Reviewerly/exHarmony huggingface dataset:
Question Answering over Knowledge Graphs with Query Path Generation

Knowledge graphs have been applied in question answering. Many researchers have proposed methods based on query graph generation, but there are some defects such as high cost of query graph generation and large search scope of knowledge graphs. Especially for the complex questions, which refer to those with multi-hop relations and constraints, there are problems such as incomplete search and inaccurate selection of answers. In order to solve the problems mentioned above, this paper proposes a staged query path generation method. The approach firstly takes the predicate sequence of the question in knowledge graphs as the breakthrough and constructs the core path. Then, the constraints are obtained by analyzing the question. And on this basis, the core path is extended to generate the query path. Finally, the final answer to the question is determined through the query path. Experimental results show that the Hit score of proposed approach is higher than that of many competitive state-of-the-art baselines.
Relational Triple Extraction with Relation-Attentive Contextual Semantic Representations

As a fundamental task in natural language processing, relation extraction from unstructured text is usually processed with named entity recognition together. These models intend to extract entities and predict relations simultaneously. However, they typically focus on entity pairs representations or relation representations, which ignores the contextual semantic. To tackle these problems, we introduce a three-stage relational triple extraction model with relation-attentive contextual semantic, termed as RARE. As a significant feature, contextual semantic are employed in both subtasks. In relation prediction, we utilize sentence embedding processed by mlp-attention to capture important information in a sentence. In the subject and object extraction, relation-attention mechanism is applied to calculate the relation-aware contextual representations. Extensive experiments on NYT* and WebNLG* show that our model can achieve better performance.
Knowledge Graph Population with Out-of-KG Entities

Existing knowledge graphs are incomplete. A lot of unstructured documents are hiding valuable information. But extracting and structuring that information is expensive. To help, the knowledge graphs can be populated (semi-) automatically. But knowledge graph population methods often assumes existing entities, yet, this is not the reality. To solve this, missing entities need to be detected and distinguished. To support an incoming stream of documents the out-of-KG entities are incrementally modelled. The first goal of the thesis is hence to create a novel entity linking method able to detect, distinguish and incrementally model out-of-KG entities. While the identification and modelling of potential out-of-KG entities are a step in the right direction, they still need to be included in the knowledge graph. To simplify the process, another goal is to generate short descriptions of newly identified entities. To accomplish that, a method building upon the representation of out-of-KG entities will be created which combines the properties of both graph-to-text and abstractive summarization methods. For training and evaluation, two silver-standard datasets, as well as one gold-standard dataset, will be created.
Event Detection Based on Multilingual Information Enhanced Syntactic Dependency GCN

Event detection is a hot and difficult problem in information extraction. It is widely used in automatic news extraction, financial event analysis and other fields. However, most of the existing event detection methods only focus on a single language, ignoring the event information provided by other languages, and can not solve the problem of polysemy in a single language, which makes it difficult to improve the performance of event detection methods. To solve these problems, this paper proposes a new Event Detection based on Multilingual Information Enhanced Syntactic Dependency GCN. Specifically, the model translates the original language and aligns words, takes multilingual data as input, and constructs syntactic dependency diagrams for initial language sentences. Then, a graph neural network is constructed based on the syntactic dependency graph, and combined with the attention mechanism, the nodes of the syntactic dependency graph are enhanced by the translated language. Finally, the classifier finds the trigger and judges the event type. The model effectively improves the recognition efficiency of polysemous words by using multilingual information, and makes full use of sentence structure information by using syntactic dependency graph. Experiments on ace2005 benchmark data set show that the model can detect events effectively and is obviously superior to the existing event detection methods.
Query and Neighbor-Aware Reasoning Based Multi-hop Question Answering over Knowledge Graph

Multi-hop Question Answering over Knowledge Graph (multi-hop KGQA) is a challenging task since it requires reasoning with multiple triplets over knowledge graph to find the correct answer entities. Benefiting from expeditious development of attention mechanism and graph neural network, recent works in multi-hop KGQA based on information retrieval have made great progress. However, most existing works focus on encoding questions and knowledge graph in isolation so that the reasoning module lacks interaction of encoding information. Additionally, they only consider matching relation embeddings with the encoded queries at each hop, hence the complex questions, such as those containing one-to-many relation, are hard to answer. In this paper, we propose a query and neighbor-aware reasoning based multi-hop KGQA model to solve the above problems by introducing the CoAttention module and the neighbor-aware reasoning module. Experiments show that our model can not only keep competitive performance on MetaQA datasets but also improve performance than the state-of-the-art baselines on the wide-used benchmarks WebQSP and CWQ.
Causal Domain Adaptation for Information Extraction from Complex Conversations

Complex conversations can be seen everywhere on the web from email lists to discussion forms. Being able to more effectively extract entities and their relations from these conversations would be an important contribution to conversational content analysis. Despite the success of current information extraction systems, their use in complex conversations is challenging due to, among other reasons, the existence of long-tail entities that are unrepresented in standard training corpora (e.g. news). Moreover, in general the distribution of entities in the target domain is frequently different from that of the training domain, which requires the algorithms to be able to perform domain adaptation. In this research, we will focus on identifying domain shifts that might impact information extractions systems and we aim to propose a causal framework for domain adaptation in information extraction.
MAST-NER: A Low-Resource Named Entity Recognition Method Based on Trigger Pool

Named entity recognition (NER) is a basic knowledge extraction task. At present, many domains face a lack of labeled data, but current models for low-resource NER does not utilize the features of domain text. In this paper, we propose the MAST-NER model to improve the NER performance on domain-specific text. This model introduces multiple type pools based on entity triggers, and enhances sequence tagging through a multi-head attention mechanism, where the query matrix is jointly constructed by each type of triggers. MAST-NER can take full advantages of entity triggers on domain text with similar sentence patterns, and enable each type of entity recognition to be enhanced. The experimental results show that the model in this paper can achieve higher cost-effectiveness, especially for domain datasets (up to 3.33%). For general domain datasets, this model also has a certain performance improvement.
Relation Canonicalization in Open Knowledge Graphs: A Quantitative Analysis

Open Information Extraction (OpenIE) allows the detection of meaningful triples of (noun phrase, relation phrase, noun phrase) in unstructured texts in an unsupervised manner. This makes OpenIE highly adaptable for any domain and suitable for creation of an open knowledge graph (KG). The OpenIE methods, however, often result in generation of redundant and ambiguous information. Canonicalization is therefore needed to reduce redundancy and improve the quality of the resultant KG. In this work, we create a dataset for a systematic evaluation of relation canonicalization and present a quantitative analysis of existing state-of-the-art methods which has been previously missing.
Vietnamese Named Entity Recognition on Medical Topic

Recent advances in language representation using neural networks combined with transfer learning have brought great success in natural language processing tasks, such as named entity recognition (NER), question answering, sentiment analysis, etc. It has been shown that leveraging of pre-trained language models improves overall performance on many tasks and is very beneficial when little data is tagged. For the task of recognizing named entities in the medical field, most of available datasets are English-specific. Therefore, it is of interest to create datasets related to health topics for languages other than English. In this paper, we present the first manually annotated dataset for Vietnamese for named entity recognition (NER) task with newly defined entity types that can be used in future outbreaks. Our dataset also contains the largest number of entities compared to the existing Vietnamese NER dataset. We experimentally conducted our dataset tests on language models for Vietnamese and make comparisons including PhoBERT and our own language model CMCBERT.
Study on Chinese Named Entity Recognition Based on Dynamic Fusion and Adversarial Training

This paper aims at the Chinese Named Entity Recognition task, uses NEZHA Chinese pre-trained language model as the word embedding layer, then adopts the BiLSTM network architecture to encode it, and finally connects the CRF layer to optimize the output sequence. In order to enhance the fusion of semantic features of the NEZHA model in the upper, middle, and lower layers, an attention mechanism has been adopted to integrate the NEZHA coding layers. At first, weight was given to each representation generated by its 12 Transformer coding layers. Secondly, the weight value was dynamically adjusted through supervised training, and then the generated layer representation was weighted average to get the final word embedded representation. Finally, some noise was introduced to the input data, which is used for adversarial training to improve the generalization and robustness of the model. The results show that the F1 Score of the proposed model on Chinese Clinical Named Entity Recognition Dataset and people’s daily corpus are respectively 98.52% and 96.84%, which are 2.36% and 4.21% respectively higher than the benchmark model Bert BiLSTM CRF.
An Approach to Evaluate the Reliability of the Face Recognition Process Using Adversarial Samples Generated by Deep Neural Networks

Many studies have shown that Deep Neural Networks (DNNs) can be fooled by deceptive inputs, known as adversarial samples. Adversarial samples are produced by putting a small amount of perturbations into input images. These samples might mislead the object detection/recognition phases, thus it leads to the decreasing in the overall reliability of the object recognition process. Therefore, understanding the characteristic of adversarial samples is a very important step to propose the efficient attacking algorithms. In the paper, we present a new approach to particularly attack the face recognition process based on Fast Gradient Sign Attack (FGSA) and Jacobian-based Saliency Map Attack (JSMA) algorithms for the white-box attacks. By this way, we can evaluate the reliability of the face recognition process which are using MobileFaceNets and Caffe models.
How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks?

This paper deals with historical document image segmentation with focus on chronicles available in the Porta fontium portal. We build on our previously published database that has precise pixel-level annotations in PAGE format but also utilise other datasets for transfer learning in order to improve the results. We discuss a series of experiments that evaluate possibilities how to train a neural model for image, text and background segmentation. The outcome, in a form of segmentation method with relatively low computational costs and great results, is integrated into the Porta fontium portal to improve its possibilities of searching and publication of the documents.
A Combinatorial Approach to Weighted Model Counting in the Two-Variable Fragment with Cardinality Constraints

Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain. First-Order Logic theories that admit polynomial-time WFOMC w.r.t domain cardinality are called domain liftable. In this paper, we reconstruct the closed-form formula for polynomial-time First Order Model Counting (FOMC) in the universally quantified fragment of FO $$^2$$ , earlier proposed by Beame et al.. We then expand this closed-form to incorporate cardinality constraints and existential quantifiers. Our approach requires a constant time (w.r.t the previous linear time result) for handling equality and allows us to handle cardinality constraints in a completely combinatorial fashion. Finally, we show that the obtained closed-form motivates a natural definition of a family of weight functions strictly larger than symmetric weight functions.
An Advanced IoU Loss Function for Accurate Bounding Box Regression

Bounding box regression (BBR) is one of the important steps for object detection. To improve accuracy of recognition ability between true object and prediction object, many researches have developed loss functions for BBR. In existing researches, some main drawbacks can be shown: (i) both IOU-based loss functions and $$l{_n}-norm$$ are inefficient enough to detect the object; (ii) the loss functions ignore the imbalance issues in BBR when the large number of anchor boxes have overlaps with the target boxes; (iii) the loss functions own redundant parameters which lead to extend training process. To address these problems, this paper is proposed a new approach by using an Advanced IoU loss function. Three geometric factors including overlap area, distances and side length are considered in the proposed function. The proposal focuses on the overlap area to improve accuracy for object detection. By this way, the proposal can relocate anchor box for covering the ground truth in the training process and optimize anchor boxes for object detection. The proposal is tested on VOC Pascal and MS COCO dataset. The experimental results are compared to existing IoU models and show that the proposal can improve accuracy level for Bounding box regression.
Advances in Measuring Inflation Within Virtual Economies Using Deep Reinforcement Learning

This paper assesses an improved framework for evaluating the performance of economies within online multiplayer games. Games in this medium traditionally require a great deal of testing and analysis to assess the outcomes and affects of player interactions. This process is normally imperfect and time consuming, leading a lot of games developers and publishers to maintain a lot of risk during the development and launch of multi-million dollar projects. The framework and example project presented within this paper uses deep reinforcement learning to simulate economic interactions between learning agents. This is possible by having agents learn from player demonstrations to interact with game systems such as Battling, Collecting Resources and Crafting Weapons which allows them to co-ordinate and cooperate to achieve long-term goals in the game. This paper shows recent breakthroughs in relation to training these agents to the variety of metrics learning agents can generate to help games designers test and polish multiplayer experience, which have proved hard to quantify and measure without extensive play testing. This paper is an extension paper to our publication in ICAART 2021 [21], within this paper includes the following additions:
Attending to SPARQL Logs for Knowledge Representation Learning

Knowledge Representation Learning (KRL) maps entities and relations to a continuous low-dimensional vector space, alleviating data sparsity of Knowledge Graphs (KGs) and improving computing efficiency. However, most prior KRL models represented by TransE embed all triple facts equally, without distinguishing latent semantic information of relations. In this paper, (1) we propose a novel correlation-aware Knowledge Representation Learning framework, which integrates semantic features of relations while embedding; (2) we capture the semantics by mining time-sequence and frequency characteristics of relations from historical SPARQL logs; (3) we design a weighted encoder to distinguish correlation of each triple according to relational semantics, and introduce the triple correlation into translation-based models to enhance entity representations. Experimental results on Wikidata datasets show that our proposed model significantly outperformed the state-of-the-art translation-based models on both knowledge graph completion tasks and triple classification tasks.
GADN: GCN-Based Attentive Decay Network for Course Recommendation

Course recommendation in online platforms aims to address the information explosion problem and make personalized recommendations for users. Most of the recent recommendation models mainly strive to model inherent user preference while ignoring the users’ learning process and overlooking the relations among courses (e.g., The prerequisite course for Deep Learning is Linear Algebra). This paper proposes an innovative model named GCN-based Attentive Decay Network for Course Recommendation (GADN). Specifically, (1) we utilize the GCN-based Knowledge Extraction Layer to explicitly model the relationships on Collaborative Sequence Graph (CSG), which incorporates user-item interactions and course sequence information; (2) we incorporate the Knowledge Evolution Layer with a monotonic attention decay mechanism to model users’ learning process. At the Knowledge Evolution Layer, we calculate attention weights using exponential decay and an absolute distance measure, in addition to the similarity between courses; (3) our method has a specific explanation through visualizing attention weight. Systematically, we conduct a series of experiments to demonstrate the effectiveness of our model on several prevalent metrics compared to the other prevailing baseline methods.
Real-Time Object Following Based on Visual Information and Online Video Stabilization for Unmanned Aerial Vehicles

In recent, Unmanned Aerial Vehicles (UAV) have been becoming widely used for many applications such as documentary, agriculture, rescue, surveillance, etc. This research addresses two problems of UAVs. The first is designing the solution for real-time object following using visual information to extract the target object on a current frame defined by the user in the initial state and estimate the position control parameter for the drone system based on the ArduPilot opensource flight controller. In complex systems and wind-disturbance can lead to vibration of video sensor, and the research also focuses on designing the online video stabilization based Kalman multi-rate to improve the navigation performance. The architecture is validated using the ArduPilot flight controller and Jetson TX2 for the object tracking-based OpenTLD tracker method. The experiment shows the system’s capabilities can track a great variety of things present in outdoor areas and video stable robustness against wind perturbations and target occlusion in outdoor environments.
Asymmetric Neighboring Context Modeling for Knowledge Graph Embedding

Knowledge graph embedding (KGE) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples. When dealing with surrounding information, recent models either ignore the interactions between triples within the knowledge graph or use too many parameters to take the surrounding information into the model. Besides, the asymmetric information in the surrounding triples deserves further investigation. In this paper, we propose an Asymmetric Context Aware Representation for Knowledge Graph Embedding method (AcarE). Specifically, we first use an asymmetric context encoder to introduce the surrounding triples information to the head and relation entity. Afterwards we use an encoding system based on convolutional neural network (CNN) to encode the context-aware head and context-aware relation. Experimental results on both WN18RR and FB15K-237 datasets demonstrate the AcarE’s promising potential.
A Neural-Machine-Translation System Resilient to Out of Vocabulary Words for Translating Natural Language to SPARQL

The development and diffusion of ontologies allowed the creation of large banks of information regarding multiple domains known as knowledge bases. Ontologies propose a way to represent information providing semantic meaning that allows the data to be machine-interpretable. However, enjoying such rich knowledge is a difficult task for the majority of potential users who do not know either the knowledge-base definition or how to write queries with SPARQL. Systems able to translate natural language questions into SPARQL queries have the potential to overcome this problem. In this paper, we propose an approach that combines the Named Entity Recognition and Neural Machine Translation tasks to perform an automatic translation of natural language questions into executables SPARQL queries. The resulting approach provides robustness to the presence of terms that do not occur in the training set. We evaluate the potential of our approach by using Monument and QALD-9, which are well-known datasets for Question Answering over the DBpedia ontology.
Cross-Sentence Temporal Relation Extraction with Relative Sentence Time

Event temporal relation is capable to detect event evolution and plays an important role in natural language processing. Many recent studies which employ pre-trained language models have shown prominent performance improvement. However, due to more complex context, these approaches usually perform poorly when two events are not within the same sentence. Thus in this paper, we propose a cross-sentence temporal relation extraction model which incorporates the prediction of temporal relations between sentences to enhance the performance of temporal event relation extraction. A multi-task learning framework is adopted by integrating the temporal relation classifier with an auxiliary task to predict the temporal order of the sentences. In addition, to deal with the problem of class-imbalanced data, we propose a sub-sampling method by decreasing the number of Vague relations. Compared to the baseline model, extensive experiments show that our model is capable to enhance the performance of cross-sentence temporal relation extraction while achieving state-of-the-art results on TimeBank-Dense, MATRES, and TCR dataset.