Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Databases/Information Systems
Title: A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search
Abstract: This paper proposes a methodology which discriminates the articles by the target authors (“true” articles) from those by other homonymous authors (“false” articles). Author name searches for 2,595 “source” authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90–95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely. © 2011 Wiley Periodicals, Inc.
Extracted statement — Evidence: Affiliation
Research field: Databases/Information Systems
Title: A Real-time Heuristic-based Unsupervised Method for Name Disambiguation in Digital Libraries
Abstract: This paper addresses the problem of name disambiguation in the context of digital libraries that administer bibliographic citations. The problem occurs when multiple authors share a common name or when multiple name variations for an author appear in citation records. Name disambiguation is not a trivial task, and most digital libraries do not provide an ecient way to accurately identify the citation records for an author. Furthermore, lack of complete meta-data information in digital libraries hinders the development of a generic algorithm that can be applicable to any dataset. We propose a heuristic-based, unsupervised and adaptive method that also examines users’ interactions in order to include users’ feedback in the disambiguation process. Moreover, the method exploits important features associated with author and citation records, such as co-authors, aliation, publication title, venue, etc., creating a multilayered hierarchical clustering algorithm which transforms itself according to the available information, and forms clusters of unambiguous records. Our experiments on a set of researchers’ names considered to be highly ambiguous produced high precision and recall results, and decisively armed the viability of our algorithm.
Extracted statement — Evidence: Authors
Research field: Databases/Information Systems
Title: Citation-based bootstrapping for large-scale author disambiguation
Abstract: We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first “bootstrap” stage, a collection of high-precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self-supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1. © 2012 Wiley Periodicals, Inc.
Extracted statement — Evidence: Authors
Research field: Databases/Information Systems
Title: Self-training author name disambiguation for information scarce scenarios
Abstract: We present a novel 3‐step self‐training method for author name disambiguation—SAND (self‐training associative name disambiguator)—which requires no manual labeling, no parameterization (in real‐world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real‐world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state‐of‐the‐art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self‐citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information.
Extracted statement — Evidence: Authors
Research field: Databases/Information Systems
Title: Network based Framework for Author Name Disambiguation Applications
Abstract: With the rapid development of digital libraries, name disambiguation becomes more and more important technique to distinguish authors with same names from physical persons. Many algorithms have been developed to accomplish the task. However, they are usually based on some restricted preconditions and rarely concern how to be incorporated into a practical application. In this paper, name disambiguation is regarded as the technique of learning module integrated with a knowledge base. A network is defined for the modeling of publication information, which facilitates the representation of differenttypes of relations among the attributes. The knowledge base component serves as the user interface for domain knowledge input. Furthermore, this paper exploits a random walk with restart algorithm and affinity propagation clustering algorithm to finally output name disambiguation results.
Extracted statement — Evidence: Authors
Research field: Databases/Information Systems
Title: A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search
Abstract: This paper proposes a methodology which discriminates the articles by the target authors (“true” articles) from those by other homonymous authors (“false” articles). Author name searches for 2,595 “source” authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90–95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely. © 2011 Wiley Periodicals, Inc.
Extracted statement — Evidence: Country
Research field: Databases/Information Systems
Title: Heuristics-based query optimisation for SPARQL
Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Extracted statement — Has implementation: MonetDB
Research field: Databases/Information Systems
Title: A learning object ontology repository to support annotation and discovery of educational resources using semantic thesauri
Abstract: <jats:p> Open educational resources are currently becoming increasingly available from a multitude of sources and are consequently annotated in many diverse ways. Interoperability concerns that naturally arise can often be resolved through the semantification of metadata descriptions, while at the same time strengthening the knowledge value of resources. SKOS can be a solid linking point offering a standard vocabulary for thematic descriptions, by referencing semantic thesauri. We propose the enhancement and maintenance of educational resources’ metadata in the form of learning object ontologies and introduce the notion of a learning object ontology repository that can help towards their publication, discovery and reuse. At the same time, linking to thesauri datasets and contextualized sources interrelates learning objects with linked data and exposes them to the Web of Data. We build a set of extensions and workflows on top of contemporary ontology management tools, such as WebProtégé, that can make it suitable as a learning object ontology repository. The proposed approach and implementation can help libraries and universities in discovering, managing and incorporating open educational resources and enhancing current curricula. </jats:p>
Extracted statement — keywords: ontology
Research field: Databases/Information Systems
Title: A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search
Abstract: This paper proposes a methodology which discriminates the articles by the target authors (“true” articles) from those by other homonymous authors (“false” articles). Author name searches for 2,595 “source” authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90–95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely. © 2011 Wiley Periodicals, Inc.
Extracted statement — Performance metric: Precision
Research field: Databases/Information Systems
Title: A Real-time Heuristic-based Unsupervised Method for Name Disambiguation in Digital Libraries
Abstract: This paper addresses the problem of name disambiguation in the context of digital libraries that administer bibliographic citations. The problem occurs when multiple authors share a common name or when multiple name variations for an author appear in citation records. Name disambiguation is not a trivial task, and most digital libraries do not provide an ecient way to accurately identify the citation records for an author. Furthermore, lack of complete meta-data information in digital libraries hinders the development of a generic algorithm that can be applicable to any dataset. We propose a heuristic-based, unsupervised and adaptive method that also examines users’ interactions in order to include users’ feedback in the disambiguation process. Moreover, the method exploits important features associated with author and citation records, such as co-authors, aliation, publication title, venue, etc., creating a multilayered hierarchical clustering algorithm which transforms itself according to the available information, and forms clusters of unambiguous records. Our experiments on a set of researchers’ names considered to be highly ambiguous produced high precision and recall results, and decisively armed the viability of our algorithm.
Extracted statement — Performance metric: Precision
Research field: Databases/Information Systems
Title: On Graph-Based Name Disambiguation
Abstract: Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation . In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall .
Extracted statement — Performance metric: Precision
Research field: Databases/Information Systems
Title: A learning object ontology repository to support annotation and discovery of educational resources using semantic thesauri
Abstract: <jats:p> Open educational resources are currently becoming increasingly available from a multitude of sources and are consequently annotated in many diverse ways. Interoperability concerns that naturally arise can often be resolved through the semantification of metadata descriptions, while at the same time strengthening the knowledge value of resources. SKOS can be a solid linking point offering a standard vocabulary for thematic descriptions, by referencing semantic thesauri. We propose the enhancement and maintenance of educational resources’ metadata in the form of learning object ontologies and introduce the notion of a learning object ontology repository that can help towards their publication, discovery and reuse. At the same time, linking to thesauri datasets and contextualized sources interrelates learning objects with linked data and exposes them to the Web of Data. We build a set of extensions and workflows on top of contemporary ontology management tools, such as WebProtégé, that can make it suitable as a learning object ontology repository. The proposed approach and implementation can help libraries and universities in discovering, managing and incorporating open educational resources and enhancing current curricula. </jats:p>
Extracted statement — Reuse of existing vocabularies: thesauri
Research field: Databases/Information Systems
Title: A fast method based on multiple clustering for name disambiguation in bibliographic citations
Abstract: Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse‐to‐fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state‐of‐the‐art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.
Extracted statement — Evidence: Venues
Research field: Databases/Information Systems
Title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library
Abstract: Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.
Extracted statement — Evidence: Year
Research field: Databases/Information Systems
Title: A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search
Abstract: This paper proposes a methodology which discriminates the articles by the target authors (“true” articles) from those by other homonymous authors (“false” articles). Author name searches for 2,595 “source” authors in six subject fields retrieved about 629,000 articles. In order to extract true articles from the large amount of the retrieved articles, including many false ones, two filtering stages were applied. At the first stage any retrieved article was eliminated as false if either its affiliation addresses had little similarity to those of its source article or there was no citation relationship between the journal of the retrieved article and that of its source article. At the second stage, a sample of retrieved articles was subjected to manual judgment, and utilizing the judgment results, discrimination functions based on logistic regression were defined. These discrimination functions demonstrated both the recall ratio and the precision of about 95% and the accuracy (correct answer ratio) of 90–95%. Existence of common coauthor(s), address similarity, title words similarity, and interjournal citation relationships between the retrieved and source articles were found to be the effective discrimination predictors. Whether or not the source author was from a specific country was also one of the important predictors. Furthermore, it was shown that a retrieved article is almost certainly true if it was cited by, or cocited with, its source article. The method proposed in this study would be effective when dealing with a large number of articles whose subject fields and affiliation addresses vary widely. © 2011 Wiley Periodicals, Inc.
Extracted statement — Performance metric: Recall