Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Animal Sciences
Title: Fulminant hepatic failure associated with oral administration of diazepam in 11 cats
Abstract: Acute fulminant hepatic necrosis was associated with repeated oral administration of diazepam (1.25 to 2 mg, PO, q 24 or 12 h), prescribed for behavioral modification or to facilitate urination. Five of 11 cats became lethargic, atactic, and anorectic within 96 hours of initial treatment. All cats became jaundiced during the first 11 days of illness. Serum biochemical analysis revealed profoundly high alanine transaminase and aspartate transaminase activities. Results of coagulation tests in 3 cats revealed marked abnormalities. Ten cats died or were euthanatized within 15 days of initial drug administration, and only 1 cat survived. Histologic evaluation of hepatic tissue specimens from each cat revealed florid centrilobular hepatic necrosis, profound biliary ductule proliferation and hyperplasia, and suppurative intraductal inflammation. Idiosyncratic hepatotoxicosis was suspected because of the rarity of this condition. Prior sensitization to diazepam was possible in only 1 cat, and consistent risk factors that could explain susceptibility to drug toxicosis were not identified. On the basis of the presumption that diazepam was hepatotoxic in these cats, an increase in serum transaminase activity within 5 days of treatment initiation indicates a need to suspend drug administration and to provide supportive care.
Extracted statement — AED evaluated: Diazepam
Research field: Animal Sciences
Title: Pharmacokinetics and toxicity of zonisamide in cats
Abstract: With the eventual goal of making zonisamide (ZNS), a relatively new antiepileptic drug, available for the treatment of epilepsy in cats, the pharmacokinetics after a single oral administration at 10 mg/kg and the toxicity after 9-week daily administration of 20 mg/kg/day of ZNS were studied in healthy cats. Pharmacokinetic parameters obtained with a single administration of ZNS at 10 mg/day were as follows: C max =13.1 μg/ml; T max =4.0 h; T 1/2 =33.0 h; areas under the curves (AUCs)=720.3 μg/mlh (values represent the medians). The study with daily administrations revealed that the toxicity of ZNS was comparatively low in cats, suggesting that it may be an available drug for cats. However, half of the cats that were administered 20 mg/kg/day daily showed adverse reactions such as anorexia, diarrhoea, vomiting, somnolence and locomotor ataxia.
Extracted statement — AED evaluated: Zonisamide
Research field: Artificial Intelligence
Title: Trainable, scalable summarization using robust NLP and machine learning
Abstract: We describe a trainable and scalable summarization system which utilizes features derived from information retrieval, information extraction, and NLP techniques and on-line resources. The system combines these features using a trainable feature combiner learned from summary examples through a machine learning algorithm. We demonstrate system scalability by reporting results on the best combination of summarization features for different document sources. We also present preliminary results from a task-based evaluation on summarization output usability.
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: Discourse Trees Are Good Indicators of Importance in Text
Abstract: Researchers in computational linguistics have long speculated that the nuclei of the rhetorical structure tree of a text form an adequate \summary" of the text for which that tree was built. However, to my knowledge, there has been no experiment to connrm how valid this speculation really is. In this paper, I describe a psycholinguistic experiment that shows that the concepts of discourse structure and nuclearity can be used eeectively in text summarization. More precisely, I show that there is a strong correlation between the nuclei of the discourse structure of a text and what readers perceive to be the most important units in that text. In addition, I propose and evaluate the quality of an automatic, discourse-based summa-rization system that implements the methods that were validated by the psycholinguistic experiment. The evaluation indicates that although the system does not match yet the results that would be obtained if discourse trees had been built manually, it still signiicantly outperforms both a baseline algorithm and Microsoft's OOce97 summarizer. 1 Motivation Traditionally, previous approaches to automatic text summarization have assumed that the salient parts of a text can be determined by applying one or more of the following assumptions: important sentences in a text contain words that are used frequently (Luhn 1958; Edmundson 1968); important sentences contain words that are used in the title and section headings (Edmundson 1968); important sentences are located at the beginning or end of paragraphs (Baxendale 1958); important sentences are located at positions in a text that are genre dependent, and these positions can be determined automatically, through training important sentences use bonus words such as \greatest" and \signiicant" or indicator phrases such as \the main aim of this paper" and \the purpose of this article", while unimportant sentences use stigma words such as \hardly" and \im-possible" important sentences and concepts are the highest connected entities in elaborate semantic struc-important and unimportant sentences are derivable from a discourse representation of the text (Sparck Jones 1993b; Ono, Sumita, & Miike 1994). In determining the words that occur most frequently in a text or the sentences that use words that occur in the headings of sections, computers are accurate tools. Therefore, in testing the validity of using these indicators for determining the most important units in a text, it is adequate to compare the direct output of a summarization program that implements the assump-tion(s) under scrutiny with a human-made …
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: Automated multi-document summarization in NeATS
Abstract: This paper describes the multi-document text summarization system NeATS. Using a simple algorithm, NeATS was among the top two performers of the DUC-01 evaluation.
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: ERSS 2005: Coreference-Based Summarization Reloaded
Abstract: We present ERSS 2005, our entry to this year’s DUC competition. With only slight modifications from last year’s version to accommodate the more complex context information present in DUC 2005, we achieved a similar performance to last year’s entry, ranking roughly in the upper third when examining the ROUGE-1 and Basic Element score. We also participated in the additional manual evaluation based on the new Pyramid method and performed further evaluations based on the Basic Elements method and the automatic generation of Pyramids. Interestingly, the ranking of our system differs greatly between the different measures; we attempt to analyse this effect based on correlations between the different results using the Spearman coefficient.
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: MSBGA: A Multi-Document Summarization System Based on Genetic Algorithm
Abstract: The multi-document summarizer using genetic algorithm-based sentence extraction (MSBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, MSBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. The evaluation function employs four features according to the criteria of a good summary: satisfied length, high coverage, high informativeness and low redundancy. To improve the accuracy of term frequency, MSBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: Personalized PageRank Based Multi-document Summarization
Abstract: This paper presents a novel multi-document summarization approach based on personalized pagerank (PPRSum). In this algorithm, we uniformly integrate various kinds of information in the corpus. At first, we train a salience model of sentence global features based on Naive Bayes Model. Secondly, we generate a relevance model for each corpus utilizing the query of it. Then, we compute the personalized prior probability for each sentence in the corpus utilizing the salience model and the relevance model both. With the help of personalized prior probability, a Personalized PageRank ranking process is performed depending on the relationships among all sentences in the corpus. Additionally, the redundancy penalty is imposed on each sentence. The summary is produced by choosing the sentences with both high query-focused information richness and high information novelty. Experiments on DUC2007 are performed and the ROUGE evaluation results show that PPRSum ranks between the 1st and the 2nd systems on DUC2007 main task.
Extracted statement — evaluation: Evaluation
Research field: Artificial Intelligence
Title: AdaSum: an adaptive model for summarization
Abstract: Topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. This paper proposes a novel adaptive model for summarization, AdaSum, under the assumption that the summary and the topic representation can be mutually boosted. AdaSum aims to simultaneously optimize the topic representation and extract effective summaries. This model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. Furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. We prove that the training process of AdaSum can enhance the performance measure used. Experimental results on DUC 2007 dataset show that AdaSum significantly outperforms the baseline methods for summarization (e.g. MRP, LexRank, and GSPS).
Extracted statement — implementation: AdaSum
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Method: approach
Research field: Artificial Intelligence
Title: A Blocking Scheme for Entity Resolution in the Semantic Web
Abstract: The amount and diversity of data in the Semantic Web has grown quite. RDF datasets has proportionally more problems than relational datasets due to the way data are published, usually without formal criteria. Entity Resolution is n important issue which is related to a known task of many research communities and it aims at finding all representations that refer to the same entity in different datasets. Yet, it is still an open problem. Blocking methods are used to avoid the quadratic complexity of the brute force approach by clustering entities into blocks and limiting the evaluation of entity specifications to entity pairs within blocks. In the last years only a few blocking methods were conceived to deal with RDF data and novel blocking techniques are required for dealing with noisy and heterogeneous data in the Web of Data. In this paper we present a blocking scheme, CER-Blocking, which is based on an inverted index structure and that uses different data evidences from a triple, aiming to maximize its effectiveness. To overcome the problems of data quality or even the very absence thereof, we use two blocking key definitions. This scheme is part of an ER approach which is based on a relational learning algorithm that addresses the problem by statistical approximation. It was empirically evaluated on real and synthetic datasets which are part of consolidated benchmarks found on the literature.
Extracted statement — Has method: Blocking
Research field: Artificial Intelligence
Title: Towards Exploring Literals to Enrich Data Linking in Knowledge Graphs
Abstract: Knowledge graph completion is still a challenging solution that uses techniques from distinct areas to solve many different tasks. Most recent works, which are based on embedding models, were conceived to improve an existing knowledge graph using the link prediction task. However, even considering the ability of these solutions to solve other tasks, they did not present results for data linking, for example. Furthermore, most of these works focuses only on structural information, i.e., the relations between entities. In this paper, we present an approach for data linking that enrich entity embeddings in a model with their literal information and that do not rely on external information of these entities. The key aspect of this proposal is that we use a blocking scheme to improve the effectiveness of the solution in relation to the use of literals. Thus, in addition to the literals from object elements in a triple, we use other literals from subjects and predicates. By merging entity embeddings with their literal information it is possible to extend many popular embedding models. Preliminary experiments were performed on real-world datasets and our solution showed competitive results to the performance of the task of data linking.
Extracted statement — Has method: Blocking
Research field: Artificial Intelligence
Title: Food Recognition: A New Dataset, Experiments, and Results
Abstract: We propose a new dataset for the evaluation of food recognition algorithms that can be used in dietary monitoring applications. Each image depicts a real canteen tray with dishes and foods arranged in different ways. Each tray contains multiple instances of food classes. The dataset contains 1027 canteen trays for a total of 3616 food instances belonging to 73 food classes. The food on the tray images has been manually segmented using carefully drawn polygonal boundaries. We have benchmarked the dataset by designing an automatic tray analysis pipeline that takes a tray image as input, finds the regions of interest, and predicts for each region the corresponding food class. We have experimented with three different classification strategies using also several visual descriptors. We achieve about 79% of food and tray recognition accuracy using convolutional-neural-networks-based features. The dataset, as well as the benchmark framework, are available to the research community.
Extracted statement — Acquisition: Canteen
Research field: Artificial Intelligence
Title: Bridging the Gap between Citizens and Local Administrations with Knowledge-Based Service Bundle Recommendations
Abstract: The Italian Public Administration Services (IPAS) is a registry of services provided to Italian citizens likewise the Local Government Service List (UK), or the European Service List for local authorities from other nations. Unlike existing registries, IPAS presents the novelty of modelling public services from the view point of the value they have for the consumers and the providers. A value-added-service (VAS) is linked to a life event that requires its fruition, addresses consumer categories to identify market opportunities for private providers, and is described by non-functional-properties such as price and time of fruition. Where Italian local authorities leave the citizen-users in a daedalus of references to understand whether they can/have to apply for a service, the IPAS model captures the necessary back-ground knowledge about the connection between administrative legislation and service specifications, life events, and application contexts to support the citizen-users to fulfill their needs. As a proof of concept, we developed an operational Web environment named ASSO, designed to assist the citizen-user to intuitively create bundles of mandatory-by-legislation and recommended services, to accomplish his bureaucratic fulfillments. Although ASSO is an ongoing project, domain experts gave preliminary positive feedback on the innovativeness and effectiveness of the proposed approach.
Extracted statement — has Target users: Citizens
Research field: Artificial Intelligence
Title: Personalized recommendations in e-participation: offline experiments for the 'Decide Madrid' platform
Abstract: In e-participation platforms, citizens suggest, discuss and vote online for initiatives aimed to address a wide range of issues and problems in a city, such as economic development, public safety, budges, infrastructure, housing, environment, social rights, and health care. For a particular citizen, the number of proposals and debates may be overwhelming, and recommender systems could help filtering and ranking those that are more relevant. Focusing on a particular case, the `Decide Madrid' platform, in this paper we empirically investigate which sources of user preferences and recommendation approaches could be more effective, in terms of several aspects, namely precision, coverage and diversity.
Extracted statement — has Target users: Citizens
Research field: Artificial Intelligence
Title: What's going on in my city?: recommender systems and electronic participatory budgeting
Abstract: In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities - Cambridge, Miami and New York City-, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area.
Extracted statement — has Application Scope: City
Research field: Artificial Intelligence
Title: Personalized recommendations in e-participation: offline experiments for the 'Decide Madrid' platform
Abstract: In e-participation platforms, citizens suggest, discuss and vote online for initiatives aimed to address a wide range of issues and problems in a city, such as economic development, public safety, budges, infrastructure, housing, environment, social rights, and health care. For a particular citizen, the number of proposals and debates may be overwhelming, and recommender systems could help filtering and ranking those that are more relevant. Focusing on a particular case, the `Decide Madrid' platform, in this paper we empirically investigate which sources of user preferences and recommendation approaches could be more effective, in terms of several aspects, namely precision, coverage and diversity.
Extracted statement — has Application Scope: City
Research field: Artificial Intelligence
Title: A framework for explainable deep neural models using external knowledge graphs
Abstract: Deep neural networks (DNNs) have become the gold standard for solving challenging classification problems, especially given complex sensor inputs (e.g., images and video). While DNNs are powerful, they are also brittle, and their inner workings are not fully understood by humans, leading to their use as “black-box” models. DNNs often generalize poorly when provided new data sampled from slightly shifted distributions; DNNs are easily manipulated by adversarial examples; and the decision-making process of DNNs can be difficult for humans to interpret. To address these challenges, we propose integrating DNNs with external sources of semantic knowledge. Large quantities of meaningful, formalized knowledge are available in knowledge graphs and other databases, many of which are publicly obtainable. But at present, these sources are inaccessible to deep neural methods, which can only exploit patterns in the signals they are given to classify. In this work, we conduct experiments on the ADE20K dataset, using scene classification as an example task where combining DNNs with external knowledge graphs can result in more robust and explainable models. We align the atomic concepts present in ADE20K (i.e., objects) to WordNet, a hierarchically-organized lexical database. Using this knowledge graph, we expand the concept categories which can be identified in ADE20K and relate these concepts in a hierarchical manner. The neural architecture we present performs scene classification using these concepts, illuminating a path toward DNNs which can efficiently exploit high-level knowledge in place of excessive quantities of direct sensory input. We hypothesize and experimentally validate that incorporating background knowledge via an external knowledge graph into a deep learning-based model should improve the explainability and robustness of the model.
Extracted statement — Machine Learning Task: Classification
Research field: Artificial Intelligence
Title: The more you know: Using knowledge graphs for image classification
Abstract: One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.
Extracted statement — Machine Learning Task: Classification