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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.
2020
Journal article
Open Access
Entity deduplication in big data graphs for scholarly communication
Manghi P., Atzori C., De Bonis M., Bardi A.
Purpose: Several online services offer functionalities to access information from "big research graphs" (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts. Design/methodology/approach: This work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments. Findings: GDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph. Originality/value: To our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.Source: DATA TECHNOLOGIES AND APPLICATIONS, vol. 54, pp. 409-435
DOI: 10.1108/dta-09-2019-0163
Project(s): OpenAIRE2020
, OpenAIRE-Advance
Metrics:
Manghi P., Atzori C., De Bonis M., Bardi A.
Purpose: Several online services offer functionalities to access information from "big research graphs" (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts. Design/methodology/approach: This work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments. Findings: GDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph. Originality/value: To our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.Source: DATA TECHNOLOGIES AND APPLICATIONS, vol. 54, pp. 409-435
DOI: 10.1108/dta-09-2019-0163
Project(s): OpenAIRE2020
, OpenAIRE-Advance Metrics:
See at:
Data Technologies and Applications 
| CNR IRIS | ISTI Repository | www.emerald.com | Data Technologies and Applications | CNR IRIS 
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
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
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
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
2024
Conference article
Open Access
FDup: a framework for general-purpose and 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
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
2019
Conference article
Open Access
Deep learning techniques for visual food recognition on a mobile app
De Bonis M, Amato G, Falchi F, Gennaro C, Manghi P
The paper provides an efficient solution to implement a mobile application for food recognition using Convolutional Neural Networks (CNNs). Different CNNs architectures have been trained and tested on two datasets available in literature and the best one in terms of accuracy has been chosen. Since our CNN runs on a mobile phone, efficiency measurements have also taken into account both in terms of memory and computational requirements. The mobile application has been implemented relying on RenderScript and the weights of every layer have been serialized in different files stored in the mobile phone memory. Extensive experiments have been carried out to choose the optimal configuration and tuning parameters.Source: ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING, vol. 833, pp. 303-312. Wroclaw; Poland, 12-14 September 2018
DOI: 10.1007/978-3-319-98678-4_31
Metrics:
De Bonis M, Amato G, Falchi F, Gennaro C, Manghi P
The paper provides an efficient solution to implement a mobile application for food recognition using Convolutional Neural Networks (CNNs). Different CNNs architectures have been trained and tested on two datasets available in literature and the best one in terms of accuracy has been chosen. Since our CNN runs on a mobile phone, efficiency measurements have also taken into account both in terms of memory and computational requirements. The mobile application has been implemented relying on RenderScript and the weights of every layer have been serialized in different files stored in the mobile phone memory. Extensive experiments have been carried out to choose the optimal configuration and tuning parameters.Source: ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING, vol. 833, pp. 303-312. Wroclaw; Poland, 12-14 September 2018
DOI: 10.1007/978-3-319-98678-4_31
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.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
2018
Conference article
Open Access
OpenAIRE: Advancing open science
Manghi P, Artini M, Atzori C, Baglioni M, Bardi A, La Bruzzo S, De Bonis M, Dimitropoulos H, Foufoulas I, Iatropoulou K, Manola N, Martziou S, Principe P
OpenAIRE, the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Source: THE GL-CONFERENCE SERIES. CONFERENCE PROCEEDINGS, pp. 107-112. Roma, Italy, 23-24 October 2017
DOI: 10.26069/greynet-2018-000.010-gg
Project(s): OpenAIRE-Connect
Metrics:
Manghi P, Artini M, Atzori C, Baglioni M, Bardi A, La Bruzzo S, De Bonis M, Dimitropoulos H, Foufoulas I, Iatropoulou K, Manola N, Martziou S, Principe P
OpenAIRE, the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Source: THE GL-CONFERENCE SERIES. CONFERENCE PROCEEDINGS, pp. 107-112. Roma, Italy, 23-24 October 2017
DOI: 10.26069/greynet-2018-000.010-gg
Project(s): OpenAIRE-Connect
Metrics:
See at:
greyguide.isti.cnr.it | CNR IRIS | ISTI Repository | CNR IRIS
2019
Journal article
Open Access
OpenAIRE: Advancing open science
Manghi P, Artini M, Atzori C, Baglioni M, Bardi A, La Bruzzo S, De Bonis M, Dimitropoulos H, Foufoulas I, Iatropoulou K, Manola N, Martziou S, Principe P
OpenAIRE, the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Source: THE GREY JOURNAL, vol. 15 (issue 3), pp. 141-146
Project(s): OpenAIRE-Connect
Manghi P, Artini M, Atzori C, Baglioni M, Bardi A, La Bruzzo S, De Bonis M, Dimitropoulos H, Foufoulas I, Iatropoulou K, Manola N, Martziou S, Principe P
OpenAIRE, the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Source: THE GREY JOURNAL, vol. 15 (issue 3), pp. 141-146
Project(s): OpenAIRE-Connect
See at:
CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2017
Contribution to conference
Open Access
OpenAIRE: The OpenScience European Infrastructure
Artini M, Atzori C, Baglioni M, Bardi A, Biagioni S, De Bonis M, Dell'Amico A, La Bruzzo S, Manghi P
OpenAIRE , the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Project(s): OpenAIRE-Advance
Artini M, Atzori C, Baglioni M, Bardi A, Biagioni S, De Bonis M, Dell'Amico A, La Bruzzo S, Manghi P
OpenAIRE , the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today's scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.Project(s): OpenAIRE-Advance
See at:
greyguide.isti.cnr.it | CNR IRIS | ISTI Repository | CNR IRIS
2019
Other
Open Access
The OpenAIRE research graph: third-party publishing APIs
Atzori C, Baglioni M, Bardi A, Manghi P, La Bruzzo S, De Bonis M, Dell'Amico A, Artini M, Mannocci A, Ottonello E
This work describes the specification of the OpenAIRE publishing APIs that support third-party services at publishing metadata about interlinked and packaged research products into the OpenAIRE Research Graph, in respect of the OpenAIRE interoperability guidelines (https://guidelines.openaire.eu). Research products generated by researchers using services of research infrastructures are today manually published by researchers in a repository external to their research infrastructure. This phase is often considered an extra burden, because researchers have to fill in metadata forms with information that is already available in the scope of the services they used. By using the OpenAIRE publishing APIs, services of research infrastructures can implement an on-demand publishing workflow for any type of research products to support their researchers at improving the FAIRness of their research products and relief them from the tedious step of finding a suitable repository and manually depositing the products in it.
Atzori C, Baglioni M, Bardi A, Manghi P, La Bruzzo S, De Bonis M, Dell'Amico A, Artini M, Mannocci A, Ottonello E
This work describes the specification of the OpenAIRE publishing APIs that support third-party services at publishing metadata about interlinked and packaged research products into the OpenAIRE Research Graph, in respect of the OpenAIRE interoperability guidelines (https://guidelines.openaire.eu). Research products generated by researchers using services of research infrastructures are today manually published by researchers in a repository external to their research infrastructure. This phase is often considered an extra burden, because researchers have to fill in metadata forms with information that is already available in the scope of the services they used. By using the OpenAIRE publishing APIs, services of research infrastructures can implement an on-demand publishing workflow for any type of research products to support their researchers at improving the FAIRness of their research products and relief them from the tedious step of finding a suitable repository and manually depositing the products in it.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2019
Conference article
Open Access
The OpenAIRE Research Community Dashboard: On Blending Scientific Workflows and Scientific Publishing
Baglioni M, Bardi A, Kokogiannaki A, Manghi P, Iatropoulou K, Principe P, Vieira A, Nielsen L H, Dimitropoulos H, Foufoulas I, Manola N, Atzori C, La Bruzzo S, Lazzeri E, Artini M, De Bonis M, Dell'Amico A
Despite the hype, the effective implementation of Open Science is hindered by several cultural and technical barriers. Researchers embraced digital science, use "digital laboratories" (e.g. research infrastructures, thematic services) to conduct their research and publish research data, but practices and tools are still far from achieving the expectations of transparency and reproducibility of Open Science. The places where science is performed and the places where science is published are still regarded as different realms. Publishing is still a post-experimental, tedious, manual process, too often limited to articles, in some contexts semantically linked to datasets, rarely to software, generally disregarding digital representations of experiments. In this work we present the OpenAIRE Research Community Dashboard (RCD), designed to overcome some of these barriers for a given research community, minimizing the technical efforts and without renouncing any of the community services or practices. The RCD flanks digital laboratories of research communities with scholarly communication tools for discovering and publishing interlinked scientific products such as literature, datasets, and software. The benefits of the RCD are show-cased by means of two real-case scenarios: the European Marine Science community and the European Plate Observing System (EPOS) research infrastructure.DOI: 10.1007/978-3-030-30760-8_5
DOI: 10.5281/zenodo.3467104
DOI: 10.5281/zenodo.3467103
Project(s): OpenAIRE-Connect, OpenAIRE-Advance
Metrics:
Baglioni M, Bardi A, Kokogiannaki A, Manghi P, Iatropoulou K, Principe P, Vieira A, Nielsen L H, Dimitropoulos H, Foufoulas I, Manola N, Atzori C, La Bruzzo S, Lazzeri E, Artini M, De Bonis M, Dell'Amico A
Despite the hype, the effective implementation of Open Science is hindered by several cultural and technical barriers. Researchers embraced digital science, use "digital laboratories" (e.g. research infrastructures, thematic services) to conduct their research and publish research data, but practices and tools are still far from achieving the expectations of transparency and reproducibility of Open Science. The places where science is performed and the places where science is published are still regarded as different realms. Publishing is still a post-experimental, tedious, manual process, too often limited to articles, in some contexts semantically linked to datasets, rarely to software, generally disregarding digital representations of experiments. In this work we present the OpenAIRE Research Community Dashboard (RCD), designed to overcome some of these barriers for a given research community, minimizing the technical efforts and without renouncing any of the community services or practices. The RCD flanks digital laboratories of research communities with scholarly communication tools for discovering and publishing interlinked scientific products such as literature, datasets, and software. The benefits of the RCD are show-cased by means of two real-case scenarios: the European Marine Science community and the European Plate Observing System (EPOS) research infrastructure.DOI: 10.1007/978-3-030-30760-8_5
DOI: 10.5281/zenodo.3467104
DOI: 10.5281/zenodo.3467103
Project(s): OpenAIRE-Connect
, OpenAIRE-Advance Metrics:
See at:
ZENODO | ZENODO | Universidade do Minho: RepositoriUM | CNR IRIS | ISTI Repository | repositorium.sdum.uminho.pt | doi.org | CNR IRIS | CNR IRIS | CNR IRIS
2019
Dataset
Metadata Only Access
OpenAIRE Research Graph Dump
Manghi P, Atzori C, Bardi A, Schirrwagen J, Dimitropoulos H, La Bruzzo S, Foufoulas I, Loehden A, Baecker A, Mannocci A, Horst M, Baglioni M, Czerniak A, Kiatropoulou K, Kokogiannaki A, De Bonis M, Artini M, Ottonello E, Lempesis A, Nielsen L H, Ioannidis A, Bigarella C, Summan F
The OpenAIRE Research Graph is one of the largest open scholarly record collections worldwide, key in fostering Open Science and establishing its practices in the 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 in the hands of the scientific community. Imagine a vast collection of research products all linked together, contextualised and openly available. For the past ten years OpenAIRE has been working to gather this valuable record. OpenAIRE is pleased to announce the beta release of its Research Graph, a massive collection of metadata and links between scientific products such as articles, datasets, software, and other research products, entities like organisations, funders, funding streams, projects, communities, and data sources. As of today, the OpenAIRE Research Graph aggregates around 450Mi metadata records with links collecting from 10,000 data sources trusted by scientists, including repositories registered in OpenDOAR, Open Access journals registered in DOAJ, Crossref, Unpaywall, ORCID and Microsoft Academic Graph. After cleaning, deduplication, and fine-grained classification processes, they narrow down to ~100Mi publications, ~8Mi datasets, ~200K software research products, 8Mi other products linked together with semantic relations. More than 10Mi full-texts of Open Access publications are mined by algorithms to enrich metadata records with additional properties and links among research products, funders, projects, communities, and organizations. Thanks to the mining algorithm, the graph is completed with 480Mi semantic relations. The OpenAIRE Research graph is available via our BETA Explore Portal and you can download it from Zenodo.DOI: 10.5281/zenodo.3516918
Project(s): OpenAIRE-Advance
Metrics:
Manghi P, Atzori C, Bardi A, Schirrwagen J, Dimitropoulos H, La Bruzzo S, Foufoulas I, Loehden A, Baecker A, Mannocci A, Horst M, Baglioni M, Czerniak A, Kiatropoulou K, Kokogiannaki A, De Bonis M, Artini M, Ottonello E, Lempesis A, Nielsen L H, Ioannidis A, Bigarella C, Summan F
The OpenAIRE Research Graph is one of the largest open scholarly record collections worldwide, key in fostering Open Science and establishing its practices in the 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 in the hands of the scientific community. Imagine a vast collection of research products all linked together, contextualised and openly available. For the past ten years OpenAIRE has been working to gather this valuable record. OpenAIRE is pleased to announce the beta release of its Research Graph, a massive collection of metadata and links between scientific products such as articles, datasets, software, and other research products, entities like organisations, funders, funding streams, projects, communities, and data sources. As of today, the OpenAIRE Research Graph aggregates around 450Mi metadata records with links collecting from 10,000 data sources trusted by scientists, including repositories registered in OpenDOAR, Open Access journals registered in DOAJ, Crossref, Unpaywall, ORCID and Microsoft Academic Graph. After cleaning, deduplication, and fine-grained classification processes, they narrow down to ~100Mi publications, ~8Mi datasets, ~200K software research products, 8Mi other products linked together with semantic relations. More than 10Mi full-texts of Open Access publications are mined by algorithms to enrich metadata records with additional properties and links among research products, funders, projects, communities, and organizations. Thanks to the mining algorithm, the graph is completed with 480Mi semantic relations. The OpenAIRE Research graph is available via our BETA Explore Portal and you can download it from Zenodo.DOI: 10.5281/zenodo.3516918
Project(s): OpenAIRE-Advance
Metrics:
See at:
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
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