Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Information Science
Title: A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities
Abstract: In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.
Extracted statement — has research problem: Recommender Systems
Research field: Information Science
Title: Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem
Abstract: E-learning recommender systems are gaining significance nowadays due to its ability to enhance the learning experience by providing tailor-made services based on learner preferences. A Personalized Learning Environment (PLE) that automatically adapts to learner characteristics such as learning styles and knowledge level can recommend appropriate learning resources that would favor the learning process and improve learning outcomes. The pure cold-start problem is a relevant issue in PLEs, which arises due to the lack of prior information about the new learner in the PLE to create appropriate recommendations. This article introduces a semantic framework based on ontology to address the pure cold-start problem in content recommenders. The ontology encapsulates the domain knowledge about the learners as well as Learning Objects (LOs). The semantic model that we built has been experimented with different combinations of the key learner parameters such as learning style, knowledge level, and background knowledge. The proposed framework utilizes these parameters to build natural learner groups from the learner ontology using SPARQL queries. The ontology holds 480 learners’ data, 468 annotated learning objects with 5,600 learner ratings. A multivariate k-means clustering algorithm, an unsupervised machine learning technique for grouping similar data, is used to evaluate the learner similarity computation accuracy. The learner satisfaction achieved with the proposed model is measured based on the ratings given by the 40 participants of the experiments. From the evaluation perspective, it is evident that 79% of the learners are satisfied with the recommendations generated by the proposed model in pure cold-start condition.
Extracted statement — has research problem: Recommender Systems
Research field: Information Science
Title: Enriching pre-trained language model with entity information for relation classification
Abstract: Relation classification is an important NLP task to extract relations between entities. The state-of-the-art methods for relation classification are primarily based on Convolutional or Recurrent Neural Networks. Recently, the pre-trained BERT model achieves very successful results in many NLP classification / sequence labeling tasks. Relation classification differs from those tasks in that it relies on information of both the sentence and the two target entities. In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task. We locate the target entities and transfer the information through the pre-trained architecture and incorporate the corresponding encoding of the two entities. We achieve significant improvement over the state-of-the-art method on the SemEval-2010 task 8 relational dataset.
Extracted statement — has research problem: Relation classification
Research field: Information Science
Title: Improving relation extraction by pre-trained language representations
Abstract: Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction, extending the OpenAI Generative Pre-trained Transformer [Radford et al., 2018]. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.
Extracted statement — has research problem: Relation extraction
Research field: Information Science
Title: Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Abstract: Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.
Extracted statement — has research problem: Relation extraction
Research field: Information Science
Title: Sentence similarity learning by lexical decomposition and composition
Abstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.
Extracted statement — has research problem: sentence similarity
Research field: Information Science
Title: Learning context-sensitive convolutional filters for text processing
Abstract: Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-sensitive filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.
Extracted statement — has research problem: Text Processing
Research field: Information Science
Title: A parallel-hierarchical model for machine comprehension on sparse data
Abstract: Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging {\it MCTest} benchmark. Partly because of its limited size, prior work on {\it MCTest} has focused mainly on engineering better features. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set. Perspectives range from the word level to sentence fragments to sequences of sentences; the networks operate only on word-embedding representations of text. When trained with a methodology designed to help cope with limited training data, our Parallel-Hierarchical model sets a new state of the art for {\it MCTest}, outperforming previous feature-engineered approaches slightly and previous neural approaches by a significant margin (over 15\% absolute).
Extracted statement — has research problem: Understanding unstructured text
Research field: Information Systems, Process and Knowledge Management
Title: Open data hackathons: an innovative strategy to enhance entrepreneurial intention
Abstract: <jats:sec>
<jats:title content-type="abstract-subheading">Purpose</jats:title>
<jats:p>In terms of entrepreneurship, open data benefits include economic growth, innovation, empowerment and new or improved products and services. Hackathons encourage the development of new applications using open data and the creation of startups based on these applications. Researchers focus on factors that affect nascent entrepreneurs’ decision to create a startup but researches in the field of open data hackathons have not been fully investigated yet. This paper aims to suggest a model that incorporates factors that affect the decision of establishing a startup by developers who have participated in open data hackathons.</jats:p>
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<jats:sec>
<jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title>
<jats:p>In total, 70 papers were examined and analyzed using a three-phased literature review methodology, which was suggested by Webster and Watson (2002). These surveys investigated several factors that affect a nascent entrepreneur to create a startup.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Findings</jats:title>
<jats:p>Eventually, by identifying the motivations for developers to participate in a hackathon, and understanding the benefits of the use of open data, researchers will be able to elaborate the proposed model and evaluate if the contest has contributed to the decision of establish a startup and what factors affect the decision to establish a startup apply to open data developers, and if the participants of the contest agree with these factors.</jats:p>
</jats:sec>
<jats:sec>
<jats:title content-type="abstract-subheading">Originality/value</jats:title>
<jats:p>The paper expands the scope of open data research on entrepreneurship field, stating the need for more research to be conducted regarding the open data in entrepreneurship through hackathons.</jats:p>
</jats:sec>
Extracted statement — has research problem: aims to suggest a model that incorporates factors that affect the decision of establishing a startup by developers who have participated in open data hackathons
Research field: Information Systems, Process and Knowledge Management
Title: The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments
Abstract: In the past years, many methodologies and tools have been developed to assess the FAIRness of research data. These different methodologies and tools have been based on various interpretations of the FAIR principles, which makes comparison of the results of the assessments difficult. The work in the RDA FAIR Data Maturity Model Working Group reported here has delivered a set of indicators with priorities and guidelines that provide a ‘lingua franca’ that can be used to make the results of the assessment using those methodologies and tools comparable. The model can act as a tool that can be used by various stakeholders, including researchers, data stewards, policy makers and funding agencies, to gain insight into the current FAIRness of data as well as into the aspects that can be improved to increase the potential for reuse of research data. Through increased efficiency and effectiveness, it helps research activities to solve societal challenges and to support evidence-based decisions. The Maturity Model is publicly available and the Working Group is encouraging application of the model in practice. Experience with the model will be taken into account in the further development of the model.
Extracted statement — has research problem: assess the FAIRness of research data
Research field: Information Systems, Process and Knowledge Management
Title: DIGITAL MANUFACTURING: REQUIREMENTS AND CHALLENGES FOR IMPLEMENTING DIGITAL SURROGATES
Abstract: A key challenge for manufacturers today is efficiently producing and delivering products on time. Issues include demand for customized products, changes in orders, and equipment status change, complicating the decision-making process. A real-time digital representation of the manufacturing operation would help address these challenges. Recent technology advancements of smart sensors, IoT, and cloud computing make it possible to realize a "digital twin" of a manufacturing system or process. Digital twins or surrogates are data-driven virtual representations that replicate, connect, and synchronize the operation of a manufacturing system or process. They utilize dynamically collected data to track system behaviors, analyze performance, and help make decisions without interrupting production. In this paper, we define digital surrogate, explore their relationships to simulation, digital thread, artificial intelligence, and IoT. We identify the technology and standard requirements and challenges for implementing digital surrogates. A production planning case is used to exemplify the digital surrogate concept.
Extracted statement — has research problem: digital twin
Research field: Learner-Interface Interaction
Title: Getting the Mix Right Again: An updated and theoretical rationale for interaction
Abstract: No topic raises more contentious debate among educators than the role of interaction as a crucial component of the education process. This debate is fueled by surface problems of definition and vested interests of professional educators, but is more deeply marked by epistemological assumptions relative to the role of humans and human interaction in education and learning. The seminal article by Daniel and Marquis (1979) challenged distance educators to get the mixture right between independent study and interactive learning strategies and activities. They quite rightly pointed out that these two primary forms of education have differing economic, pedagogical, and social characteristics, and that we are unlikely to find a “perfect” mix that meets all learner and institutional needs across all curricula and content. Nonetheless, hard decisions have to be made. Even more than in 1979, the development of newer, cost effective technologies and the nearly ubiquitous (in developed countries) Net-based telecommunications system is transforming, at least, the cost and access implications of getting the mix right. Further, developments in social cognitive based learning theories are providing increased evidence of the importance of collaborative activity as a component of all forms of education – including those delivered at a distance. Finally, the context in which distance education is developed and delivered is changing in response to the capacity of the semantic Web (Berners-Lee, 1999) to support interaction, not only amongst humans, but also between and among autonomous agents and human beings. Thus, the landscape and challenges of “getting the mix right” have not lessened in the past 25 years, and, in fact, have become even more complicated. This paper attempts to provide a theoretical rationale and guide for instructional designers and teachers interested in developing distance education systems that are both effective and efficient in meeting diverse student learning needs.
Extracted statement — has research problem: The role of interaction as a crucial component of the education process
Research field: Machine Learning
Title: Active Learning Yields Better Training Data for Scientific Named Entity Recognition
Abstract: Despite significant progress in natural language processing, machine learning models require substantial expertannotated training data to perform well in tasks such as named entity recognition (NER) and entity relations extraction. Furthermore, NER is often more complicated when working with scientific text. For example, in polymer science, chemical structure may be encoded using nonstandard naming conventions, the same concept can be expressed using many different terms (synonymy), and authors may refer to polymers with ad-hoc labels. These challenges, which are not unique to polymer science, make it difficult to generate training data, as specialized skills are needed to label text correctly. We have previously designed polyNER, a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based classifier. PolyNER facilitates a labeling process that is otherwise tedious and expensive. Here, we use active learning to efficiently obtain more annotations from experts and improve performance. Our approach requires just five hours of expert time to achieve discrimination capacity comparable to that of a state-of-the-art chemical NER toolkit.
Extracted statement — has research problem: Active Learning
Research field: Machine Learning
Title: LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
Abstract: Knowledge graph embedding models have gained significant attention in AI research. The aim of knowledge graph embedding is to embed the graphs into a vector space in which the structure of the graph is preserved. Recent works have shown that the inclusion of background knowledge, such as logical rules, can improve the performance of embeddings in downstream machine learning tasks. However, so far, most existing models do not allow the inclusion of rules. We address the challenge of including rules and present a new neural based embedding model (LogicENN). We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph. To the best of our knowledge, this has not been proved so far for the neural based family of embedding models. Moreover, we derive formulae for the inclusion of various rules, including (anti-)symmetric, inverse, irreflexive and transitive, implication, composition, equivalence, and negation. Our formulation allows avoiding grounding for implication and equivalence relations. Our experiments show that LogicENN outperforms the existing models in link prediction.
Extracted statement — has research problem: Knowledge Graph Embedding
Research field: Machine Learning
Title: OWL2Vec*: Embedding of OWL Ontologies
Abstract: Abstract Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named , which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, often significantly outperforms the state-of-the-art methods in our experiments.
Extracted statement — has research problem: Ontology Embedding
Research field: Machine Learning
Title: Accurate unlexicalized parsing
Abstract: We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F1) is better than that of early lexicalized PCFG models, and surprisingly close to the current state-of-the-art. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize.
Extracted statement — has research problem: Parsing
Research field: Machine Learning
Title: NLTK: The Natural Language Toolkit
Abstract: NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.
Extracted statement — has research problem: ready-to-use computational linguistics courseware