scholarly journals Fact Checking via Evidence Patterns

2018 ◽  
Author(s):  
Valeria Fionda ◽  
Giuseppe Pirrò
Keyword(s):  
Query Language ◽  
State Of The Art ◽  
Sparql Query ◽  
Efficient Algorithms ◽  
The State ◽  
The Core ◽  
Numerical Assessment ◽  
Comparable Accuracy ◽  
Fact Checking ◽  
Knowledge Graphs

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and provide a numerical assessment of its truthfulness. We present efficient algorithms to generate and verify evidence patterns, and assemble evidence. We also provide a translation of the core of our algorithms into the SPARQL query language. Not only our approach is faster than the state of the art and offers comparable accuracy, but it can also use any SPARQL-enabled KG.

High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution

2020 ◽  
Author(s):  
Anastasia Dimou
Keyword(s):  
State Of The Art ◽  
The State ◽  
Knowledge Graph ◽  
High Quality ◽  
Inconsistency Resolution ◽  
Knowledge Graphs ◽  
Rdf Graphs ◽  
Graph Generation

In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.

Ex-Vessel Coolability Analysis for a Belgian NPP

2009 ◽  
Author(s):  
Jarne R. Verpoorten ◽  
Miche`le Auglaire ◽  
Frank Bertels
Keyword(s):  
State Of The Art ◽  
The State ◽  
Severe Accident ◽  
Research Projects ◽  
Specific Data ◽  
The Core ◽  
Core Damage ◽  
Accident Management ◽  
Lower Head ◽  
Core Cooling

During a hypothetical Severe Accident (SA), core damage is to be expected due to insufficient core cooling. If the lack of core cooling persists, the degradation of the core can continue and could lead to the presence of corium in the lower plenum. There, the thermo-mechanical attack of the lower head by the corium could eventually lead to vessel failure and corium release to the reactor cavity pit. In this paper, it is described how the international state-of-the-art knowledge has been applied in combination with plant-specific data in order to obtain a custom Severe Accident Management (SAM) approach and hardware adaptations for existing NPPs. Also the interest of Tractebel Engineering in future SA research projects related to this topic will be addressed from the viewpoint of keeping the analysis up-to-date with the state-of-the art knowledge.

scholarly journals MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Semantic Web ◽  
2021 ◽  
pp. 1-17
Author(s):  
Lucia Siciliani ◽  
Pierpaolo Basile ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
State Of The Art ◽  
Translation Process ◽  
Specific Data ◽  
Data Query ◽  
Question Answering Systems ◽  
Knowledge Graphs ◽  
The Impact

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.

scholarly journals A Biscriptual Morphological Transducer for Crimean Tatar

2019 ◽  
Author(s):  
Francis M. Tyers ◽  
Jonathan N. Washington ◽  
Darya Kavitskaya ◽  
Memduh Gökırmak
Keyword(s):  
Computational Linguistics ◽  
Morphological Analysis ◽  
State Of The Art ◽  
Full Range ◽  
The State ◽  
Loan Words ◽  
The Core ◽  
Finite State ◽  
Morphological Modelling ◽  
Crimean Tatar

This paper describes a weighted finite-state morphological transducer for Crimean Tatar able to analyse and generate in both Latin and Cyrillic orthographies. This transducer was developed by a team including a community member and language expert, a field linguist who works with the community, a Turkologist with computational linguistics expertise, and an experienced computational linguist with Turkic expertise. Dealing with two orthographic systems in the same transducer is challenging as they employ different strategies to deal with the spelling of loan words and encode the full range of the language's phonemes and their interaction. We develop the core transducer using the Latin orthography and then design a separate transliteration transducer to map the surface forms to Cyrillic. To help control the non-determinism in the orthographic mapping, we use weights to prioritise forms seen in the corpus. We perform an evaluation of all components of the system, finding an accuracy above 90% for morphological analysis and near 90% for orthographic conversion. This comprises the state of the art for Crimean Tatar morphological modelling, and, to our knowledge, is the first biscriptual single morphological transducer for any language.

scholarly journals End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion

2019 ◽  
Vol 33 ◽  
pp. 3060-3067 ◽  
Author(s):  
Chao Shang ◽  
Yun Tang ◽  
Jing Huang ◽  
Jinbo Bi ◽  
Xiaodong He ◽  
...  
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
The State ◽  
Graph Connectivity ◽  
Knowledge Graph ◽  
Graph Node ◽  
Convolutional Network ◽  
Node Attributes ◽  
Knowledge Graphs ◽  
End To End

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.

scholarly journals Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Yinyu Lan ◽  
Shizhu He ◽  
Kang Liu ◽  
Xiangrong Zeng ◽  
Shengping Liu ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Semantic Representation ◽  
Medical Knowledge ◽  
The State ◽  
Language Models ◽  
Knowledge Graph ◽  
Knowledge Reasoning ◽  
Numerical Computing ◽  
Knowledge Graphs

Abstract Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. Methods To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Results Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. Conclusions In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

scholarly journals Adversarial Imitation Learning from Incomplete Demonstrations

2019 ◽  
Author(s):  
Mingfei Sun ◽  
Xiaojuan Ma
Keyword(s):  
State Of The Art ◽  
Auxiliary Information ◽  
The State ◽  
Imitation Learning ◽  
Action Sequence ◽  
State Information ◽  
The Core ◽  
Action Sequences ◽  
Core Idea ◽  
Novel Algorithm

Imitation learning targets deriving a mapping from states to actions, a.k.a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservable actions have been proposed, they focus solely on state information and over- look the fact that the action sequence could still be partially available and provide useful information for policy deriving. In this paper, we propose a novel algorithm called Action-Guided Adversarial Imitation Learning (AGAIL) that learns a pol- icy from demonstrations with incomplete action sequences, i.e., incomplete demonstrations. The core idea of AGAIL is to separate demonstrations into state and action trajectories, and train a policy with state trajectories while using actions as auxiliary information to guide the training whenever applicable. Built upon the Generative Adversarial Imitation Learning, AGAIL has three components: a generator, a discriminator, and a guide. The generator learns a policy with rewards provided by the discriminator, which tries to distinguish state distributions between demonstrations and samples generated by the policy. The guide provides additional rewards to the generator when demonstrated actions for specific states are available. We com- pare AGAIL to other methods on benchmark tasks and show that AGAIL consistently delivers com- parable performance to the state-of-the-art methods even when the action sequence in demonstrations is only partially available.

scholarly journals 13. Taking Stock of Integration Theory

2009 ◽  
Author(s):  
Antje Wiener ◽  
Thomas Diez
Keyword(s):  
European Integration ◽  
Theoretical Approach ◽  
State Of The Art ◽  
The State ◽  
Integration Theory ◽  
Test Cases ◽  
Historical Overview ◽  
Constitutional Development ◽  
The Core ◽  
Theoretical Perspectives

This volume has examined the state of the art in European integration theorizing with chapters which have presented and reflected upon the core theoretical contributions that have been developed since the early stages of studying European integration and governance. This concluding chapter provides a historical overview of the type and focus of each theoretical approach to European integration. It compares the respective strengths and weaknesses of each approach according to the definitions of ‘theorizing’ and ‘integration’ developed in the introduction. It also considers the first edition’s outlook on constitutional development and identifies current challenges that lie ahead. It argues that the different theoretical perspectives discussed in this edition demonstrate an emerging robustness of European integration theory. It suggests that the variation in approaching the respective ‘test cases’ of European enlargement reveals the need for both rigorously prescriptive and normative approaches to European integration.

scholarly journals multiTFA: a Python package for multi-variate Thermodynamics-based Flux Analysis

2020 ◽  
Author(s):  
Vishnuvardhan Mahamkali ◽  
Tim McCubbin ◽  
Moritz Emanuel Beber ◽  
Esteban Marcellin ◽  
Lars Keld Nielsen
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
State Of The Art ◽  
The State ◽  
Group Contribution ◽  
Flux Analysis ◽  
E Coli ◽  
The Core ◽  
Thermodynamic Variables ◽  
Python Package

AbstractSummaryWe achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables.ResultsWe present multiTFA, a Python implementation of our framework. We evaluated our application using the core E. coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible.Availability and implementationOur framework along with documentation is available on https://github.com/biosustain/multitfa.

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