The document provides an overview of ChemXSeer, a digital library and search engine for chemistry research. It was created using the Penn State's SeerSuite toolkit to build specialized search engines and digital libraries. ChemXSeer indexes the full text of research papers and extracts additional metadata like author information, tables, figures, chemical names and formulas. It allows for targeted searches in these different fields, like searching for specific authors, tables containing experimental data, figures with plotted results, or chemical structures. The system aims to improve on general search engines by preserving additional scholarly context during the indexing and search process. Future work areas include integrating 3D chemical graph searching and semantic chemical data from sources like spectra and experimental methods.

1. ChemXSeer:
Digital library tools, features,
and crawling characteristics
Edward A. Fox
Professor, Computer Science, Virginia Tech
Blacksburg, VA 24061 USA
fox@vt.edu http://fox.cs.vt.edu
and
Sagnik Ray Choudhury
Ph.D. Student, College of Information Science and
Technology, Penn State, USA
szr163@ist.psu.edu
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2. Outline
• Acknowledgments
• Introduction
• ELISQ
• Technology
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3. Sponsored by Qatar University Library
HTTP://qnl.qa
HTTP://WWW.QU.EDU.QA/
Funding provided thru the ELISQ project:
Electronic Library Institute - SeerQ
HTTP://WWW.VT.EDU/
HTTP://WWW.PSU.EDU/
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HTTP://WWW.TAMU.EDU/
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4. Acknowledgments
• Dr. Mazen Hasna, VP and Chief Academic Officer,
Qatar University
• Dr. Rashid Alammari, Dean, College of Engineering,
Qatar University
• Dr. Moumen Hasnah , Director of Academic Research,
Qatar University
• Dr. Imad Bachir, Qatar University Library Director
• Prof. Sebti Foufou, Head of Department of Computer
Science and Engineering, Qatar University
• Prof. Ramazan Kahraman, Head of the Department of
Chemical Engineering, Qatar University
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5. Additional Thanks
QScience – providing collection:
Christopher J. Leonard, Editorial Director
Paul Coyne, CTO
US National Science Foundation
(recent and current grants to Fox):
• IIS-1319578
• IIS-0916733
• DUE-0840719
• OCI-1032677
• plus those to PSU, TAMU
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6. Outline
• Acknowledgments
• Introduction
• ELISQ
• Technology
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7. Introduction
• Digital libraries have emerged since 1991.
• Now each major publisher has its own
digital library; many others exist too.
• Related systems include:
• Institutional repositories, e.g., at QU
• Content & courseware management systems
• Research and development funding of
hundreds of millions of dollars has led to
powerful tailored systems, such as for
chemical information.
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9. Information Life Cycle
Authoring
Modifying
Using
Creating
Retention
/ Mining
Accessing
Filtering
Organizing
Indexing
Storing
Retrieving
Distributing
Networking
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10. Infrastructure Services
Repository-Building
Creational
Preservational
Acquiring
Cataloging
Crawling (focused)
Describing
Digitizing
Federating
Harvesting
Purchasing
Submitting
Conserving
Converting
Copying/Replicating
Emulating
Renewing
Translating (format)
Add
Value
Annotating
Classifying
Clustering
Evaluating
Extracting
Indexing
Measuring
Publicizing
Rating
Reviewing (peer)
Surveying
Translating
(language)
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Information
Satisfaction
Services
Browsing
Collaborating
Customizing
Filtering
Providing access
Recommending
Requesting
Searching
Visualizing
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11. Outline
• Acknowledgments
• Introduction
• ELISQ
• Technology
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12. ELISQ – Electronic Library Institute –
SeerQ –– Project Team
Qatar University, Qatar:
Mohammed Samaka (Ph.D., Co-Lead PI)
Sumaya Ali S A Al-Maadeed (Ph.D., PI)
Myrna Tabet
Asad Nafees
Tahseena Moideen
Qatar National Library, Qatar:
Claudia Lux (PI)
Krishna RoyChowdhury
Postdoc - TBA
Virginia Tech, USA:
Edward Fox (Ph.D., Lead-PI)
Tarek Kanan
Penn. State University, USA:
C. Lee Giles (Ph.D., PI)
Sagnik Ray Choudhury
Texas A&M, USA:
Richard Furuta (Ph.D., PI)
Hamed Alhoori
Consultants:
John Impagliazzo (Ph.D., Key Investigator)
Susan Lukesh (Ph.D.)
This project was made possible by NPRP Grant # 4 - 029 - 1 – 007 from
Carole Thompson
the Qatar National Research Fund (a member of Qatar Foundation).
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13. ELISQ Project (1 of 2)
Project Objectives/Aims
A. Research and prototype digital library systems and
infrastructure for Qatar, focusing initially on Qatari
information related to government and scholarly
activities.
Leverage the crawling engine from Penn State‘s SeerSuite software
infrastructure, and extend it beyond its current focus on English to
support Arabic-English collections, and to cover a broad range of
scholarly disciplines, and all types of government information.
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14. ELISQ Project (2 of 2)
Project Objectives/Aims (continued)
B. Research and build the digital library community in
Qatar, supporting digital library use, services,
collection development, tailored systems, and
advancing toward a Knowledge Society.
Study scholarly activities, and engage in community building in
Qatar, so DLs can be tailored to specific domains and to the unique
needs of Qatar. Through workshops, a consulting center at the
proposed Institute, and collaborative efforts with libraries and
museums in Qatar, we will identify particular needs and uses, and
tailor collections, systems, and services, to lead toward the Qatari
Knowledge Society.
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15. Outline
• Acknowledgments
• Introduction
• ELISQ
• Technology
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16. Crawler (Heritrix)
(for search engines & Web archives)
• A Web crawler starts with a list of URLs to visit,
called the seeds.
• On those page, identifies all the hyperlinks
• adds them to the list of URLs to visit
• recursively visits pages pointed to
• according to a set of policies.
• Prioritizes its downloads – some pages change often.
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17. Selected SeerSuite Instantiations
• CiteSeerx
• http://citeseerx.ist.psu.edu
• A scientific literature digital library and search engine
• ChemXSeer
• http://chemxseer.ist.psu.edu
• Portal for researchers in environmental chemistry
integrating the scientific literature with experimental,
analytical, and simulation results and tools
• ArchSeer
• http://archseer.ist.psu.edu/
• Archeology literature
• TableSeer
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18. CiteSeerX
http://citeseerx.ist.psu.edu
• CiteSeerX crawls researcher homepages on the web for scholarly papers, formerly in
computer science
• Converts PDF to text
• Automatically extracts OAI metadata and other data
• Automatic citation indexing, links to cited documents, creation of
document page, author disambiguation
• Software open source – can be used to build other such tools
• 3 M documents
• Ms of files
• 60 M citations
• 3 to 6 M authors
• 2 to 4 M hits day
• 100K documents added
monthly
• 800K individual users
• several Tbytes
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21. SeerSuite
• Tool kit used to build search engines and digital libraries
• CiteSeerX , MyCiteSeerX , ChemXSeer, ArchSeer, AlgoSeer,
AckSeer, BizSeer, CSSeer, CollabSeer, RefSeer, GrantSeer,
SeerSeer, YouSeer, etc.
• Built on commercial grade open source tools (Solr/Lucene)
• Penn State expertise – automated specialized metadata
extraction
• Supports research in
• Indexing and search
• Data mining & structures
• Information and knowledge extraction
• Social networks: Name/entity disambiguation
• Scientometrics/infometrics
• Systems engineering
• User interface design (HCI = human-computer interaction)
• Software engineering and management

22. SeerSuite is not Google
• Metadata (as in library catalogs) as well as content
• Sets of collections, rather than the Web as a whole
• Provided by a curator (e.g., publisher, museum)
• Provided by user submissions
• Or collected by focused ‘crawling’
• Tailored services, rather than the same for everyone
• Browsing using categories, preserving, adding value
• Based on studying user requirements, e.g., chemists
• Working with entities, rather than just words
• Citations, tables, figures, names, chemical formula
• Using knowledge bases, machine learning, artificial intelligence
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23. Questions for Us?
• http://elisq.qu.edu.qa/
• fox@vt.edu
• http://fox.cs.vt.edu
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24. Search Engine and Repository for eChemistry
C. Lee Giles, Prasenjit Mitra, Karl Mueller, Levent Bolelli, Xiaonan Lu, Saurabh Kataria, Ying
Liu, Anuj Jaiswal, Kun Bai, Bingjun Sun, Isaac Councill, James Z. Wang, James Kubicki,
Barbara Garrison, William Brouwer, Joel Bandstra, Qingzhao Tan, Juan Pablo Ramirez
Fernandez, Madian Khabsa, Hung-Hsuan Chen, Sagnik Ray Choudhury
Chemistry, Computer Sciences and Engineering, Geosciences, Information Sciences and
Technology
Pennsylvania State University, University Park, PA, USA
Past funding: NSF Cyberinfrastructure Chemistry, Microsoft
Current Support: Dow Chemical
http://chemxseer.ist.psu.edu

25. Talk Overview
●
Challenges and Motivation.
●
Functionalities
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Fulltext Search
Author Search
Table Search
Figure Search
Expertise Search
Chemical Name and Formula Tagging
Chemical Name and Formula Search
Summary.

26. Based on cyberinfrastructure
for CiteSeerX
Built on Solr/Lucene,
SeerSuite, other OSS

27. ChemXSeer RSC

28. ChemXSeer Fulltext Search

29. ChemXSeer Author Search

30. ChemXSeer Table Search
• Tables are widely used to present experimental results or statistical
data in scientific documents.
• Existing search engines treat tabular data as regular text
– Structural information and semantics not preserved.
– We automatically identify tables and extract table metadata in xml.
Table Metadata Representation:
• Environment metadata: (document specifics: type, title,…)
• Frame metadata: (border left, right, top, bottom, …)
• Affiliated metadata: (Caption, footnote, …)
• Layout metadata: (number of rows, columns, headers,…)
• Cell content metadata: (values in cells)
• Type metadata: (numeric, symbolic, hybrid, …)
Y. Liu, et.al, AAAI 2007, JCDL 2007.

31. Sample Table Metadata Extracted File

32. Sample Table Metadata Extracted File

33. ChemXSeer Table Search

34. ChemXSeer Figure/Plot Data Extraction and
Search
Numerical data in
scientific publications
are often found in figures.
No search engine allows
searching on figures and their
data in chemical documents.
Tools that automate the data extraction from figures and allow
search on them can provide the following:
•
•
•
•
Increases our understanding of key concepts of papers.
Provides data for automatic comparative analyses.
Enables regeneration of figures in different contexts.
Enables search for documents with figures containing specific experiment
results.
X. Lu, et.al, JCDL 2006., Ray Choudhury et al. JCDL 2013, ICDAR 2013

35. Our Contribution

36. ChemXSeer Name and Formula Extraction
and Search
• Extraction and search of chemical names and formulae in scientific
documents has been shown to be very useful.
• Extraction and search on chemical names is hard:
– Many chemical molecules are created everyday, any dictionary based name
recognizer will fail eventually.
– Names need to segmented to get semantically meaningful sub-terms such as
“methyl”, “ethyl” and “alcohol” from “methylethyl alcohol”.
• Identifying formula is hard:
• “… YSI 5301, Yellow Springs, OH, USA …” (Non-formula)
• “… such as hydroxyl radical OH, superoxide O2- …” (formula)
• For searching, formulae cannot be treated as text.
• Domain knowledge (formula identification)
• Structural knowledge (substructure finding and search)
B. Sun, et.al., WWW 2007, WWW 2008, TOIS

37. Chemical Entity Extraction and Tagging
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Name tagging
–
–
Each chemical name can be a phrase
Example
●
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Formula tagging
–
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Each formula is a single term
Example
●
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"... such as hydroxyl radical OH, superoxide ..."
Non-formula example
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"... Determination of lactic acid and ...“
"... insecticide promecarb (3-isopropyl-5-methylphenyl
methylcarbamate) acts against ..."
"... YSI 5301, Yellow Springs, OH, USA ... ”
Tagging examples
–
Name tagging:
"... of <name-type>lactic acid</name-type> and ...“
–
Formula tagging:
"...
radical <formula-type>OH</formula-type> , superoxide ..."

38. Online Chemical Entity Tagger
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We have an open source chemical name and formula
tagger and a web based interface for evaluation.
The interface takes a PDF file as input, returns text of the
PDF with names or formulas tagged.

39. Online Chemical Entity Tagger: Chemical Name
Tagging Example
●
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Results on a sample PDF.
Some chemical formula erroneously identified as chemical name (loss
of precision).
High recall (most chemical names identified)

40. Online Chemical Entity Tagger: Chemical
Formula Tagging Example
●
●
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Results on a sample PDF.
Some chemical formulas not identified (loss of recall).
High precision (words identified as formula are actual formulas)

41. Chemical Name Indexing and Search
• Index Schemes:
– Which tokens to index?
– Indexing all subsequences generates a large size index
– “but” in “butane” is morpheme, but not for “nembutal”.
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Segmentation-based index scheme
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–
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Used for indexing chemical names
First segment a chemical name hierarchically and then index
substrings at each node if frequent.
acetaldoxime->aldoxime->oxime.
Search for oxime returns all, depending on ranking function.
This can not be done in usual text search.

42. Example Formula Search
http://chemxseer.ist.psu.edu/ChemXSeerFormulaSearch/help.htm

43. Expert Recommendation - CiteSeerX
http://seerseer.ist.psu.edu (new version CSSeers)
Built on top of millions of
papers in CiteSeerX.
A similar system was
developed for Dow
Chemicals.
Can find experts in
“polymer chemistry” or
expertise of “Linus Pauling”
Finds an expert based on
their publications.
Many approaches:
Keyphases
Citations
Download count.
Treeratpituk, Chen, JCDL’13
Affiliation

44. Future Work
Lots of interesting work to do! Few computer/machine
learning scientists involved.
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Acquisitions - more documents, data, knowledge
Chemical 3D graph search
Fundamental chemical graph representation analysis
Table data storage and access
Figure search and data extraction and access
New data and feature search
• spectra, experimental methods, instrumentation
New documents: 400K PubMed
Semantic chemical graphs
Expert/collaborator search
Search integration of all features