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- research-article
Effective entity matching with transformers
Yuliang Li
Meta AI, Menlo Park, USA
,Jinfeng Li
Megagon Labs, Mountain View, USA
,Yoshi Suhara
Grammarly, San Francisco, USA
,AnHai Doan
https://ror.org/01y2jtd41University of Wisconsin Madison, Madison, USA
,Wang-Chiew Tan
Meta AI, Menlo Park, USA
The VLDB Journal — The International Journal on Very Large Data Bases, Volume 32, Issue 6•Nov 2023, pp 1215-1235 • https://doi.org/10.1007/s00778-023-00779-zAbstractWe present , a novel entity matching system based on pre-trained Transformer language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a ...
- 2Citation
MetricsTotal Citations2
- research-article
Published By ACM
Published By ACM
Annotating Columns with Pre-trained Language Models
Yoshihiko Suhara
Megagon Labs, Mountain View, CA, USA
,Jinfeng Li
Megagon Labs, Mountain View, CA, USA
,Yuliang Li
Megagon Labs, Mountain View, CA, USA
,Dan Zhang
Megagon Labs, Mountain View, CA, USA
,Çağatay Demiralp
Sigma Computing, San Fransisco, CA, USA
,Chen Chen
Megagon Labs, Mountain View, CA, USA
,Wang-Chiew Tan
Meta AI, Menlo Park, CA, USA
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data•June 2022, pp 1493-1503• https://doi.org/10.1145/3514221.3517906Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper, we study the problem of ...
- 24Citation
- 1,054
- Downloads
MetricsTotal Citations24Total Downloads1,054Last 12 Months364Last 6 weeks21
- research-article
Data augmentation for ML-driven data preparation and integration
Yuliang Li
Megagon Labs
,Xiaolan Wang
Megagon Labs
,Zhengjie Miao
Duke University
,Wang-Chiew Tan
Facebook AI
Proceedings of the VLDB Endowment, Volume 14, Issue 12•July 2021, pp 3182-3185 • https://doi.org/10.14778/3476311.3476403In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques ...
- 5Citation
- 373
- Downloads
MetricsTotal Citations5Total Downloads373Last 12 Months123Last 6 weeks8
- keynote
Published By ACM
Published By ACM
Deep Data Integration
Wang-Chiew Tan
Facebook AI, Menlo Park, CA, USA
SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data•June 2021, pp 2-2• https://doi.org/10.1145/3448016.3460534We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art ...
- 3Citation
- 397
- Downloads
MetricsTotal Citations3Total Downloads397Last 12 Months40Last 6 weeks1- 1
Supplementary Material3448016.3460534.mp4
- research-article
Published By ACM
Published By ACM
Adaptive Rule Discovery for Labeling Text Data
Sainyam Galhotra
University of Massachusetts Amherst, Amherst, MA, USA
,Behzad Golshan
Megagon Labs, Mountain View, CA, USA
,Wang-Chiew Tan
Facebook AI, Menlo Park, CA, USA
SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data•June 2021, pp 2217-2225• https://doi.org/10.1145/3448016.3457334Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further ...
- 5Citation
- 414
- Downloads
MetricsTotal Citations5Total Downloads414Last 12 Months47Last 6 weeks2- 1
Supplementary Material3448016.3457334.mp4
- research-article
Published By ACM
Published By ACM
Constructing Explainable Opinion Graphs from Reviews
Nofar Carmeli
Technion, Israel
,Xiaolan Wang
Megagon Labs, USA
,Yoshihiko Suhara
Megagon Labs, USA
,Stefanos Angelidis
University of Edinburgh, United Kingdom
,Yuliang Li
Megagon Labs, USA
,Jinfeng Li
Megagon Labs, USA
,Wang-Chiew Tan
Facebook AI, USA
WWW '21: Proceedings of the Web Conference 2021•April 2021, pp 3419-3431• https://doi.org/10.1145/3442381.3450081The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less work on organizing opinions, which are abundant in subjective data, ...
- 1Citation
- 250
- Downloads
MetricsTotal Citations1Total Downloads250Last 12 Months29Last 6 weeks1
- research-article
Published By ACM
Published By ACM
Deep Entity Matching: Challenges and Opportunities
Yuliang Li
Megagon Labs, Mountain View, CA
,Jinfeng Li
Megagon Labs, Mountain View, CA
,Yoshihiko Suhara
Megagon Labs, Mountain View, CA
,Jin Wang
Megagon Labs, Mountain View, CA
,Wataru Hirota
Megagon Labs, Mountain View, CA
,Wang-Chiew Tan
Megagon Labs, Mountain View, CA
Journal of Data and Information Quality, Volume 13, Issue 1•March 2021, Article No.: 1, pp 1-17 • https://doi.org/10.1145/3431816Entity matching refers to the task of determining whether two different representations refer to the same real-world entity. It continues to be a prevalent problem for many organizations where data resides in different sources and duplicates the need to ...
- 32Citation
- 1,610
- Downloads
MetricsTotal Citations32Total Downloads1,610Last 12 Months364Last 6 weeks24
- research-article
Querying subjective data
Yuliang Li
Megagon Labs, Mountain View, USA
,Aaron Feng
Megagon Labs, Mountain View, USA
,Jinfeng Li
Megagon Labs, Mountain View, USA
,Shuwei Chen
Megagon Labs, Mountain View, USA
,Saran Mumick
University of Pennsylvania, Philadelphia, USA
,Alon Halevy
Facebook AI, California, USA
,Vivian Li
Megagon Labs, Mountain View, USA
,Wang-Chiew Tan
Megagon Labs, Mountain View, USA
The VLDB Journal — The International Journal on Very Large Data Bases, Volume 30, Issue 1•Jan 2021, pp 115-140 • https://doi.org/10.1007/s00778-020-00634-5AbstractOnline users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, ...
- 0Citation
MetricsTotal Citations0
- research-article
Deep entity matching with pre-trained language models
Yuliang Li
Megagon Labs
,Jinfeng Li
Megagon Labs
,Yoshihiko Suhara
Megagon Labs
,AnHai Doan
University of Wisconsin Madison
,Wang-Chiew Tan
Megagon Labs
Proceedings of the VLDB Endowment, Volume 14, Issue 1•September 2020, pp 50-60 • https://doi.org/10.14778/3421424.3421431We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a ...
- 98Citation
- 511
- Downloads
MetricsTotal Citations98Total Downloads511Last 12 Months130Last 6 weeks9
- invited-talk
Published By ACM
Published By ACM
Unleashing the Power of Subjective Data: Managing Experiences as First-Class Citizens
Wang-Chiew Tan
Megagon, Mountain View, CA, USA
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining•August 2020, pp 3610-3610• https://doi.org/10.1145/3394486.3409560Subjective data refers to data that contains opinions and experiences. Such data is ubiquitous in product reviews, tweets, and discussion forums in social media. Consumers today spend considerable time sifting through subjective data to make informed ...
- 0Citation
- 76
- Downloads
MetricsTotal Citations0Total Downloads76Last 12 Months4
- research-article
Deep or simple models for semantic tagging?: it depends on your data
Jinfeng Li
Megagon Labs
,Yuliang Li
Megagon Labs
,Xiaolan Wang
Megagon Labs
,Wang-Chiew Tan
Megagon Labs
Proceedings of the VLDB Endowment, Volume 13, Issue 12•August 2020, pp 2549-2562 • https://doi.org/10.14778/3407790.3407844Semantic tagging, which has extensive applications in text mining, predicts whether a given piece of text conveys the meaning of a given semantic tag. The problem of semantic tagging is largely solved with supervised learning and today, deep learning ...
- 1Citation
- 63
- Downloads
MetricsTotal Citations1Total Downloads63Last 12 Months17
- research-article
Sato: contextual semantic type detection in tables
Dan Zhang
UMASS Amherst
,Madelon Hulsebos
The HEINEKEN Company
,Yoshihiko Suhara
Megagon Labs
,Çağatay Demiralp
Megagon Labs
,Jinfeng Li
Megagon Labs
,Wang-Chiew Tan
Megagon Labs
Proceedings of the VLDB Endowment, Volume 13, Issue 12•August 2020, pp 1835-1848 • https://doi.org/10.14778/3407790.3407793Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection ...
- 28Citation
- 132
- Downloads
MetricsTotal Citations28Total Downloads132Last 12 Months34Last 6 weeks1
- research-article
Published By ACM
Published By ACM
Teddy: A System for Interactive Review Analysis
Xiong Zhang
University of Rochester, Rochester, NY, USA
,Jonathan Engel
Megagon Labs, Mountain View, CA, USA
,Sara Evensen
Megagon Labs, Mountain View, CA, USA
,Yuliang Li
Megagon Labs, Mountain View, CA, USA
,Çağatay Demiralp
Megagon Labs, Mountain View, CA, USA
,Wang-Chiew Tan
Megagon Labs, Mountain View, CA, USA
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems•April 2020, pp 1-13• https://doi.org/10.1145/3313831.3376235Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, ...
- 6Citation
- 669
- Downloads
MetricsTotal Citations6Total Downloads669Last 12 Months126Last 6 weeks4- 3
- research-article
Published By ACM
Published By ACM
ExtremeReader: An interactive explorer for customizable and explainable review summarization
Xiaolan Wang
Megagon Labs
,Yoshihiko Suhara
Megagon Labs
,Natalie Nuno
Megagon Labs
,Yuliang Li
Megagon Labs
,Jinfeng Li
Megagon Labs
,Nofar Carmeli
Technion
,Stefanos Angelidis
University of Edinburgh
,Eser Kandogann
Megagon Labs
,Wang-Chiew Tan
Megagon Labs
WWW '20: Companion Proceedings of the Web Conference 2020•April 2020, pp 176-180• https://doi.org/10.1145/3366424.3383535Building summarization systems have become a necessity due to the extensive volume and growth of online reviews. Despite extensive research on this topic, existing summarization systems generally fall short on two aspects. First, existing techniques ...
- 7Citation
- 409
- Downloads
MetricsTotal Citations7Total Downloads409Last 12 Months50Last 6 weeks1
- research-article
Published By ACM
Published By ACM
Snippext: Semi-supervised Opinion Mining with Augmented Data
Zhengjie Miao
Duke University, USA
,Yuliang Li
Megagon Labs, USA
,Xiaolan Wang
Megagon Labs, USA
,Wang-Chiew Tan
Megagon Labs, USA
WWW '20: Proceedings of The Web Conference 2020•April 2020, pp 617-628• https://doi.org/10.1145/3366423.3380144Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-...
- 43Citation
- 881
- Downloads
MetricsTotal Citations43Total Downloads881Last 12 Months71Last 6 weeks3
- research-article
What I probably did right and what I think I could have done better
Wang-Chiew Tan
Megagon Labs
Proceedings of the VLDB Endowment, Volume 12, Issue 12•August 2019, pp 2308-2308 • https://doi.org/10.14778/3352063.3352147They say a lot of good things in life are not free. Success is one of them. Successful research requires an immense amount of hard work and dedication over a long period of time. For better or worse, hard work alone does not guarantee success. In my ...
- 0Citation
MetricsTotal Citations0
- research-article
Subjective databases
Yuliang Li
Megagon Labs
,Aaron Feng
Megagon Labs
,Jinfeng Li
Megagon Labs
,Saran Mumick
Megagon Labs
,Alon Halevy
Megagon Labs
,Vivian Li
Megagon Labs
,Wang-Chiew Tan
Megagon Labs
Proceedings of the VLDB Endowment, Volume 12, Issue 11•July 2019, pp 1330-1343 • https://doi.org/10.14778/3342263.3342271Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price,...
- 9Citation
- 167
- Downloads
MetricsTotal Citations9Total Downloads167Last 12 Months17Last 6 weeks1
- research-article
Published By ACM
Published By ACM
Voyageur: An Experiential Travel Search Engine
Sara Evensen
Megagon Labs
,Aaron Feng
Megagon Labs
,Alon Halevy
Megagon Labs
,Jinfeng Li
Megagon Labs
,Vivian Li
Megagon Labs
,Yuliang Li,
Huining Liu
Megagon Labs
,George Mihaila
Megagon Labs
,John Morales
Megagon Labs
,Natalie Nuno
Megagon Labs
,Ekaterina Pavlovic
Megagon Labs
,Wang-Chiew Tan
Megagon Labs
,Xiaolan Wang
Megagon Labs
We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In ...
- 3Citation
- 207
- Downloads
MetricsTotal Citations3Total Downloads207Last 12 Months7
- research-article
Published By ACM
Published By ACM
Data Provenance: What next?
Peter Buneman
University of Edinburgh, Edinburgh, United Kingdom
,Wang-Chiew Tan
Megagon Labs, Mountain View, CA, USA
ACM SIGMOD Record, Volume 47, Issue 3•September 2018, pp 5-16 • https://doi.org/10.1145/3316416.3316418Research into data provenance has been active for almost twenty years. What has it delivered and where will it go next? What practical impact has it had and what might it have? We provide speculative answers to these questions which may be somewhat ...
- 30Citation
- 928
- Downloads
MetricsTotal Citations30Total Downloads928Last 12 Months76Last 6 weeks1
- research-article
Published By ACM
Published By ACM
Technical Perspective:: Toward Building Entity Matching Management Systems
Wang-Chiew Tan
Recruit Institute of Technology
ACM SIGMOD Record, Volume 47, Issue 1•March 2018, pp 32-32 • https://doi.org/10.1145/3277006.3277014- 1Citation
- 32
- Downloads
MetricsTotal Citations1Total Downloads32Last 12 Months2
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner