Moussab/ORKG-training-evaluation-set huggingface dataset:
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: Shape
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 — Properties learning: Size
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: Size
Research field: Semantic Web
Title: Representing the Hierarchy of Industrial Taxonomies in OWL: The gen/tax Approach
Abstract: Existing taxonomies are valuable input for creating ontologies, because they reflect some degree of community consensus and contain, readily available, a wealth of concept definitions plus a hierarchy. However, the transformation of such taxonomies into useful ontologies is not as straightforward as it appears, because simply taking the hierarchy of concepts, which was originally developed for some external purpose other than ontology engineering, as the subsumption hierarchy using rdfs:subClassOf can yield useless ontologies. In this paper, we (1) illustrate the problem by analyzing OWL and RDF-S ontologies derived from UNSPSC (a products and services taxonomy), (2) detail how the interpretation and representation of the original taxonomic relationship is an important modeling decision when deriving ontologies from existing taxonomies, (3) propose a novel “gen/tax” approach to capture the original semantics of taxonomies in OWL, based on the split of each category in the taxonomy into two concepts, a generic concept and a taxonomy concept, and (4) show the usefulness of this approach by transforming eCl@ss into a fully-fledged products and services ontology.
Extracted statement — Relationships: Subclass
Research field: Semantic Web
Title: Enabling Folksonomies for Knowledge Extraction: A Semantic Grounding Approach
Abstract: Folksonomies emerge as the result of the free tagging activity of a large number of users over a variety of resources. They can be considered as valuable sources from which it is possible to obtain emerging vocabularies that can be leveraged in knowledge extraction tasks. However, when it comes to understanding the meaning of tags in folksonomies, several problems mainly related to the appearance of synonymous and ambiguous tags arise, specifically in the context of multilinguality. The authors aim to turn folksonomies into knowledge structures where tag meanings are identified, and relations between them are asserted. For such purpose, they use DBpedia as a general knowledge base from which they leverage its multilingual capabilities.
Extracted statement — input: Tags
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 — Input format: Video
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 — Input format: Video
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 — Knowledge source: Video
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 — Knowledge source: Video
Research field: Semantic Web
Title: REUSING UML CLASS MODELS TO GENERATE OWL ONTOLOGIES - A Use Case in the Pharmacotherapeutic Domain: 
Abstract: This paper presents a method for the reuse of existing knowledge in UML software models. Our purpose is being able to adapt fragments of existing UML class diagrams in order to build domain ontologies, represented in OWL-DL, reducing the required amount of time and resources to create one from scratch. Our method is supported by a CASE tool, VisualWADE, and a developed plug-in, used for the management of ontologies and the generation of semantically tagged Web applications. In order to analyse the designed transformations between knowledge representation formalisms, UML and OWL, we have chosen a use case in the pharmacotherapeutic domain. Then, we discuss some of the most relevant aspects of the proposal and, finally, conclusions are obtained and future work briefly described.
Extracted statement — implementation: Visualwade
Research field: Semantic Web
Title: Automatic Product Ontology Extraction from Textual Reviews
Abstract: Ontologies have proven beneficial in different settings that make use of textual reviews. However, manually constructing ontologies is a laborious and time-consuming process in need of automation. We propose a novel methodology for automatically extracting ontologies, in the form of meronomies, from product reviews, using a very limited amount of hand-annotated training data. We show that the ontologies generated by our method outperform hand-crafted ontologies (WordNet) and ontologies extracted by existing methods (Text2Onto and COMET) in several, diverse settings. Specifically, our generated ontologies outperform the others when evaluated by human annotators as well as on an existing Q&A dataset from Amazon. Moreover, our method is better able to generalise, in capturing knowledge about unseen products. Finally, we consider a real-world setting, showing that our method is better able to determine recommended products based on their reviews, in alternative to using Amazon’s standard score aggregations.
Extracted statement — Input format: Text
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 — Input format: Text
Research field: Semantic Web
Title: Automatic Product Ontology Extraction from Textual Reviews
Abstract: Ontologies have proven beneficial in different settings that make use of textual reviews. However, manually constructing ontologies is a laborious and time-consuming process in need of automation. We propose a novel methodology for automatically extracting ontologies, in the form of meronomies, from product reviews, using a very limited amount of hand-annotated training data. We show that the ontologies generated by our method outperform hand-crafted ontologies (WordNet) and ontologies extracted by existing methods (Text2Onto and COMET) in several, diverse settings. Specifically, our generated ontologies outperform the others when evaluated by human annotators as well as on an existing Q&A dataset from Amazon. Moreover, our method is better able to generalise, in capturing knowledge about unseen products. Finally, we consider a real-world setting, showing that our method is better able to determine recommended products based on their reviews, in alternative to using Amazon’s standard score aggregations.
Extracted statement — Knowledge source: Text
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 — Knowledge source: Text
Research field: Semantic Web
Title: The Ontology Extraction & Maintenance Framework Text-To-Onto
Abstract: Ontologies play an increasingly important role in Knowledge Management. One of the main problems associated with ontologies is that they need to be constructed and maintained. Manual construction of larger ontologies is usually not feasible within companies because of the effort and costs required. Therefore, a semi-automatic approach to ontology construction and maintenance is what everybody is wishing for. The paper presents a framework for semi-automatically learning ontologies from domainspecific texts by applying machine learning techniques. The TEXT-TO-ONTO framework integrates manual engineering facilities to follow a balanced cooperative modelling paradigm.
Extracted statement — Knowledge source: Text
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 — Output format: Text
Research field: Semiconductor and Optical Materials
Title: Thin-Film Solar Cells with 19% Efficiency by Thermal Evaporation of CdSe and CdTe
Abstract: CdTe-based solar cells exhibiting 19% power conversion efficiency were produced using widely available thermal evaporation deposition of the absorber layers on SnO2-coated glass with or without a t...
Extracted statement — keywords: Deposition
Research field: Semiconductor and Optical Materials
Title: Dismantling the “Red Wall” of Colloidal Perovskites: Highly Luminescent Formamidinium and Formamidinium–Cesium Lead Iodide Nanocrystals
Abstract: Colloidal nanocrystals (NCs) of APbX3-type lead halide perovskites [A = Cs+, CH3NH3+ (methylammonium or MA+) or CH(NH2)2+ (formamidinium or FA+); X = Cl–, Br–, I–] have recently emerged as highly versatile photonic sources for applications ranging from simple photoluminescence down-conversion (e.g., for display backlighting) to light-emitting diodes. From the perspective of spectral coverage, a formidable challenge facing the use of these materials is how to obtain stable emissions in the red and infrared spectral regions covered by the iodide-based compositions. So far, red-emissive CsPbI3 NCs have been shown to suffer from a delayed phase transformation into a nonluminescent, wide-band-gap 1D polymorph, and MAPbI3 exhibits very limited chemical durability. In this work, we report a facile colloidal synthesis method for obtaining FAPbI3 and FA-doped CsPbI3 NCs that are uniform in size (10–15 nm) and nearly cubic in shape and exhibit drastically higher robustness than their MA- or Cs-only cousins with similar sizes and morphologies. Detailed structural analysis indicated that the FAPbI3 NCs had a cubic crystal structure, while the FA0.1Cs0.9PbI3 NCs had a 3D orthorhombic structure that was isostructural to the structure of CsPbBr3 NCs. Bright photoluminescence (PL) with high quantum yield (QY > 70%) spanning red (690 nm, FA0.1Cs0.9PbI3 NCs) and near-infrared (near-IR, ca. 780 nm, FAPbI3 NCs) regions was sustained for several months or more in both the colloidal state and in films. The peak PL wavelengths can be fine-tuned by using postsynthetic cation- and anion-exchange reactions. Amplified spontaneous emissions with low thresholds of 28 and 7.5 μJ cm–2 were obtained from the films deposited from FA0.1Cs0.9PbI3 and FAPbI3 NCs, respectively. Furthermore, light-emitting diodes with a high external quantum efficiency of 2.3% were obtained by using FAPbI3 NCs.
Extracted statement — Material: films
Research field: Semiconductor and Optical Materials
Title: Thin-Film Solar Cells with 19% Efficiency by Thermal Evaporation of CdSe and CdTe
Abstract: CdTe-based solar cells exhibiting 19% power conversion efficiency were produced using widely available thermal evaporation deposition of the absorber layers on SnO2-coated glass with or without a t...
Extracted statement — keywords: Layers