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2025
Other
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InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2024
Other
Open Access
Enhancing author name disambiguation workflows in big data scholarly knowledge graphs
De Bonis M.
Open Science, defined by its commitment to transparency, collaboration, openness, and accessibility, has deeply affected scientific research. Following this new paradigm, scientists produce and publish research data and software alongside research publications to enable reproducibility, monitoring, and assessment of science. In this context, Scholarly Knowledge Graphs (SKGs) are “big data” metadata collections, playing a crucial role in research discovery and assessment by aggregating bibliographic metadata records and semantic relationships describing research products and their associations between them (e.g., citations, versions) and with other entities, such as organizations, authors, funders, etc. Examples of SKGs are the OpenAIRE Graph, Google Scholar, OpenAlex, Semantic Scholar, OpenCitations, and Research- Graph.org. However, constructing and maintaining SKGs demands innovative solutions to address the inherent scalability, heterogeneity, duplication, inconsistency, and incompleteness challenges introduced by the metadata sources to be aggregated. Motivated by the urge of Open Science and the challenges posed by SKG construction, this Ph.D. thesis makes pioneering contributions to the field of Author Name Disambiguation (AND). This perennial issue addresses the challenge of identifying and removing duplicate author nodes representing the same author in the SKG. Acknowledging the pivotal role of AND, the thesis discerns two main interwoven imperatives in the duplicate resolution processes: mitigating the efficiency challenge derived by the inherent quadratic complexity in comparing hundreds of millions of author nodes; and the effectiveness challenge introduced by the efficiency optimization strategies, which renounce parts of the matches, and affected by the poverty of metadata used to compare author nodes, which is often limited to the name’s string. To address the efficiency challenge, the thesis introduces FDup, a groundbreaking framework meticulously designed to reimagine and enhance the traditional disambiguation workflow. At its core, FDup prioritizes the optimization of the similarity match phase. This optimization is achieved through the incorporation of a decision tree-based comparison technique. This innovative approach ensures a customizable and efficient disambiguation workflow and enables parallelization, a crucial aspect in handling the substantial datasets inherent in Scholarly Knowledge Graphs. To address the effectiveness challenge, the thesis leverages Graph Neural Networks III (GNNs), which have been recently successfully applied to perform innovative research on node classification, graph classification, and link prediction. The proposed contributions manifest in two dedicated GNN architectures to enhance the effectiveness of Author Name Disambiguation via an evaluation of the outputs of a disambiguation algorithm: the first technique evaluates similarity relationships with an attentive neural network integrating GraphSAGE models; the second technique evaluates groups of duplicates with a combination of Graph Attention Network (GAT) and Long Short Term Memory (LSTM) components. In summary, this thesis is a responsive and forward-thinking contribution within the landscape of Open Science and Scholarly Knowledge Graphs. By introducing novel frameworks and harnessing advanced techniques like Graph Neural Networks, the thesis not only addresses the current challenges but also lays the groundwork for the continual evolution of Open Science practices and the optimal utilization of Scholarly Knowledge Graphs in the ever-expanding realm of scientific knowledge.
De Bonis M.
Open Science, defined by its commitment to transparency, collaboration, openness, and accessibility, has deeply affected scientific research. Following this new paradigm, scientists produce and publish research data and software alongside research publications to enable reproducibility, monitoring, and assessment of science. In this context, Scholarly Knowledge Graphs (SKGs) are “big data” metadata collections, playing a crucial role in research discovery and assessment by aggregating bibliographic metadata records and semantic relationships describing research products and their associations between them (e.g., citations, versions) and with other entities, such as organizations, authors, funders, etc. Examples of SKGs are the OpenAIRE Graph, Google Scholar, OpenAlex, Semantic Scholar, OpenCitations, and Research- Graph.org. However, constructing and maintaining SKGs demands innovative solutions to address the inherent scalability, heterogeneity, duplication, inconsistency, and incompleteness challenges introduced by the metadata sources to be aggregated. Motivated by the urge of Open Science and the challenges posed by SKG construction, this Ph.D. thesis makes pioneering contributions to the field of Author Name Disambiguation (AND). This perennial issue addresses the challenge of identifying and removing duplicate author nodes representing the same author in the SKG. Acknowledging the pivotal role of AND, the thesis discerns two main interwoven imperatives in the duplicate resolution processes: mitigating the efficiency challenge derived by the inherent quadratic complexity in comparing hundreds of millions of author nodes; and the effectiveness challenge introduced by the efficiency optimization strategies, which renounce parts of the matches, and affected by the poverty of metadata used to compare author nodes, which is often limited to the name’s string. To address the efficiency challenge, the thesis introduces FDup, a groundbreaking framework meticulously designed to reimagine and enhance the traditional disambiguation workflow. At its core, FDup prioritizes the optimization of the similarity match phase. This optimization is achieved through the incorporation of a decision tree-based comparison technique. This innovative approach ensures a customizable and efficient disambiguation workflow and enables parallelization, a crucial aspect in handling the substantial datasets inherent in Scholarly Knowledge Graphs. To address the effectiveness challenge, the thesis leverages Graph Neural Networks III (GNNs), which have been recently successfully applied to perform innovative research on node classification, graph classification, and link prediction. The proposed contributions manifest in two dedicated GNN architectures to enhance the effectiveness of Author Name Disambiguation via an evaluation of the outputs of a disambiguation algorithm: the first technique evaluates similarity relationships with an attentive neural network integrating GraphSAGE models; the second technique evaluates groups of duplicates with a combination of Graph Attention Network (GAT) and Long Short Term Memory (LSTM) components. In summary, this thesis is a responsive and forward-thinking contribution within the landscape of Open Science and Scholarly Knowledge Graphs. By introducing novel frameworks and harnessing advanced techniques like Graph Neural Networks, the thesis not only addresses the current challenges but also lays the groundwork for the continual evolution of Open Science practices and the optimal utilization of Scholarly Knowledge Graphs in the ever-expanding realm of scientific knowledge.
2024
Conference article
Open Access
FDup framework: a general-purpose solution for efficient entity deduplication of record collections
De Bonis M., Atzori C., La Bruzzo S., Manghi P.
Deduplication is a technique aimed at identifying and resolving duplicate metadata records in a collection with a special focus on the performances of the approach. This paper describes FDup(Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity-matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Graph, a known open-access knowledge base in Scholarly communication.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 624-632. Villasimius, Italy, 23-26/06/2024
Project(s): FAIRCORE4EOSC
De Bonis M., Atzori C., La Bruzzo S., Manghi P.
Deduplication is a technique aimed at identifying and resolving duplicate metadata records in a collection with a special focus on the performances of the approach. This paper describes FDup(Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity-matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Graph, a known open-access knowledge base in Scholarly communication.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3741, pp. 624-632. Villasimius, Italy, 23-26/06/2024
Project(s): FAIRCORE4EOSC
See at:
ceur-ws.org 
| CNR IRIS | CNR IRIS 
2024
Dataset
Open Access
OpenAIRE Graph Dataset v8.0.0 (July 2024)
Manghi P., Atzori C., Bardi A., Baglioni M., Dimitropoulos H., La Bruzzo S., Foufoulas I., Mannocci A., Horst M., Iatropoulou K., Kokogiannaki A., De Bonis M., Artini M., Lempesis A., Ioannidis A., Manola N., Principe P., Vergoulis T., Chatzopoulos S.
The OpenAIRE Graph is a large and rich collection of open and linked scholarly records from trusted data sources, such as journals, repositories, and registries. It aims to foster Open Science practices and enable the scientific community to discover, monitor, and evaluate science. The Graph is cleaned, deduplicated, enriched, and full-text mined to generate statistics and insights. The Graph is accessible via various services, such as OpenAIRE MONITOR, EXPLORE, ScholeXplorer (Scholix API for the retrieval of literature-data links), search APIs and snapshots in json format updated every six months. The Graph data are openly available with CC-BY license for third-parties to reuse and create added value services. The documentation is available at: https://graph.openaire.euDOI: 10.5281/zenodo.12819872
Project(s): FAIRCORE4EOSC, SciLake , EOSC Beyond , GraspOS , OSTrails
Metrics:
Manghi P., Atzori C., Bardi A., Baglioni M., Dimitropoulos H., La Bruzzo S., Foufoulas I., Mannocci A., Horst M., Iatropoulou K., Kokogiannaki A., De Bonis M., Artini M., Lempesis A., Ioannidis A., Manola N., Principe P., Vergoulis T., Chatzopoulos S.
The OpenAIRE Graph is a large and rich collection of open and linked scholarly records from trusted data sources, such as journals, repositories, and registries. It aims to foster Open Science practices and enable the scientific community to discover, monitor, and evaluate science. The Graph is cleaned, deduplicated, enriched, and full-text mined to generate statistics and insights. The Graph is accessible via various services, such as OpenAIRE MONITOR, EXPLORE, ScholeXplorer (Scholix API for the retrieval of literature-data links), search APIs and snapshots in json format updated every six months. The Graph data are openly available with CC-BY license for third-parties to reuse and create added value services. The documentation is available at: https://graph.openaire.euDOI: 10.5281/zenodo.12819872
Project(s): FAIRCORE4EOSC
, SciLake , EOSC Beyond , GraspOS , OSTrails Metrics:
See at:
CNR IRIS | zenodo.org | CNR IRIS
2023
Conference article
Open Access
(Semi)automated disambiguation of scholarly repositories
Baglioni M, Mannocci A, Pavone G, De Bonis M, Manghi P
The full exploitation of scholarly repositories is pivotal in modern Open Science, and scholarly repository registries are kingpins in enabling researchers and research infrastructures to list and search for suitable repositories. However, since multiple registries exist, repository managers are keen on registering multiple times the repositories they manage to maximise their traction and visibility across different research communities, disciplines, and applications. These multiple registrations ultimately lead to information fragmentation and redundancy on the one hand and, on the other, force registries' users to juggle multiple registries, profiles and identifiers describing the same repository. Such problems are known to registries, which claim equivalence between repository profiles whenever possible by cross-referencing their identifiers across different registries. However, as we will see, this "claim set" is far from complete and, therefore, many replicas slip under the radar, possibly creating problems downstream. In this work, we combine such claims to create duplicate sets and extend them with the results of an automated clustering algorithm run over repository metadata descriptions. Then we manually validate our results to produce an "as accurate as possible" de-duplicated dataset of scholarly repositories.Source: CEUR WORKSHOP PROCEEDINGS, pp. 47-59. Bari, Italy, 23-24/02/2023
Project(s): OpenAIRE Nexus
Baglioni M, Mannocci A, Pavone G, De Bonis M, Manghi P
The full exploitation of scholarly repositories is pivotal in modern Open Science, and scholarly repository registries are kingpins in enabling researchers and research infrastructures to list and search for suitable repositories. However, since multiple registries exist, repository managers are keen on registering multiple times the repositories they manage to maximise their traction and visibility across different research communities, disciplines, and applications. These multiple registrations ultimately lead to information fragmentation and redundancy on the one hand and, on the other, force registries' users to juggle multiple registries, profiles and identifiers describing the same repository. Such problems are known to registries, which claim equivalence between repository profiles whenever possible by cross-referencing their identifiers across different registries. However, as we will see, this "claim set" is far from complete and, therefore, many replicas slip under the radar, possibly creating problems downstream. In this work, we combine such claims to create duplicate sets and extend them with the results of an automated clustering algorithm run over repository metadata descriptions. Then we manually validate our results to produce an "as accurate as possible" de-duplicated dataset of scholarly repositories.Source: CEUR WORKSHOP PROCEEDINGS, pp. 47-59. Bari, Italy, 23-24/02/2023
Project(s): OpenAIRE Nexus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Graph-based methods for author name disambiguation: a survey
De Bonis M, Falchi F, Manghi P
Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers' activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.Source: PEERJ. COMPUTER SCIENCE., vol. 9
DOI: 10.7717/peerj-cs.1536
Project(s): EOSC Future, OpenAIRE Nexus
Metrics:
De Bonis M, Falchi F, Manghi P
Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers' activity. Various issues, such as homonymy, scarcity of contextual information, and cardinality of the SKG, make simple name string matching insufficient or computationally complex. Many AND deep learning methods have been developed, and interesting surveys exist in the literature, comparing the approaches in terms of techniques, complexity, performance, etc. However, none of them specifically addresses AND methods in the context of SKGs, where the entity-relationship structure can be exploited. In this paper, we discuss recent graph-based methods for AND, define a framework through which such methods can be confronted, and catalog the most popular datasets and benchmarks used to test such methods. Finally, we outline possible directions for future work on this topic.Source: PEERJ. COMPUTER SCIENCE., vol. 9
DOI: 10.7717/peerj-cs.1536
Project(s): EOSC Future
, OpenAIRE Nexus Metrics:
See at:
PeerJ Computer Science | CNR IRIS | ISTI Repository | peerj.com | CNR IRIS
2023
Conference article
Open Access
A graph neural network approach for evaluating correctness of groups of duplicates
De Bonis M, Minutella F, Falchi F, Manghi P
Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of "groups of duplicates", which can be used to measure the group's accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.DOI: 10.1007/978-3-031-43849-3_18
Project(s): OpenAIRE Nexus
Metrics:
De Bonis M, Minutella F, Falchi F, Manghi P
Unlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of "groups of duplicates", which can be used to measure the group's accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.DOI: 10.1007/978-3-031-43849-3_18
Project(s): OpenAIRE Nexus
Metrics:
See at:
doi.org | CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2023
Other
Open Access
InfraScience research activity report 2023
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud, EOSC Future , TAILOR
Metrics:
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
2022
Conference article
Open Access
Towards unsupervised machine learning approaches for knowledge graphs
Minutella F, Falchi F, Manghi P, De Bonis M, Messina N
Nowadays, a lot of data is in the form of Knowledge Graphs aiming at representing information as a set of nodes and relationships between them. This paper proposes an efficient framework to create informative embeddings for node classification on large knowledge graphs. Such embeddings capture how a particular node of the graph interacts with his neighborhood and indicate if it is either isolated or part of a bigger clique. Since a homogeneous graph is necessary to perform this kind of analysis, the framework exploits the metapath approach to split the heterogeneous graph into multiple homogeneous graphs. The proposed pipeline includes an unsupervised attentive neural network to merge different metapaths and produce node embeddings suitable for classification. Preliminary experiments on the IMDb dataset demonstrate the validity of the proposed approach, which can defeat current state-of-the-art unsupervised methods.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
Project(s): OpenAIRE Nexus
Minutella F, Falchi F, Manghi P, De Bonis M, Messina N
Nowadays, a lot of data is in the form of Knowledge Graphs aiming at representing information as a set of nodes and relationships between them. This paper proposes an efficient framework to create informative embeddings for node classification on large knowledge graphs. Such embeddings capture how a particular node of the graph interacts with his neighborhood and indicate if it is either isolated or part of a bigger clique. Since a homogeneous graph is necessary to perform this kind of analysis, the framework exploits the metapath approach to split the heterogeneous graph into multiple homogeneous graphs. The proposed pipeline includes an unsupervised attentive neural network to merge different metapaths and produce node embeddings suitable for classification. Preliminary experiments on the IMDb dataset demonstrate the validity of the proposed approach, which can defeat current state-of-the-art unsupervised methods.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
Project(s): OpenAIRE Nexus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
A preliminary assessment of the article deduplication algorithm used for the OpenAIRE Research Graph
Vichos K, De Bonis M, Kanellos I, Chatzopoulos S, Atzori C, Manola N, Manghi P, Vergoulis T
In recent years, a large number of Scholarly Knowledge Graphs (SKGs) have been introduced in the literature. The communities behind these graphs strive to gather, clean, and integrate scholarly metadata from various sources to produce clean and easy-to-process knowledge graphs. In this context, a very important task of the respective cleaning and integration workflows is deduplication. In this paper, we briefly describe and evaluate the accuracy of the deduplication algorithm used for the OpenAIRE Research Graph. Our experiments show that the algorithm has an adequate performance producing a small number of false positives and an even smaller number of false negatives.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
Project(s): OpenAIRE Nexus
Vichos K, De Bonis M, Kanellos I, Chatzopoulos S, Atzori C, Manola N, Manghi P, Vergoulis T
In recent years, a large number of Scholarly Knowledge Graphs (SKGs) have been introduced in the literature. The communities behind these graphs strive to gather, clean, and integrate scholarly metadata from various sources to produce clean and easy-to-process knowledge graphs. In this context, a very important task of the respective cleaning and integration workflows is deduplication. In this paper, we briefly describe and evaluate the accuracy of the deduplication algorithm used for the OpenAIRE Research Graph. Our experiments show that the algorithm has an adequate performance producing a small number of false positives and an even smaller number of false negatives.Source: CEUR WORKSHOP PROCEEDINGS. Padua, Italy, 24-25/02/2022
Project(s): OpenAIRE Nexus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
InfraScience research activity report 2021
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
Project(s): ARIADNEplus, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
Project(s): ARIADNEplus
, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Journal article
Open Access
FDup: a framework for general-purpose and efficient entity deduplication of record collections
De Bonis M., Manghi P., Atzori C.
Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of "blocking" and "sliding window", by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.Source: PEERJ. COMPUTER SCIENCE., vol. 8 (issue e1058)
DOI: 10.7717/peerj-cs.1058
Project(s): OpenAIRE Nexus
Metrics:
De Bonis M., Manghi P., Atzori C.
Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of "blocking" and "sliding window", by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.Source: PEERJ. COMPUTER SCIENCE., vol. 8 (issue e1058)
DOI: 10.7717/peerj-cs.1058
Project(s): OpenAIRE Nexus
Metrics:
See at:
OpenAIRE | CNR IRIS | ISTI Repository | peerj.com | CNR IRIS
2022
Software
Metadata Only Access
dnet-dedup framework
Artini M., Atzori C., Bardi A., Baglioni M., De Bonis M., Dell'Amico A., La Bruzzo S. F., Mannocci A., Manghi P.
The GDup Software enables an integrated, scalable, general-purpose system for entity deduplication over big information graphs. GDup supports practitioners with the functionalities needed to realize a fully-fledged entity deduplication workflow over a generic input graph, including Ground Truth support, end-user feedback, and strategies for identifying and merging duplicates to obtain an output disambiguated graph. GDup is today one of the core components of the OpenAIRE infrastructure production system, monitoring Open Science trends on behalf of the European Commission.Project(s): OpenAIRE-Advance, OpenAIRE Nexus
Artini M., Atzori C., Bardi A., Baglioni M., De Bonis M., Dell'Amico A., La Bruzzo S. F., Mannocci A., Manghi P.
The GDup Software enables an integrated, scalable, general-purpose system for entity deduplication over big information graphs. GDup supports practitioners with the functionalities needed to realize a fully-fledged entity deduplication workflow over a generic input graph, including Ground Truth support, end-user feedback, and strategies for identifying and merging duplicates to obtain an output disambiguated graph. GDup is today one of the core components of the OpenAIRE infrastructure production system, monitoring Open Science trends on behalf of the European Commission.Project(s): OpenAIRE-Advance
, OpenAIRE Nexus
See at:
github.com | CNR IRIS
2022
Other
Open Access
Data model description of the OpenAIRE Research Graph
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Mannocci A, Manghi P, Pavone G
The OpenAIRE Graph (formerly known as the OpenAIRE Research Graph) is one of the largest open scholarly record collections worldwide, key to fostering Open Science and establishing its practices in daily research activities. Conceived as a public and transparent good, populated out of data sources trusted by scientists, the Graph aims at bringing discovery, monitoring, and assessment of science back into the hands of the scientific community. Imagine a vast collection of research products all linked together, contextualized, and openly available. For the past years, OpenAIRE has been working to gather this valuable record. It is a massive collection of metadata and links between scientific products such as articles, datasets, software, and other research products, entities like organizations, funders, funding streams, projects, communities, and data sources. This technical Report describes the public data model adopted by the OpenAIRE Graph.DOI: 10.32079/isti-tr-2022/031
Metrics:
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Mannocci A, Manghi P, Pavone G
The OpenAIRE Graph (formerly known as the OpenAIRE Research Graph) is one of the largest open scholarly record collections worldwide, key to fostering Open Science and establishing its practices in daily research activities. Conceived as a public and transparent good, populated out of data sources trusted by scientists, the Graph aims at bringing discovery, monitoring, and assessment of science back into the hands of the scientific community. Imagine a vast collection of research products all linked together, contextualized, and openly available. For the past years, OpenAIRE has been working to gather this valuable record. It is a massive collection of metadata and links between scientific products such as articles, datasets, software, and other research products, entities like organizations, funders, funding streams, projects, communities, and data sources. This technical Report describes the public data model adopted by the OpenAIRE Graph.DOI: 10.32079/isti-tr-2022/031
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
OpenAIRE Research Graph: aggregation workflow
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Dell'Amico A, Mannocci A, Manghi P, Pavone G
The OpenAIRE Graph (formerly the OpenAIRE Research Graph) is one of the largest open scholarly record collections worldwide. It is key in fostering Open Science and establishing its practices in daily research activities. Conceived as a public and transparent good, populated out of data sources trusted by scientists, the Graph aims at bringing discovery, monitoring, and assessment of science back into the hands of the scientific community. OpenAIRE collects metadata records from more than 70K scholarly communication sources worldwide, including Open Access institutional repositories, data archives, and journals. All the metadata records (i.e., descriptions of research products) are put together in a data lake with records from Crossref, Unpaywall, ORCID, ROR, and information about projects provided by national and international funders. This technical Report describes the main Aggregation Workflow to orchestrate the data aggregation and the implemented mapping from some of the main datasources into the OpenAIRE research graph data model.DOI: 10.32079/isti-tr-2022/033
Project(s): OpenAIRE-Advance, OpenAIRE Nexus
Metrics:
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Dell'Amico A, Mannocci A, Manghi P, Pavone G
The OpenAIRE Graph (formerly the OpenAIRE Research Graph) is one of the largest open scholarly record collections worldwide. It is key in fostering Open Science and establishing its practices in daily research activities. Conceived as a public and transparent good, populated out of data sources trusted by scientists, the Graph aims at bringing discovery, monitoring, and assessment of science back into the hands of the scientific community. OpenAIRE collects metadata records from more than 70K scholarly communication sources worldwide, including Open Access institutional repositories, data archives, and journals. All the metadata records (i.e., descriptions of research products) are put together in a data lake with records from Crossref, Unpaywall, ORCID, ROR, and information about projects provided by national and international funders. This technical Report describes the main Aggregation Workflow to orchestrate the data aggregation and the implemented mapping from some of the main datasources into the OpenAIRE research graph data model.DOI: 10.32079/isti-tr-2022/033
Project(s): OpenAIRE-Advance
, OpenAIRE Nexus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
OpenAIRE Research Graph deduplication workflow
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Mannocci A, Manghi P, Pavone G
The OpenAIRE aggregation workflow can collect metadata records from different providers about the same scholarly work. Each metadata record can carry different information because, for example, some providers are not aware of links to projects, keywords, or other details. Another typical case is when OpenAIRE collects one metadata record from a repository about a pre-print and another from a journal about the published article. To provide correct statistics, OpenAIRE must identify those cases and "merge" the two metadata records so that the scholarly work is counted only once in the statistics OpenAIRE produces. This technical Report describes the Deduplication workflow and technique adopted to deduplicate the OpenAIRE Graph.DOI: 10.32079/isti-tr-2022/032
Project(s): OpenAIRE-Connect, OpenAIRE Nexus
Metrics:
La Bruzzo Sf, Artini M, Atzori C, Bardi A, Baglioni M, De Bonis M, Mannocci A, Manghi P, Pavone G
The OpenAIRE aggregation workflow can collect metadata records from different providers about the same scholarly work. Each metadata record can carry different information because, for example, some providers are not aware of links to projects, keywords, or other details. Another typical case is when OpenAIRE collects one metadata record from a repository about a pre-print and another from a journal about the published article. To provide correct statistics, OpenAIRE must identify those cases and "merge" the two metadata records so that the scholarly work is counted only once in the statistics OpenAIRE produces. This technical Report describes the Deduplication workflow and technique adopted to deduplicate the OpenAIRE Graph.DOI: 10.32079/isti-tr-2022/032
Project(s): OpenAIRE-Connect
, OpenAIRE Nexus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
OpenOrgs: a tool for the disambiguation of organizations
Artini M, La Bruzzo Sf, De Bonis M, Pavone G
Organizations appear all over the Research & Innovation ecosystem in different shapes and formats: the same organization may appear with different metadata fields, different names - e.g., full legal name, short or alternative names, acronym. The ambiguity of organizations results in a huge deficiency in the exchange of information, the findability of research products, the monitoring of activities, and ultimately building a linked open scholarly communication system. OpenOrgs combines an automated process and human curation to compensate for the lack of information available and improve the organization's discoverability.DOI: 10.32079/isti-tr-2022/034
Project(s): OpenAIRE-Advance, OpenAIRE Nexus
Metrics:
Artini M, La Bruzzo Sf, De Bonis M, Pavone G
Organizations appear all over the Research & Innovation ecosystem in different shapes and formats: the same organization may appear with different metadata fields, different names - e.g., full legal name, short or alternative names, acronym. The ambiguity of organizations results in a huge deficiency in the exchange of information, the findability of research products, the monitoring of activities, and ultimately building a linked open scholarly communication system. OpenOrgs combines an automated process and human curation to compensate for the lack of information available and improve the organization's discoverability.DOI: 10.32079/isti-tr-2022/034
Project(s): OpenAIRE-Advance
, OpenAIRE Nexus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
InfraScience research activity report 2022
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
Project(s): ARIADNEplus, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
Project(s): ARIADNEplus
, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Dataset
Metadata Only Access
OpenAIRE research graph: dumps for research communities and initiatives
Manghi P, Atzori C, Bardi A, Baglioni M, Schirrwagen J, Dimitropoulos H, La Bruzzo S, Foufoulas I, Lohden A, Backer A, Mannocci A, Horst M, Czerniak A, Kiatropoulou K, Kokogiannaki A, De Bonis M, Artini M, Ottonello E, Lempesis A, Ioannidis A, Summan F
This dataset contains dumps of the OpenAIRE Research Graph containing metadata records relevant for the research communities and initiatives collaborating with OpenAIRE. Each dataset is a tar file containing gzip files with one json per line. Each json is compliant to the schema available at DOI: 10.5281/zenodo.3974226DOI: 10.5281/zenodo.3974604
Project(s): RISIS 2, BE OPEN , OpenAIRE-Advance
Metrics:
Manghi P, Atzori C, Bardi A, Baglioni M, Schirrwagen J, Dimitropoulos H, La Bruzzo S, Foufoulas I, Lohden A, Backer A, Mannocci A, Horst M, Czerniak A, Kiatropoulou K, Kokogiannaki A, De Bonis M, Artini M, Ottonello E, Lempesis A, Ioannidis A, Summan F
This dataset contains dumps of the OpenAIRE Research Graph containing metadata records relevant for the research communities and initiatives collaborating with OpenAIRE. Each dataset is a tar file containing gzip files with one json per line. Each json is compliant to the schema available at DOI: 10.5281/zenodo.3974226DOI: 10.5281/zenodo.3974604
Project(s): RISIS 2
, BE OPEN , OpenAIRE-Advance Metrics:
See at:
CNR IRIS
2021
Dataset
Open Access
OpenAIRE Covid-19 publications, datasets, software and projects metadata
Bardi A., Kuchma I., Pavone G., Artini M., Atzori C., Backer A., Baglioni M., Czerniak A., De Bonis M., Dimitropoulos H., Foufoulas I., Horst M., Iatropoulou K., Jacewicz P., Kokogiannaki A., La Bruzzo S., Lazzeri E., Lohden A., Manghi P., Mannocci A., Manola N., Ottonello E., Schirrwagen J.
This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway (https://covid-19.openaire.eu/), identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph (https://explore.openaire.eu/). The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.The dump consists of a gzip file containing one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.3974226DOI: 10.5281/zenodo.3980490
Project(s): OpenAIRE-Advance
Metrics:
Bardi A., Kuchma I., Pavone G., Artini M., Atzori C., Backer A., Baglioni M., Czerniak A., De Bonis M., Dimitropoulos H., Foufoulas I., Horst M., Iatropoulou K., Jacewicz P., Kokogiannaki A., La Bruzzo S., Lazzeri E., Lohden A., Manghi P., Mannocci A., Manola N., Ottonello E., Schirrwagen J.
This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway (https://covid-19.openaire.eu/), identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph (https://explore.openaire.eu/). The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.The dump consists of a gzip file containing one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.3974226DOI: 10.5281/zenodo.3980490
Project(s): OpenAIRE-Advance
Metrics: