AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data

Marina Danilevsky; Chi Wang; Fangbo Tao; Son Nguyen; Gong Chen; Nihit Desai; Lidan Wang; Jiawei Han

doi:10.1145/2487575.2487716

AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data

Marina Danilevsky, Chi Wang, Fangbo Tao, Son Nguyen, Gong Chen, Nihit Desai, Lidan Wang, Jiawei Han

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

Abstract

In this demo we present AMETHYST, a system for explor- ing and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed top- ical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.

Original language	English (US)
Title of host publication	KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editors	Rajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
Publisher	Association for Computing Machinery
Pages	1458-1461
Number of pages	4
ISBN (Electronic)	9781450321747
DOIs	https://doi.org/10.1145/2487575.2487716
State	Published - Aug 11 2013
Event	19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States Duration: Aug 11 2013 → Aug 14 2013

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	Part F128815

Other

Other	19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country	United States
City	Chicago
Period	8/11/13 → 8/14/13

Keywords

Entity mining
Heterogeneous network
Network analysis
Topic modeling

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/2487575.2487716

Fingerprint Dive into the research topics of 'AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data'. Together they form a unique fingerprint.

Cite this

Danilevsky, M., Wang, C., Tao, F., Nguyen, S., Chen, G., Desai, N., Wang, L., & Han, J. (2013). AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data. In R. Parekh, J. He, D. S. Inderjit, P. Bradley, Y. Koren, R. Ghani, T. E. Senator, R. L. Grossman, & R. Uthurusamy (Eds.), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1458-1461). [2487716] (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815). Association for Computing Machinery. https://doi.org/10.1145/2487575.2487716

AMETHYST : A system for mining and exploring topical hierarchies of heterogeneous data. / Danilevsky, Marina; Wang, Chi; Tao, Fangbo; Nguyen, Son; Chen, Gong; Desai, Nihit; Wang, Lidan; Han, Jiawei.

KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / Rajesh Parekh; Jingrui He; Dhillon S. Inderjit; Paul Bradley; Yehuda Koren; Rayid Ghani; Ted E. Senator; Robert L. Grossman; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. p. 1458-1461 2487716 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815).

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

Danilevsky, M, Wang, C, Tao, F, Nguyen, S, Chen, G, Desai, N, Wang, L & Han, J 2013, AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data. in R Parekh, J He, DS Inderjit, P Bradley, Y Koren, R Ghani, TE Senator, RL Grossman & R Uthurusamy (eds), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., 2487716, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F128815, Association for Computing Machinery, pp. 1458-1461, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, United States, 8/11/13. https://doi.org/10.1145/2487575.2487716

Danilevsky M, Wang C, Tao F, Nguyen S, Chen G, Desai N et al. AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data. In Parekh R, He J, Inderjit DS, Bradley P, Koren Y, Ghani R, Senator TE, Grossman RL, Uthurusamy R, editors, KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2013. p. 1458-1461. 2487716. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2487575.2487716

Danilevsky, Marina ; Wang, Chi ; Tao, Fangbo ; Nguyen, Son ; Chen, Gong ; Desai, Nihit ; Wang, Lidan ; Han, Jiawei. / AMETHYST : A system for mining and exploring topical hierarchies of heterogeneous data. KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / Rajesh Parekh ; Jingrui He ; Dhillon S. Inderjit ; Paul Bradley ; Yehuda Koren ; Rayid Ghani ; Ted E. Senator ; Robert L. Grossman ; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. pp. 1458-1461 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{cdbac297040a49358f0d41a3b67633f2,

title = "AMETHYST: A system for mining and exploring topical hierarchies of heterogeneous data",

abstract = "In this demo we present AMETHYST, a system for explor- ing and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed top- ical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.",

keywords = "Entity mining, Heterogeneous network, Network analysis, Topic modeling",

author = "Marina Danilevsky and Chi Wang and Fangbo Tao and Son Nguyen and Gong Chen and Nihit Desai and Lidan Wang and Jiawei Han",

note = "Funding Information: 6. ACKNOWLEDGMENTS This work was supported in part by the U.S. National Science Foundation grants IIS–0905215, CNS-0931975, and IIS-1017362; U.S. Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA); and IIS-1017362, U.S. Air Force Office of Scientific Research MURI award FA9550-08-1-0265, and MIAS, a DHS-IDS Center for Multimodal Information Access and Synthesis at UIUC. Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Research Fellowship grant NSF DGE 07-15088.; 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 ; Conference date: 11-08-2013 Through 14-08-2013",

year = "2013",

month = aug,

day = "11",

doi = "10.1145/2487575.2487716",

language = "English (US)",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "1458--1461",

editor = "Rajesh Parekh and Jingrui He and Inderjit, {Dhillon S.} and Paul Bradley and Yehuda Koren and Rayid Ghani and Senator, {Ted E.} and Grossman, {Robert L.} and Ramasamy Uthurusamy",

booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - AMETHYST

T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013

AU - Danilevsky, Marina

AU - Wang, Chi

AU - Tao, Fangbo

AU - Nguyen, Son

AU - Chen, Gong

AU - Desai, Nihit

AU - Wang, Lidan

AU - Han, Jiawei

N1 - Funding Information: 6. ACKNOWLEDGMENTS This work was supported in part by the U.S. National Science Foundation grants IIS–0905215, CNS-0931975, and IIS-1017362; U.S. Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA); and IIS-1017362, U.S. Air Force Office of Scientific Research MURI award FA9550-08-1-0265, and MIAS, a DHS-IDS Center for Multimodal Information Access and Synthesis at UIUC. Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Research Fellowship grant NSF DGE 07-15088.

PY - 2013/8/11

Y1 - 2013/8/11

N2 - In this demo we present AMETHYST, a system for explor- ing and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed top- ical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.

AB - In this demo we present AMETHYST, a system for explor- ing and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN). HINs, composed of multiple types of entities and links are very common in the real world. Many have a text component, and thus can benefit from a high quality hierarchical organization of the topics in the network dataset. By organizing the topics into a hierarchy, AMETHYST helps understand search results in the context of an ontology, and explain entity relatedness at different granularities. The automatically constructed top- ical hierarchy reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.

KW - Entity mining

KW - Heterogeneous network

KW - Network analysis

KW - Topic modeling

UR - http://www.scopus.com/inward/record.url?scp=84938107404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938107404&partnerID=8YFLogxK

U2 - 10.1145/2487575.2487716

DO - 10.1145/2487575.2487716

M3 - Conference contribution

AN - SCOPUS:84938107404

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 1458

EP - 1461

BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

A2 - Parekh, Rajesh

A2 - He, Jingrui

A2 - Inderjit, Dhillon S.

A2 - Bradley, Paul

A2 - Koren, Yehuda

A2 - Ghani, Rayid

A2 - Senator, Ted E.

A2 - Grossman, Robert L.

A2 - Uthurusamy, Ramasamy

PB - Association for Computing Machinery

Y2 - 11 August 2013 through 14 August 2013

ER -