Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Semantic Web
Title: Automatic Subject Indexing with Knowledge Graphs
Abstract: Automatic subject indexing has been a longstanding goal of digital curators to facilitate effective retrieval access to large collections of both online and offline information resources. Controlled vocabularies are often used for this purpose, as they standardise annotation practices and help users to navigate online resources through following interlinked topical concepts. However, to this date, the assignment of suitable text annotations from a controlled vocabulary is still largely done manually, or at most (semi-)automatically, even though effective machine learning tools are already in place. This is because existing procedures require a sufficient amount of training data and they have to be adapted to each vocabulary, language and application domain anew. In this paper, we argue that there is a third solution to subject indexing which harnesses cross-domain knowledge graphs. Our KINDEX approach fuses distributed knowledge graph information from different sources. Experimental evaluation shows that the approach achieves good accuracy scores by exploiting correspondence links of publicly available knowledge graphs.
Extracted statement — Method: KINDEX approach
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 — Data types: Knowledge Graph
Research field: Semantic Web
Title: Image domain ontology fusion approach using multi-level inference mechanism
Abstract: One of the main challenges in content-based or semantic image retrieval is still to bridge the gap between low-level features and semantic information. In this paper, An approach is presented using integrated multi-level image features in ontology fusion construction by a fusion framework, which based on the latent semantic analysis. The proposed method promotes images ontology fusion efficiently and broadens the application fields of image ontology retrieval system. The relevant experiment shows that this method ameliorates the problem, such as too many redundant data and relations, in the traditional ontology system construction, as well as improves the performance of semantic images retrieval.
Extracted statement — Learning method: Latent semantic analysis
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: Lebanese criminal system
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 — Learning method: Linguistic patterns
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: mapping rules
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: Medical glossaries
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 — Learning purpose: Ontology construction
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 — Learning purpose: Ontology construction
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 — Learning purpose: Ontology enrichment
Research field: Semantic Web
Title: Image domain ontology fusion approach using multi-level inference mechanism
Abstract: One of the main challenges in content-based or semantic image retrieval is still to bridge the gap between low-level features and semantic information. In this paper, An approach is presented using integrated multi-level image features in ontology fusion construction by a fusion framework, which based on the latent semantic analysis. The proposed method promotes images ontology fusion efficiently and broadens the application fields of image ontology retrieval system. The relevant experiment shows that this method ameliorates the problem, such as too many redundant data and relations, in the traditional ontology system construction, as well as improves the performance of semantic images retrieval.
Extracted statement — Learning method: Ontology fusion
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 — Learning method: Regular expression
Research field: Semantic Web
Title: Explainable cyber-physical energy systems based on knowledge graph
Abstract: Explainability can help cyber-physical systems alleviating risk in automating decisions that are affecting our life. Building an explainable cyber-physical system requires deriving explanations from system events and causality between the system elements. Cyber-physical energy systems such as smart grids involve cyber and physical aspects of energy systems and other elements, namely social and economic. Moreover, a smart-grid scale can range from a small village to a large region across countries. Therefore, integrating these varieties of data and knowledge is a fundamental challenge to build an explainable cyber-physical energy system. This paper aims to use knowledge graph based framework to solve this challenge. The framework consists of an ontology to model and link data from various sources and graph-based algorithm to derive explanations from the events. A simulated demand response scenario covering the above aspects further demonstrates the applicability of this framework.
Extracted statement — Application Domain: Smart Grids
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 — Relationship learning: Taxonomic relations
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 — Learning method: transformation rules
Research field: Semantic Web
Title: Ontology Learning from Thesauri: An Experience in the Urban Domain
Abstract: Ontology learning is the term used to encompass methods and techniques employed for the (semi-)automatic processing of knowledge resources that facilitate the acquisition of knowledge during ontology construction. This chapter focuses on ontology learning techniques using thesauri as input sources. Thesauri are one of the most promising sources for the creation of domain ontologies thanks to the richness of term definitions, the existence of a priori relationships between terms, and the consensus provided by their extensive use in the library context. Apart from reviewing the state of the art, this chapter shows how ontology learning techniques can be applied in the urban domain for the development of domain ontologies.
Extracted statement — Application Domain: Urban domain
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: Web resources
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: [A = Cs+, CH3NH3
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: + (methylammonium or MA+) or