Research Intelligence

Elsevier Fingerprint Engine®
Based on state-of-the-art Natural Language Processing(NLP) techniques, the Elsevier Fingerprint Engine® is aback-end software system that extracts information from theunstructured text of scientific documents. It applies a domainrelevant thesaurus to each scientific publication to map text tosemantic ‘fingerprints’, or collections of weighted key concepts.

By identifying and extracting new concepts, the Elsevier
Fingerprint Engine can enrich each thesaurus and generate
new vocabularies so it continuously improves the insights it
delivers to researchers and funding bodies. It can be used asa back-office processing component of applications or as astand-alone service.

Out-of-the-box text analytics

Thesauri that cover a wide rangeof subjects

To create a Fingerprint® index – a set of standardized,
domain-specific concepts that define the text – the Elsevier
Fingerprint Engine mines a document’s unstructured text,
including publication abstracts, funding announcements
and awards, project summaries, patents, proposals andapplications. It enables institutions to look beyond metadata
by aggregating and comparing Fingerprint indices. The ideas
extracted from documents help users to identify trends, andexpose and analyze valuable connections between groups(researchers, funders, reviewers), organizations (institutions,associations) or geographic areas.

Integrated across multiple solutions
As a key component in SciVal®, Pure and Expert Lookup,
the Elsevier Fingerprint Engine computes semantic
representations for publications and other data types toallow for presentation, navigation and reporting of scientificoutput. It also serves as the framework for customized
modules that enable funding agencies to find reviewers,analyze grant portfolios and strategically plan which areas ofresearch to fund next.
The system’s flexibility allows it to be applied in various
ways to help each institution answer its most pressing
questions. It’s used to present author profiles and match
authors and funding opportunities in Pure and Profile
Refinement Services, to analyze and match grants toreviewers in Expert Lookup, to showcase competencies
and detect trends in research areas for SciVal.

To support documents and applications across a wide
variety of subject areas, the Elsevier Fingerprint Engine
integrates a range of thesauri, as well as enriching existing
thesauri and developing stand-alone vocabularies. It can also
employ subsets of thesauri or vocabularies to continuously
update and enhance its semantic fingerprints.

The thesauri used to power Fingerprint Engine
account for approximately 500,000 uniqueconcepts and terms across scientific domains:
DomainThesaurus/VocabularyLife Sciences

MeSH thesaurus

274,000

Embase thesaurus

80,000Physics

NASA thesaurus

19,000Agriculture

NAL thesaurus

73,000

Economics

Eco Humanities vocabulary

21,000Social Sciences

Gesis thesaurus

21,000MathematicsCambridge Math thesaurus, Math vocabulary

21,000

Geosciences

Geobase thesaurus

14,000Engineering

Compendex thesaurus

12,000

Humanities

Humanities vocabulary

39,000Compounds (Chemistry)

Compendex thesaurus, MeSH thesaurus

256,000

Number of terms
per thesaurus

Creating Fingerprint indices

Input: Natural language

To identify domain-relevant concepts in a text based ona thesaurus or vocabulary, the Elsevier Fingerprint Engine
incorporates a suite of tools for:• pre-processing (sentence detection, eliminating hyphens)

Fingerprinting
Workflow• tokenization

Vocabularygeneration

Tokenization• normalization

De-hyphenation• expansion (abbreviations and coordinations)

Coordination expansion• part-of-speech tagging• pattern identification (chemical compounds, URLs, idioms)

Normalization• term disambiguation

Abbreviation expansion• annotation

Entity recognition

The tools enable the concept-finding algorithm to be sensitive
to lexical and grammatical features (casing, word order) to
distinguish different usages (for example, Windows® vs.
windows, the noun form vs. the verb form of ‘lead’). It also
has the ability to ignore difference when it has no meaning(for example, tumour vs. tumor, kidney failure vs. failure of thekidney). It also considers the context of terms, looking to theneighboring text to avoid groupings that don’t make sense (forexample, non-Hodgkin Lymphoma with Hodgkin Lymphoma,
seeing the ‘tree of human ancestry’ as a plant, or interpreting‘administration’ as management in a text about a drug as atreatment for disease). Named entities, including the namesof people and places, are identified for disambiguation acrossthesauri and vocabularies, and can be presented separatelyfrom the actual Fingerprint indices.
The document’s concepts are weighted according totheir frequency, occurrence in the text’s title or body,and occurence in automatically detected sub-sections ofa text’s body. You can then aggregate the highest ranked or all
ranked concepts of document Fingerprint indices into profilesof individual researchers, institutions or regions. While theElsevier Fingerprint Engine is technically capable of handling
all language, current applications focus on English as thescientific lingua franca.

Language detection

Part-of-speech tagging

Noun phrasedetection

Term finder
Term annotation
Idiom removal
Fingerprint creation
Annotation storage

OutputSerialization

Technical Terms

Enriching terminology resources
In addition to identifying concepts, the Fingerprint Engine
can help to enrich existing thesauri or vocabularies or tobuild new ones from scratch. Using a subset of the engine’s
NLP components, a Noun Phrase Detector extracts technicalterms from document collections of specific domains.

Elsevier Fingerprint Engine®
For more information please contact
your Elsevier representative.
Expert Lookup is a service mark of Elsevier Inc.
Copyright © 2019 Elsevier.