontologies in diverse domains and validate them using qualitative and quantitative measures. The examples include the description of high-throughput screening data relevant to drug discovery and two custom text corpora. Our unsupervised method produces viable results with sometimes unexpected content. It is complementary to the typical top-down ontology development process. Our approach may therefore also be useful to domain experts.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 217-236 |
| Number of pages | 20 |
| Volume | 7123 LNAI |
| DOIs | |
| State | Published - Jan 30 2013 |
| Event | 6th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2010, Held as Part of the 9th International Semantic Web Conferences, ISWC 2010 and 1st International Workshop on Uncertainty in Description Logics, UniDL2010 - Shanghai, China Duration: Nov 7 2010 → Nov 7 2010 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7123 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 6th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2010, Held as Part of the 9th International Semantic Web Conferences, ISWC 2010 and 1st International Workshop on Uncertainty in Description Logics, UniDL2010 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 11/7/10 → 11/7/10 |
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Keywords
- Ontology Learning
- Ontology Modeling
- Probabilistic Methods
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
PrOntoLearn : Unsupervised lexico-semantic ontology generation using probabilistic methods. / Abeyruwan, Saminda; Visser, Ubbo E; Lemmon, Vance; Schuerer, Stephan C.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7123 LNAI 2013. p. 217-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7123 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - PrOntoLearn
T2 - Unsupervised lexico-semantic ontology generation using probabilistic methods
AU - Abeyruwan, Saminda
AU - Visser, Ubbo E
AU - Lemmon, Vance
AU - Schuerer, Stephan C
PY - 2013/1/30
Y1 - 2013/1/30
N2 - It is well known that manually formalizing a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, many researchers have developed algorithms and systems to help automate the process. Among them are systems that incorporate text corpora in the knowledge acquisition process. Here, we provide a novel method for unsupervised bottom-up ontology generation. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. To illustrate our approach, we provide three examples generating ontologies in diverse domains and validate them using qualitative and quantitative measures. The examples include the description of high-throughput screening data relevant to drug discovery and two custom text corpora. Our unsupervised method produces viable results with sometimes unexpected content. It is complementary to the typical top-down ontology development process. Our approach may therefore also be useful to domain experts.
AB - It is well known that manually formalizing a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck. Therefore, many researchers have developed algorithms and systems to help automate the process. Among them are systems that incorporate text corpora in the knowledge acquisition process. Here, we provide a novel method for unsupervised bottom-up ontology generation. It is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. To illustrate our approach, we provide three examples generating ontologies in diverse domains and validate them using qualitative and quantitative measures. The examples include the description of high-throughput screening data relevant to drug discovery and two custom text corpora. Our unsupervised method produces viable results with sometimes unexpected content. It is complementary to the typical top-down ontology development process. Our approach may therefore also be useful to domain experts.
KW - Ontology Learning
KW - Ontology Modeling
KW - Probabilistic Methods
UR - http://www.scopus.com/inward/record.url?scp=84872872474&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872872474&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35975-0-12
DO - 10.1007/978-3-642-35975-0-12
M3 - Conference contribution
SN - 9783642359743
VL - 7123 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 217
EP - 236
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -