Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Animal Sciences
Title: Pharmacokinetics of levetiracetam after oral and intravenous administration of a single dose to clinically normal cats
Abstract: OBJECTIVE To determine whether therapeutic concentrations of levetiracetam can be achieved in cats and to establish reasonable i.v. and oral dosing intervals that would not be associated with adverse effects in cats. ANIMALS 10 healthy purpose-bred cats. PROCEDURES In a randomized crossover study, levetiracetam (20 mg/kg) was administered orally and i.v. to each cat. Blood samples were collected 0, 10, 20, and 40 minutes and 1, 1.5, 2, 3, 4, 6, 9, 12, and 24 hours after administration. Plasma levetiracetam concentrations were determined via high-performance liquid chromatography. RESULTS Mean ± SD peak concentration was 25.54 ± 7.97 μg/mL. The mean y-intercept for i.v. administration was 37.52 ± 6.79 μg/mL. Half-life (harmonic mean ± pseudo-SD) was 2.95 ± 0.95 hours and 2.86 ± 0.65 hours for oral and i.v. administration, respectively. Mean volume of distribution at steady state was 0.52 ± 0.09 L/kg, and mean clearance was 2.0 ± 0.60 mL/kg/min. Mean oral bioavailability was 102 ± 39%. Plasma drug concentrations were maintained in the therapeutic range reported for humans (5 to 45 μg/mL) for at least 9 hours after administration in 7 of 10 cats. Only mild, transient hypersalivation was evident in some cats after oral administration. CONCLUSIONS AND CLINICAL RELEVANCE Levetiracetam (20 mg/kg) administered orally or i.v. to cats every 8 hours should achieve and maintain concentrations within the therapeutic range for humans. Levetiracetam administration has favorable pharmacokinetics for clinical use, was apparently tolerated well, and may be a reasonable alternative antiepileptic drug in cats.
Extracted statement — AED evaluated: LEV
Research field: Applied Physics
Title: Spectroscopic Investigation of a Microwave-Generated Atmospheric Pressure Plasma Torch
Abstract: The investigated new microwave plasma torch is based on an axially symmetric resonator. Microwaves of a frequency of 2.45 GHz are resonantly fed into this cavity resulting in a sufficiently high electric field to ignite plasma without any additional igniters as well as to maintain stable plasma operation. Optical emission spectroscopy was carried out to characterize a humid air plasma. OH‐bands were used to determine the gas rotational temperature Trot while the electron temperature was estimated by a Boltzmann plot of oxygen lines. Maximum temperatures of Trot of about 3600 K and electron temperatures of 5800 K could be measured. The electron density ne was estimated to ne ≈ 3 · 1020m–3 by using Saha's equation. Parametric studies in dependence of the gas flow and the supplied microwave power revealed that the maximum temperatures are independent of these parameters. However, the volume of the plasma increases with increasing microwave power and with a decrease of the gas flow. Considerations using collision frequencies, energy transfer times and power coupling provide an explanation of the observed phenomena: The optimal microwave heating is reached for electron‐neutral collision frequencies νen being near to the angular frequency of the wave ω (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Extracted statement — Excitation_type: GHz
Research field: Applied Physics
Title: Modeling of microwave-induced plasma in argon at atmospheric pressure
Abstract: A two-dimensional model of microwave-induced plasma (field frequency 2.45 GHz) in argon at atmospheric pressure is presented. The model describes in a self-consistent manner the gas flow and heat transfer, the in-coupling of the microwave energy into the plasma, and the reaction kinetics relevant to high-pressure argon plasma including the contribution of molecular ion species. The model provides the gas and electron temperature distributions, the electron, ion, and excited state number densities, and the power deposited into the plasma for given gas flow rate and temperature at the inlet, and input power of the incoming TEM microwave. For flow rate and absorbed microwave power typical for analytical applications (200-400 ml/min and 20 W), the plasma is far from thermodynamic equilibrium. The gas temperature reaches values above 2000 K in the plasma region, while the electron temperature is about 1 eV. The electron density reaches a maximum value of about 4 × 10(21) m(-3). The balance of the charged particles is essentially controlled by the kinetics of the molecular ions. For temperatures above 1200 K, quasineutrality of the plasma is provided by the atomic ions, and below 1200 K the molecular ion density exceeds the atomic ion density and a contraction of the discharge is observed. Comparison with experimental data is presented which demonstrates good quantitative and qualitative agreement.
Extracted statement — Excitation_type: GHz
Research field: Applied Physics
Title: Integrated Microwave Atmospheric Plasma Source (IMAPlaS): thermal and spectroscopic properties and antimicrobial effect onB. atrophaeusspores
Abstract: The Integrated Microwave Atmospheric Plasma Source (IMAPlaS) operating with a microwave resonator at 2.45 GHz driven by a solid-state transistor oscillator generates a core plasma of high temperature (T > 1000 K), therefore producing reactive species such as NO very effectively. The effluent of the plasma source is much colder, which enables direct treatment of thermolabile materials or even living tissue. In this study the source was operated with argon, helium and nitrogen with gas flow rates between 0.3 and 1.0 slm. Depending on working gas and distance, axial gas temperatures between 30 and 250 °C were determined in front of the nozzle. Reactive species were identified by emission spectroscopy in the spectral range from vacuum ultraviolet to near infrared. The irradiance in the ultraviolet range was also measured. Using B. atrophaeus spores to test antimicrobial efficiency, we determined log10-reduction rates of up to a factor of 4.
Extracted statement — Excitation_type: GHz
Research field: Applied Physics
Title: The influence of the geometry and electrical characteristics on the formation of the atmospheric pressure plasma jet
Abstract: An extensive electrical study was performed on a coaxial geometry atmospheric pressure plasma jet source in helium, driven by 30 kHz sine voltage. Two modes of operation were observed, a highly reproducible low-power mode that features the emission of one plasma bullet per voltage period and an erratic high-power mode in which micro-discharges appear around the grounded electrode. The minimum of power transfer efficiency corresponds to the transition between the two modes. Effective capacitance was identified as a varying property influenced by the discharge and the dissipated power. The charge carried by plasma bullets was found to be a small fraction of charge produced in the source irrespective of input power and configuration of the grounded electrode. The biggest part of the produced charge stays localized in the plasma source and below the grounded electrode, in the range 1.2–3.3 nC for ground length of 3–8 mm.
Extracted statement — Excitation_type: kHz
Research field: Artificial Intelligence
Title: CASIA@ V2: a MLN-based question answering system over linked data
Abstract: We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which con- tains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items of Linked Data; 3) grouping the mapped semantic items into semantic triples; and 4) generat- ing the rightful SPARQL query. We present a jointly learning framework using Markov Logic Network(MLN) for phrase detection, phrases mapping to seman- tic items and semantic items grouping. We formulate the knowledge for resolving the ambiguities in three steps of QALD as first-order logic clauses in a MLN. We evaluate our approach on QALD-4 test dataset and achieve an F-measure score of 0.36, an average precision of 0.32 and an average recall of 0.40 over 50 questions.
Extracted statement — implementation: CASIA
Research field: Artificial Intelligence
Title: Deep knowledge-aware network for news recommendation
Abstract: Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.
Extracted statement — Machine Learning Method: CNN
Research field: Artificial Intelligence
Title: DTD2OWL: automatic transforming XML documents into OWL ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Class hierarchy extraction/learning: DTD
Research field: Artificial Intelligence
Title: DTD2OWL: automatic transforming XML documents into OWL ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Concepts extraction/learning: DTD
Research field: Artificial Intelligence
Title: DTD2OWL: automatic transforming XML documents into OWL ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Input format: DTD
Research field: Artificial Intelligence
Title: Automatic transforming XML documents into OWL Ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Input format: DTD
Research field: Artificial Intelligence
Title: DTD2OWL: automatic transforming XML documents into OWL ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — Properties hierarchy extraction/learning: DTD
Research field: Artificial Intelligence
Title: Duluth at SemEval-2021 Task 11: Applying deBERTa to contributing sentence selection and dependency parsing for entity extraction
Abstract: This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing. Our solution uses deBERTa for multi-class sentence classification to extract the contributing sentences and their type, and dependency parsing to outline each sentence and extract subject-predicate-object triples. Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.
Extracted statement — Team Name: DULUTH
Research field: Artificial Intelligence
Title: Extracting ontological knowledge from Java source code using Hidden Markov Models
Abstract: Abstract Ontologies have become a key element since many decades in information systems such as in epidemiological surveillance domain. Building domain ontologies requires the access to domain knowledge owned by domain experts or contained in knowledge sources. However, domain experts are not always available for interviews. Therefore, there is a lot of value in using ontology learning which consists in automatic or semi-automatic extraction of ontological knowledge from structured or unstructured knowledge sources such as texts, databases, etc. Many techniques have been used but they all are limited in concepts, properties and terminology extraction leaving behind axioms and rules. Source code which naturally embed domain knowledge is rarely used. In this paper, we propose an approach based on Hidden Markov Models (HMMs) for concepts, properties, axioms and rules learning from Java source code. This approach is experimented with the source code of EPICAM, an epidemiological platform developed in Java and used in Cameroon for tuberculosis surveillance. Domain experts involved in the evaluation estimated that knowledge extracted was relevant to the domain. In addition, we performed an automatic evaluation of the relevance of the terms extracted to the medical domain by aligning them with ontologies hosted on Bioportal platform through the Ontology Recommender tool. The results were interesting since the terms extracted were covered at 82.9% by many biomedical ontologies such as NCIT, SNOWMEDCT and ONTOPARON.
Extracted statement — Extraction methods: HMM
Research field: Artificial Intelligence
Title: A Statistical Approach for Automatic Text Summarization by Extraction
Abstract: Automatic Document Summarization is a highly interdisciplinary research area related with computer science as well as cognitive psychology. This Summarization is to compress an original document into a summarized version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a statistical automatic text summarization approach, Kmixture probabilistic model, to enhancing the quality of summaries. KSRS employs the K-mixture probabilistic model to establish term weights in a statistical sense, and further identifies the term relationships to derive the semantic relationship significance (SRS) of nouns. Sentences are ranked and extracted based on their semantic relationship significance values. The objective of this research is thus to propose a statistical approach to text summarization. We propose a K-mixture semantic relationship significance (KSRS) approach to enhancing the quality of document summary results. The K-mixture probabilistic model is used to determine the term weights. Term relationships are then investigated to develop the semantic relationship of nouns that manifests sentence semantics. Sentences with significant semantic relationship, nouns are extracted to form the summary accordingly.
Extracted statement — implementation: KSRS
Research field: Artificial Intelligence
Title: SemEval-2012 Task 5: Chinese Semantic Dependency Parsing
Abstract: The paper presents the SemEval-2012 Shared Task 5: Chinese Semantic Dependency Parsing. The goal of this task is to identify the dependency structure of Chinese sentences from the semantic view. We firstly introduce the motivation of providing Chinese semantic dependency parsing task, and then describe the task in detail including data preparation, data format, task evaluation, and so on. Over ten thousand sentences were labeled for participants to train and evaluate their systems. At last, we briefly describe the submitted systems and analyze these results.
Extracted statement — Evaluation metrics: LAS
Research field: Artificial Intelligence
Title: The University of Michigan at DUC 2004
Abstract: We present the results of Michigan’s participation in DUC 2004. Our system, MEAD, ranked as one of the top systems in four of the five tasks. We introduce our new feature, LexPageRank, a new measure of sentence centrality inspired by the prestige concept in social networks. LexPageRank gave promising results in multi-document summarization. Our approach for Task 5, biographical summarization, was simplistic, yet succesful. We used regular expression matching to boost up the scores of the sentences that are likely to contain biographical information patterns.
Extracted statement — implementation: MEAD