Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — Data: reference/citation counts
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — Material: similar venues
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — Data: subject-matter novelty
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — Material: the bibliographic database JSTOR
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — Data: their novel journal publications
Research field: Semantic Web
Title: DistSim - Scalable Distributed in-Memory Semantic Similarity Estimation for RDF Knowledge Graphs
Abstract: In this paper, we present DistSim, a Scalable Distributed in-Memory Semantic Similarity Estimation framework for Knowledge Graphs. DistSim provides a multitude of state-of-the-art similarity estimators. We have developed the Similarity Estimation Pipeline by combining generic software modules. For large scale RDF data, DistSim proposes MinHash with locality sensitivity hashing to achieve better scalability over all-pair similarity estimations. The modules of DistSim can be set up using a multitude of (hyper)-parameters allowing to adjust the tradeoff between information taken into account, and processing time. Furthermore, the output of the Similarity Estimation Pipeline is native RDF. DistSim is integrated into the SANSA stack, documented in scala-docs, and covered by unit tests. Additionally, the variables and provided methods follow the Apache Spark MLlib name-space conventions. The performance of DistSim was tested over a distributed cluster, for the dimensions of data set size and processing power versus processing time, which shows the scalability of DistSim w.r.t. increasing data set sizes and processing power. DistSim is already in use for solving several RDF data analytics related use cases. Additionally, DistSim is available and integrated into the open-source GitHub project SANSA.
Extracted statement — Scalability Framework: Apache Spark
Research field: Semantic Web
Title: Mapping ER Schemas to OWL Ontologies
Abstract: As the Semantic Web initiative gains momentum, a fundamental problem of integrating existing data-intensive WWW applications into the Semantic Web emerges. In order for today’s relational database supported Web applications to transparently participate in the Semantic Web, their associated database schemas need to be converted into semantically equivalent ontologies. In this paper we present a solution to an important special case of the automatic mapping problem with wide applicability: mapping well-formed Entity-Relationship (ER) schemas to semantically equivalent OWL Lite ontologies. We present a set of mapping rules that fully capture the ER schema semantics, along with an overview of an implementation of the complete mapping algorithm integrated into the current SFSU ER Design Tools software.
Extracted statement — Learning method: Automatic mapping
Research field: Semantic Web
Title: Multimedia ontology learning for automatic annotation and video browsing
Abstract: In this work, we offer an approach to combine standard multimedia analysis techniques with knowledge drawn from conceptual metadata provided by domain experts of a specialized scholarly domain, to learn a domain-specific multimedia ontology from a set of annotated examples. A standard Bayesian network learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level MPEG-7 based features representing audio-visual content of the videos. We construct a more robust and refined version of this ontology by learning from this set of conceptually annotated videos. To encode this knowledge, we use MOWL, a multimedia extension of Web Ontology Language (OWL) which is capable of describing domain concepts in terms of their media properties and of capturing the inherent uncertainties involved. We use the ontology specified knowledge for recognizing concepts relevant to a video to annotate fresh addition to the video database with relevant concepts in the ontology. These conceptual annotations are used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user.
Extracted statement — Learning method: Bayesian network
Research field: Semantic Web
Title: Image based mammographie ontology learning
Abstract: Understanding the content of an image is one of the challenges in the image processing field. Recently, the Content Based Image Retrieval (CBIR) and especially Semantic Content Based Image Retrieval (SCBIR) are the main goal of many research works. In medical field, understanding the content of an image is very helpful in the automatic decision making. In fact, analyzing the semantic information in an image support can assist the doctor to make the adequate diagnosis. This paper presents a new method for mammographic ontology learning from a set of mammographic images. The approach is based on four main modules: (1) the mammography segmentation, (2) the features extraction (3) the local ontology modeling and (4) the global ontology construction basing on merging the local ones. The first module allows detecting the pathological regions in the represented breast. The second module consists on extracting the most important features from the pathological zones. The third module allows modeling a local ontology by representing the pertinent entities (conceptual entities) as well as their correspondent features (shape, size, form, etc.) discovered in the previous step. The last module consists on merging the local ontologies extracted from a set of mammographies in order to obtain a global and exhaustive one. Our approach attempts to fully describe the semantic content of mammographic images in order to perform the domain knowledge modeling.
Extracted statement — Terms learning: Conceptual entities
Research field: Semantic Web
Title: A Deep Learning based Approach for Precise Video Tagging
Abstract: With the increase in smart devices and abundance of video contents, efficient techniques for the indexing, analysis and retrieval of videos are becoming more and more desirable. Improved indexing and automated analysis of millions of videos could be accomplished by getting videos tagged automatically. A lot of existing methods fail to precisely tag videos because of their lack of ability to capture the video context. The context in a video represents the interactions of objects in a scene and their overall meaning. In this work, we propose a novel approach that integrates the video scene ontology with CNN (Convolutional Neural Network) for improved video tagging. Our method captures the content of a video by extracting the information from individual key frames. The key frames are then fed to a CNN based deep learning model to train its parameters. The trained parameters are used to generate the most frequent tags. Highly frequent tags are used to summarize the input video. The proposed technique is benchmarked on the most widely used dataset of video activities, namely, UCF-101. Our method managed to achieve an overall accuracy of 99.8% with an F1- score of 96.2%.
Extracted statement — Learning method: Convolutional Neural Network
Research field: Semantic Web
Title: Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts
Abstract: The objective of this paper is to present the role of Ontology Learning Process in supporting an ontology engineer for creating and maintaining ontologies from textual resources. The knowledge structures that interest us are legal domain-specific ontologies. We will use these ontologies to build legal domain ontology for a Lebanese legal knowledge based system. The domain application of this work is the Lebanese criminal system. Ontologies can be learnt from various sources, such as databases, structured and unstructured documents. Here, the focus is on the acquisition of ontologies from unstructured text, provided as input. In this work, the Ontology Learning Process represents a knowledge extraction phase using Natural Language Processing techniques. The resulted ontology is considered as inexpressive ontology. There is a need to reengineer it in order to build a complete, correct and more expressive domain-specific ontology.
Extracted statement — Application Domain: Criminal system
Research field: Semantic Web
Title: From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions
Abstract: Learning ontologies requires the acquisition of relevant domain concepts and taxonomic, as well as non-taxonomic, relations. In this chapter, we present a methodology for automatic ontology enrichment and document annotation with concepts and relations of an existing domain core ontology. Natural language definitions from available glossaries in a given domain are processed and regular expressions are applied to identify general-purpose and domain-specific relations. We evaluate the methodology performance in extracting hypernymy and non-taxonomic relations. To this end, we annotated and formalized a relevant fragment of the glossary of Art and Architecture (AAT) with a set of 10 relations (plus the hypernymy relation) defined in the CRM CIDOC cultural heritage core ontology, a recent W3C standard. Finally, we assessed the generality of the approach on a set of web pages from the domains of history and biography.
Extracted statement — Application Domain: cultural heritage
Research field: Semantic Web
Title: DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects
Abstract: Abstract While thousands of ontologies exist on the web, a unified system for handling online ontologies – in particular with respect to discovery, versioning, access, quality-control, mappings – has not yet surfaced and users of ontologies struggle with many challenges. In this paper, we present an online ontology interface and augmented archive called DBpedia Archivo, that discovers, crawls, versions and archives ontologies on the DBpedia Databus. Based on this versioned crawl, different features, quality measures and, if possible, fixes are deployed to handle and stabilize the changes in the found ontologies at web-scale. A comparison to existing approaches and ontology repositories is given .
Extracted statement — Has result: DBpedia Archivo
Research field: Semantic Web
Title: Medical Ontology Learning Based on Web Resources
Abstract: In order to deal with heterogeneous knowledge in the medical field, this paper proposes a method which can learn a heavy-weighted medical ontology based on medical glossaries and Web resources. Firstly, terms and taxonomic relations are extracted based on disease and drug glossaries and a light-weighted ontology is constructed, Secondly, non-taxonomic relations are automatically learned from Web resources with linguistic patterns, and the two ontologies (disease and drug) are expanded from light-weighted level towards heavy-weighted level, At last, the disease ontology and drug ontology are integrated to create a practical medical ontology. Experiment shows that this method can integrate and expand medical terms with taxonomic and different kinds of non-taxonomic relations. Our experiments show that the performance is promising.
Extracted statement — output: Disease ontology
Research field: Semantic Web
Title: A Method for Building Domain Ontologies based on the Transformation of UML Models
Abstract: Ontologies are used in the integration of information resources by describing the semantics of the information sources with machine understandable terms and definitions. But, creating an ontology is a difficult and time-consuming process, especially in the early stage of extracting key concepts and relations. This paper proposes a method for domain ontology building by extracting ontological knowledge from UML models of existing systems. We compare the UML model elements with the OWL ones and derive transformation rules between the corresponding model elements. Based on these rules, we define an XSLT document which implements the transformation processes. We expect that the proposed method reduce the cost and time for building domain ontologies with the reuse of existing UML models
Extracted statement — output: Domain ontology
Research field: Semantic Web
Title: Medical Ontology Learning Based on Web Resources
Abstract: In order to deal with heterogeneous knowledge in the medical field, this paper proposes a method which can learn a heavy-weighted medical ontology based on medical glossaries and Web resources. Firstly, terms and taxonomic relations are extracted based on disease and drug glossaries and a light-weighted ontology is constructed, Secondly, non-taxonomic relations are automatically learned from Web resources with linguistic patterns, and the two ontologies (disease and drug) are expanded from light-weighted level towards heavy-weighted level, At last, the disease ontology and drug ontology are integrated to create a practical medical ontology. Experiment shows that this method can integrate and expand medical terms with taxonomic and different kinds of non-taxonomic relations. Our experiments show that the performance is promising.
Extracted statement — data source: Drug glossaries
Research field: Semantic Web
Title: Medical Ontology Learning Based on Web Resources
Abstract: In order to deal with heterogeneous knowledge in the medical field, this paper proposes a method which can learn a heavy-weighted medical ontology based on medical glossaries and Web resources. Firstly, terms and taxonomic relations are extracted based on disease and drug glossaries and a light-weighted ontology is constructed, Secondly, non-taxonomic relations are automatically learned from Web resources with linguistic patterns, and the two ontologies (disease and drug) are expanded from light-weighted level towards heavy-weighted level, At last, the disease ontology and drug ontology are integrated to create a practical medical ontology. Experiment shows that this method can integrate and expand medical terms with taxonomic and different kinds of non-taxonomic relations. Our experiments show that the performance is promising.
Extracted statement — output: Drug ontology
Research field: Semantic Web
Title: An Integrated Approach to Drive Ontological Structure from Folksonomie
Abstract: Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.
Extracted statement — Learning method: Fuzzy clustering
Research field: Semantic Web
Title: A Deep Learning based Approach for Precise Video Tagging
Abstract: With the increase in smart devices and abundance of video contents, efficient techniques for the indexing, analysis and retrieval of videos are becoming more and more desirable. Improved indexing and automated analysis of millions of videos could be accomplished by getting videos tagged automatically. A lot of existing methods fail to precisely tag videos because of their lack of ability to capture the video context. The context in a video represents the interactions of objects in a scene and their overall meaning. In this work, we propose a novel approach that integrates the video scene ontology with CNN (Convolutional Neural Network) for improved video tagging. Our method captures the content of a video by extracting the information from individual key frames. The key frames are then fed to a CNN based deep learning model to train its parameters. The trained parameters are used to generate the most frequent tags. Highly frequent tags are used to summarize the input video. The proposed technique is benchmarked on the most widely used dataset of video activities, namely, UCF-101. Our method managed to achieve an overall accuracy of 99.8% with an F1- score of 96.2%.
Extracted statement — Learning purpose: Improved video tagging