Name Disambiguation Data

中文版

Name ambiguity has long been viewed as a challenging problem in many applications, such as scientific literature management, people search, and social network analysis. When we search a person name in these systems, many documents (e.g., papers, webpages) containing that person’s name may be returned. Which documents are about the person we care about? Although much research has been conducted, the problem remains largely unsolved, especially with the rapid growth of the people information available on the Web.

This page collects three name disambiguation data sets from AMiner. The three data sets can be used for research of name disambiguation, object resolution, etc. More specifically, V1 and V2 are two data sets labeled manually, but relatively small. V3 is larger, but may contain minor noise. Interested readers please refer to the following literature [1-4] for more details.

PPT [OneDrive] [Baidu Pan] PDF [OneDrive] [Baidu Pan]

na-v1 [2][3] [Simple Version Download, Readme] [Rich Vesion Download, Readme]

This data set is used for studying name disambiguation in digital library. It contains 110 author names and their disambiguation results (ground truth). Each author name corresponds to a raw file in the "raw-data" folder and an answer file (ground truth) in the "Answer" folder. (The simple version does not contain "citation", "co-affiliation-occur", "homepage". Refer to our ICDM 2011 paper for the definition of these features.)

- Raw file: the raw file is formatted as a XML file. In the XML file, the author name is associated with a number of publications. An example of a publication is as follow:
"
  <publication>
		<title>Explanation-based Failure Recovery</title>
		<year>1987</year>
		<authors>Ajay Gupta</authors>
		<jconf>AAAI</jconf>
		<id>13048</id>
		<label>0</label>
		<organization>null</organization>
	</publication>
"	
where <title> denotes the title of the publication; 
<year> denotes the publication year;
<jconf> denotes the publication venue;
<id> denotes the publication id;
<label> denotes the labeled person, e.g., all publications with "<label>0</label>" can be considered as published by the same person;
<organization> denotes the affiliation of the author(s).


- Answer file: the answer file is the ground truth. It is actually extracted from the raw-file by viewing publications with the same "<label>0</label>" as a person. The format is in plain text. The following is an example:
"
#Ajay Gupta
#1:13048 388794 596099 1265282 1179332 675629 39153 258611
#2:988870 1490190
#3:1393934
#4:1398544
#5:1739014
#6:1671104 515636 1678096
#7:1126381 1205032 275987 277587 276300 1549674 1034401
#8:600181 846439 149270 175996 264268 264291 299548 1384744 300057 302056 545651 1212517
#9:1316053
"
where the first line denotes the author name and each of the following line indicates a disambiguate person. For example the first line indicates that an author published 8 papers. The corresponding IDs of those papers are respectively 13048, 388794, 596099, 1265282, 1179332, 675629, 39153, 258611.

na-v2 [4] [Download]

This data set contains 4 files.

1. test_name_list.json: This json array file contains all author names.

2. name_to_pubs_train_500.json: This file can be used for training data, which includes name-person-paper mapping relations.

Data schema: This file is a dictionary (denoted as dic1) saved as a json object. The keys of dic1 are author name. The values of dic1 are person dictionary (denoted as dic2). The keys of dic2 are person id. The values of dic2 are list of paper ID authored by this person.

3. name_to_pubs_test_100.json: This file can be used for testing data, which includes name-person-paper mapping relations. Its data schema is the same as name_to_pubs_train_500.json.

4. pubs_raw.json: This file contains all papers mentioned in other files as a JSON object.

Data schema: This file is a dictionary, whose keys are paper ID and whose values are coresponding papers. The data schema of each paper is:

This data set is used in [4]. In our paper, we filter persons whose total number of papers is less than 5.

na-v3 [4] [Download]

This data set contains 2 files. It is a larger version of na-v2.

1. name_to_pubs_test_large.json: This file includes name-person-paper mapping relations. Its data schema is the same as name_to_pubs_train_500.json above in na-v2.

2. papers.txt: This file contains all papers mentioned in name_to_pubs_test_large.json.

Data schema: Each line represents a paper as a JSON object. The data schema of each paper is the same as that of pubs_raw.json in na-v2.

[1] Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'2008). pp.990-998. [PDF] [Slides] [System] [API]

[2] Jie Tang, A.C.M. Fong, Bo Wang, and Jing Zhang. A Unified Probabilistic Framework for Name Disambiguation in Digital Library. IEEE Transaction on Knowledge and Data Engineering (TKDE), 2012, Volume 24, Issue 6, Pages 975-987. (if =2.236) [PDF] [Data]

[3] Xuezhi Wang, Jie Tang, Hong Cheng, and Philip S. Yu. ADANA: Active Name Disambiguation. In Proceedings of 2011 IEEE International Conference on Data Mining (ICDM'11), pages 794-803. [PDF] [Slides] [Code&Data&System]

[4] Yutao Zhang, Fanjin Zhang, Peiran Yao, and Jie Tang. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. In Proceedings of the Twenty-Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18). [PDF] [slides] [poster] [data & code] [video]