| Short description | Scans the plaintexts of publications for acknowledged concepts, including grant identifiers (projects) of funders, accession numbers of bioetities, EPO patent mentions, as well as custom concepts that can link research objects to specific research communities and initiatives in OpenAIRE. |
| Authority | ATHENA Research Center, Greece |
| Licence | CC-BY/CC-0 |
| Algorithmic details | The algorithm processes the publication's fulltext and extracts references to acknowledged concepts. It applies pattern matching and string join between the fulltext and a target database which contains the title, the acronym and the identifier of the searched concept. |
| Parameters | Concept titles, acronyms, and identifiers, publication's identifiers and fulltexts |
| Environment | Python, madIS (https://github.com/madgik/madis), APSW (https://github.com/rogerbinns/apsw) |
| References & resources | [Foufoulas, Y., Zacharia, E., Dimitropoulos, H., Manola, N., Ioannidis, Y. (2022). DETEXA: Declarative Extensible Text Exploration and Analysis. In: , et al. Linking Theory and Practice of Digital Libraries. TPDL 2022. Lecture Notes in Computer Science, vol 13541. Springer, Cham.](https://doi.org/10.1007/978-3-031-16802-4_9) |
# Extraction of acknowledged concepts
***Short description:***
Scans the plaintexts of publications for acknowledged concepts, including grant identifiers (projects) of funders, accession numbers of bioetities, EPO patent mentions, as well as custom concepts that can link research objects to specific research communities and initiatives in OpenAIRE.
***Algorithmic details:***
The algorithm processes the publication's fulltext and extracts references to acknowledged concepts. It applies pattern matching and string join between the fulltext and a target database which contains the title, the acronym and the identifier of the searched concept.
***Parameters:***
Concept titles, acronyms, and identifiers, publication's identifiers and fulltexts
* Foufoulas, Y., Zacharia, E., Dimitropoulos, H., Manola, N., Ioannidis, Y. (2022). DETEXA: Declarative Extensible Text Exploration and Analysis. In: , et al. Linking Theory and Practice of Digital Libraries. TPDL 2022. Lecture Notes in Computer Science, vol 13541. Springer, Cham. [doi:10.1007/978-3-031-16802-4_9](https://doi.org/10.1007/978-3-031-16802-4_9)
The goal of the affiliation matching module is to match affiliations extracted from the pdf and xml documents with organizations from the OpenAIRE organization database.
***Algorithmic details:***
@ -45,12 +48,11 @@ The total match strength is calculated in such a way that each consecutive voter
* output
* [MatchedOrganization](https://github.com/openaire/iis/blob/master/iis-wf/iis-wf-affmatching/src/main/resources/eu/dnetlib/iis/wf/affmatching/model/MatchedOrganization.avdl) avro datastore location with matched publications with organizations.
During a citation matching task, bibliographic entries are linked to the documents that they reference. The citation matching module - one of the modules of the Information Inference Service (IIS) - receives as an input a list of documents accompanied by their metadata and bibliography. Among them, it discovers links described above and returns them as a list. In this document we shall evaluate if the module has been properly integrated with the whole
system and assess the accuracy of the algorithm used. It is worth mentioning that the implemented algorithm has been described in detail in arXiv:1303.6906 [cs.IR]1. However, in the referenced paper the algorithm was tested on small datasets, but here we will focus on larger datasets, which are expected to be analysed by the system in the production environment.
@ -27,17 +26,17 @@ In this step, all the potentially matching pairs obtained in the heuristic step
names, we have taken longest common subsequence (LCS) of two strings into consideration. This can be seen as an instance of the assignment problem with some refinements added. The overall similarity of two citation strings is obtained by applying a linear Support Vector Machine (SVM) using field similarities as features.
***Parameters:***
* input:
* input_metadata: [ExtractedDocumentMetadataMergedWithOriginal](https://github.com/openaire/iis/blob/master/iis-schemas/src/main/avro/eu/dnetlib/iis/transformers/metadatamerger/ExtractedDocumentMetadataMergedWithOriginal.avdl) avro datastore location with the metadata of both publications and bibliorgaphic references to be matched
* input_matched_citations: [Citation](https://github.com/openaire/iis/blob/master/iis-schemas/src/main/avro/eu/dnetlib/iis/common/citations/Citation.avdl) avro datastore location with citations which were already matched and should be excluded from fuzzy matching
* output: [Citation](https://github.com/openaire/iis/blob/master/iis-schemas/src/main/avro/eu/dnetlib/iis/common/citations/Citation.avdl) avro datastore location with matched publications
| Short description | Scans the plaintexts of publications for cited concepts, currently for references to datasets and software URIs. |
| Authority | ATHENA Research Center, Greece |
| Licence | CC-BY/CC-0 |
| Algorithmic details | The algorithm extracts citations to specific datasets and software. It extracts the citation section of a publication's fulltext and applies string matching against a target database which includes an inverted index with dataset/software titles, urls and other metadata. |
| Parameters | Title, URL, creator names, publisher names and publication year for each concept to create the target database. Identifier and publication's fulltext to extract the cited concepts. |
| Environment | Python, madIS (https://github.com/madgik/madis), APSW (https://github.com/rogerbinns/apsw) |
| References & resources | [Foufoulas Y., Stamatogiannakis L., Dimitropoulos H., Ioannidis Y. (2017) “High-Pass Text Filtering for Citation Matching”. In: Kamps J., Tsakonas G., Manolopoulos Y., Iliadis L., Karydis I. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science, vol 10450. Springer, Cham.](https://doi.org/10.1007/978-3-319-67008-9_28) |
***Short description:***
Scans the plaintexts of publications for cited concepts, currently for references to datasets and software URIs.
***Algorithmic details:***
The algorithm extracts citations to specific datasets and software. It extracts the citation section of a publication's fulltext and applies string matching against a target database which includes an inverted index with dataset/software titles, urls and other metadata.
***Parameters:***
Title, URL, creator names, publisher names and publication year for each concept to create the target database. Identifier and publication's fulltext to extract the cited concepts
| Short description | A document classification algorithm that employs analysis of free text stemming from the abstracts of the publications. The purpose of applying a document classification module is to assign a scientific text to one or more predefined content classes. |
| Authority | ATHENA Research Center, Greece |
| Licence | CC-BY/CC-0 |
| Algorithmic details | The algorithm classifies publication's fulltexts using a Bayesian classifier and weighted terms according to an offline training phase. The training has been done using the following taxonomies: arXiv, MeSH (Medical Subject Headings), ACM, and DDC (Dewey Decimal Classification, or Dewey Decimal System). |
| Parameters | Publication's identifier and fulltext |
| Environment | Python, madIS (https://github.com/madgik/madis), APSW (https://github.com/rogerbinns/apsw) |
| References & resources | [Giannakopoulos, T., Stamatogiannakis, E., Foufoulas, I., Dimitropoulos, H., Manola, N., Ioannidis, Y. (2014). Content Visualization of Scientific Corpora Using an Extensible Relational Database Implementation. In: Bolikowski, Ł., Casarosa, V., Goodale, P., Houssos, N., Manghi, P., Schirrwagen, J. (eds) Theory and Practice of Digital Libraries -- TPDL 2013 Selected Workshops. TPDL 2013. Communications in Computer and Information Science, vol 416. Springer, Cham.](https://doi.org/10.1007/978-3-319-08425-1_10) |
***Short description:*** A document classification algorithm that employs analysis of free text stemming from the abstracts of the publications. The purpose of applying a document classification module is to assign a scientific text to one or more predefined content classes.
***Algorithmic details:***
The algorithm classifies publication's fulltexts using a Bayesian classifier and weighted terms according to an offline training phase. The training has been done using the following taxonomies: arXiv, MeSH (Medical Subject Headings), ACM, and DDC (Dewey Decimal Classification, or Dewey Decimal System).
***Parameters:*** Publication's identifier and fulltext
* Giannakopoulos, T., Stamatogiannakis, E., Foufoulas, I., Dimitropoulos, H., Manola, N., Ioannidis, Y. (2014). Content Visualization of Scientific Corpora Using an Extensible Relational Database Implementation. In: Bolikowski, Ł., Casarosa, V., Goodale, P., Houssos, N., Manghi, P., Schirrwagen, J. (eds) Theory and Practice of Digital Libraries -- TPDL 2013 Selected Workshops. TPDL 2013. Communications in Computer and Information Science, vol 416. Springer, Cham. [doi:10.1007/978-3-319-08425-1_10](https://doi.org/10.1007/978-3-319-08425-1_10)
Document similarity module is responsible for finding similar documents among the ones available in the OpenAIRE Information Space. It produces "similarity" links between the documents stored in the OpenAIRE Information Space. Each link has a similarity score from [0,1] range assigned; it is expected that the higher the score, the more similar are the documents with respect to their content.
***Algorithmic details:***
The similarity between two documents is expressed as the similarity between weights of their common terms (i.e., words being reduced to their root form) within a context of all terms from the first and the second document. In this approach, the computation can be divided into three consecutive steps:
1. selection of proper terms,
@ -39,10 +37,9 @@ Computation of similarity between documents is executed in the following steps.
* threshold_num_of_vector_elems_length: vector elements length threshold, when set to less than 2 all documents will be included in similarity matching (default=2)
Metadata Extraction algorithm is responsible for plaintext and metadata extraction out of the PDF documents. It based on [CERMINE](http://cermine.ceon.pl/about.html) project.
CERMINE is a comprehensive open source system for extracting metadata and content from scientific articles in born-digital form. The system is able to process documents in PDF format and extracts:
@ -13,7 +12,6 @@ CERMINE is a comprehensive open source system for extracting metadata and conten
CERMINE is based on a modular workflow, whose architecture ensures that individual workflow steps can be maintained separately. As a result it is easy to perform evaluation, training, improve or replace one step implementation without changing other parts of the workflow. Most steps implementations utilize supervised and unsupervised machine-leaning techniques, which increases the maintainability of the system, as well as its ability to adapt to new document layouts.
***Algorithmic details:***
CERMINE workflow is composed of four main parts:
* Basic structure extraction takes a PDF file on the input and produces a geometric hierarchical structure representing the document. The structure is composed of pages, zones, lines, words and characters. The reading order of all elements is determined. Every zone is labelled with one of four general categories: METADATA, REFERENCES, BODY and OTHER.
@ -28,11 +26,9 @@ CERMINE uses supervised and unsupervised machine-leaning techniques, such as Sup
