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AMUSE: Multilingual semantic parsing for question answering over linked data

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arXiv 2018年

作者： Hakimov, Sherzod Jebbara, Soufian Cimiano, Philipp Bielefeld University Bielefeld33615 Germany

The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic *** rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish. Copyright © 2018, The Authors. All rights reserved.

关键词： Query processing

Hierarchical information matters: Text classification via tree based graph neural network

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arXiv 2021年

作者： Zhang, Chong Zhu, He Peng, Xingyu Wu, Junran Xu, Ke State Key Lab of Software Development Environment Beihang University Beijing100191 China

Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. As a special kind of graph data, the tree has a simpler data structure and can provide rich hierarchical information for text classification. Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification. Specifically, we first establish a dependency parsing graph for each text. Then we designed a structural entropy minimization algorithm to decode the key information in the graph and convert each graph to its corresponding coding tree. based on the hierarchical structure of the coding tree, the representation of the entire graph is obtained by updating the representation of non-leaf nodes in the coding tree layer by layer. Finally, we present the effectiveness of hierarchical information in text classification. Experimental results show that HINT outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. © 2021, CC BY-NC-SA.

关键词： graph neural networks

Few-Shot Knowledge graph Completion based on Data Enhancement

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Few-Shot Knowledge Graph Completion based on Data Enhancemen...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zepeng Li Peilun Geng Shuo Cao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Chines Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Knowledge graphs (KGs) are widely used in various natural language processing applications. In order to expand the coverage of a KG, KG completion has attracted extensive attention. The commonly used embedding methods based on a large amount of training data can play an important role in this work. However, with few of triples, the performance of these methods will be greatly reduced. The completion of this kind of few-shot task is more challenging. In this work, we propose a method of data enhancement to increase the data quantity and solve the problem of sample shortage. Specifically, we first observe that the representation vectors of the relation in a KG are approximately subordinate to Gaussian distribution. Then we construct a Gaussian distribution for the relation of each triple in few-shot task according to the distributions of its similar relations in background graph. Further, we sample from the Gaussian distribution of each triple to expand the training data. Finally, we use an adaptive attentional network model FAAN proposed by Sheng et al. as the baseline model. Experimental results on two public datasets NELL-One and Wiki-One show that the proposed method achieves better performance.

关键词： Adaptation models Adaptive systems Biological system modeling Training data Gaussian distribution natural language processing Task analysis

EXECUTABLE first-ORDER QUERIES IN THE LOGIC OF INFORMATION FLOWS ∗

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LOGICAL methods IN COMPUTER SCIENCE 2024年第2期20卷

作者： Aamer, Heba A. Bogaerts, Bart Surinx, Dimitri Ternovska, Eugenia Van Den Bussche, Jan Vrije Univ Brussel Brussels Belgium Hasselt Univ Hasselt Belgium Simon Fraser Univ Burnaby BC Canada

. The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of procedural nature can still be modeled in a declarative, logic-based fashion. In this paper, we focus on the task of query processing under limited access patterns, a well-studied problem in the database literature. We show that LIF is well-suited for modeling this task. Toward this goal, we introduce a variant of LIF called "forward" LIF (FLIF), in a first-order setting. FLIF takes a novel graph-navigational approach;it is an XPath-like language that nevertheless turns out to be equivalent to the "executable" fragment of first-order logic defined by Nash and Lud & acirc;scher. One can also classify the variables in FLIF expressions as inputs and outputs. Expressions where inputs and outputs are disjoint, referred to as iodisjoint FLIF expressions, allow a particularly transparent translation into algebraic query plans that respect the access limitations. Finally, we show that general FLIF expressions can always be put into io-disjoint form.

关键词： Limited access pattern expressive power variable substitution composition.

Knowledge graph Enhanced Large language Model Editing

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arXiv 2024年

作者： Zhang, Mengqi Ye, Xiaotian Liu, Qiang Ren, Pengjie Wu, Shu Chen, Zhumin School of Computer Science and Technology Shandong University China School of Computer Science Beijing University of Posts and Telecommunications China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing emerges as a promising solution to address these challenges. However, existing editing methods struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge. To tackle these problems, we propose a novel model editing method that leverages knowledge graphs for enhancing LLM editing, namely GLAME. Specifically, we first utilize a knowledge graph augmentation module to uncover associated knowledge that has changed due to editing, obtaining its internal representations within LLMs. This approach allows knowledge alterations within LLMs to be reflected through an external graph structure. Subsequently, we design a graph-based knowledge edit module to integrate structured knowledge into the model editing. This ensures that the updated parameters reflect not only the modifications of the edited knowledge but also the changes in other associated knowledge resulting from the editing process. Comprehensive experiments conducted on GPT-J and GPT-2 XL demonstrate that GLAME significantly improves the generalization capabilities of post-edit LLMs in employing edited knowledge. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

Knowledge graph question answering via SPARQL silhouette generation

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arXiv 2021年

作者： Purkayastha, Sukannya Dana, Saswati Garg, Dinesh Khandelwal, Dinesh Bhargav, G.P. Shrivatsa TCS Research IBM Research

Knowledge graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two-stage neural architecture to solve the KGQA task. The first stage generates a sketch of the target SPARQL called SPARQL silhouette for the input question. This comprises of (1) Noise simulator to facilitate out-of-vocabulary words and to reduce vocabulary size (2) seq2seq model for text to SPARQL silhouette generation. The second stage is a Neural graph Search Module. SPARQL silhouette generated in the first stage is distilled in the second stage by substituting precise relation in the predicted structure. We simulate ideal and realistic scenarios by designing a noise simulator. Experimental results show that the quality of generated SPARQL silhouette in the first stage is outstanding for the ideal scenarios but for realistic scenarios (i.e. noisy linker), the quality of the resulting SPARQL silhouette drops drastically. However, our neural graph search module recovers it considerably. We show that our method can achieve reasonable performance improving the state-of-art by a margin of 3.72% F1 for the LC-QuAD-1 dataset. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are suited for practical applications. © 2021, CC BY.

关键词： Knowledge graph

Family History Extraction From Synthetic Clinical Narratives Using natural language processing: Overview and Evaluation of a Challenge Data Set and Solutions for the 2019 National NLP Clinical Challenges (n2c2)/Open Health natural language processing (OHNLP) Competition

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JMIR MEDICAL INFORMATICS 2021年第1期9卷 e24008页

作者： Shen, Feichen Liu, Sijia Fu, Sunyang Wang, Yanshan Henry, Sam Uzuner, Ozlem Liu, Hongfang Mayo Clin Div Digital Hlth Sci 200 First St SW Rochester MN 55905 USA George Mason Univ Dept Informat Sci & Technol Fairfax VA 22030 USA MIT Dept Biomed Informat 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Med Sch Dept Biomed Informat Boston MA 02115 USA

Background: As a risk factor for many diseases, family history (FH) captures both shared genetic variations and living environments among family members. Though there are several systems focusing on FH extraction using natural language processing (NLP) techniques, the evaluation protocol of such systems has not been standardized. Objective: The n2c2/OHNLP (National NLP Clinical Challenges/Open Health natural language processing) 2019 FH extraction task aims to encourage the community efforts on a standard evaluation and system development on FH extraction from synthetic clinical narratives. methods: We organized the first BioCreative/OHNLP FH extraction shared task in 2018. We continued the shared task in 2019 in collaboration with the n2c2 and OHNLP consortium, and organized the 2019 n2c2/OHNLP FH extraction track. The shared task comprises 2 subtasks. Subtask 1 focuses on identifying family member entities and clinical observations (diseases), and subtask 2 expects the association of the living status, side of the family, and clinical observations with family members to be extracted. Subtask 2 is an end-to-end task which is based on the result of subtask 1. We manually curated the first deidentified clinical narrative from FH sections of clinical notes at Mayo Clinic Rochester, the content of which is highly relevant to patients' FH. Results: A total of 17 teams from all over the world participated in the n2c2/OHNLP FH extraction shared task, where 38 runs were submitted for subtask 1 and 21 runs were submitted for subtask 2. For subtask 1, the top 3 runs were generated by Harbin Institute of Technology, ezDI, Inc., and The Medical University of South Carolina with F1 scores of 0.8745, 0.8225, and 0.8130, respectively. For subtask 2, the top 3 runs were from Harbin Institute of Technology, ezDI, Inc., and University of Florida with F1 scores of 0.681, 0.6586, and 0.6544, respectively. The workshop was held in conjunction with the AMIA 2019 Fall Symposium. Conclusi

关键词： family history extraction information extraction natural language processing named entity recognition relation extraction

PubTrends - a scientific literature explorer 21

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PubTrends - a scientific literature explorer

12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Shpynov, Oleg Nikolai, Kapralov JetBrains Res Biolabs St Petersburg Russia Max Planck Inst Human Cognit & Brain Sci Leipzig Leipzig Germany

ISBN: (纸本)9781450384506

With an ever-increasing number of scientific papers published each year, it becomes more difficult for researchers to explore unfamiliar or fast-growing research areas. This greatly inhibits the potential for cross-disciplinary research. When approaching a new subject, a researcher often starts with a search of relevant papers in a dedicated search engine - Google Scholar, Scopus, etc. Besides the search by title, author names, or keywords, these services also provide the user with basic statistics such as number of citations of the paper. However, it is hard to organize the information from these papers, especially with the current rise in publication numbers. For instance, regarding the latest COVID-19 epidemic, thousands of papers were published in the first *** methods [1] allow for structuring the information in papers by highlighting the most frequent keywords in the selected area. Also, the number of citations is widely used to rank papers according to their estimated scientific impact, citation and co-citation networks for groups of papers may reflect the underlying structure of the research field [2]. Papers are more likely to be co-cited if they belong to closely related research topics, and network analysis allows extracting meaningful clusters representing different scientific *** approach for structuring the information is based on the methods for natural language processing. In particular, we have shown that citation context can be used for summarization of papers and, subsequently, research topics [3].To our knowledge, no freely available tools exist that combine different approaches for structuring information in research papers in an easy-to-use web service. Most bibliometrics tools require manual preparation of papers dataset, and most of the summarization methods are lacking ready-to-use *** present PubTrends - a scientific publication exploratory tool capable of analyzing the intellectual structure of

关键词： scientific publications extractive summarization visualization citations analysis graph analysis communities detection PubMed bibliometrics

A survey based on knowledge graph in fault diagnosis, analysis and prediction: key technologies and challenges

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A survey based on knowledge graph in fault diagnosis, analys...

Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Lianqing Su Ziyuan Wang Yude Ji Xing Guo Hebei University of Science and Technology Shijiazhuang China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

With the advent of artificial intelligence and the advent of the era of big data, knowledge graph can graphically display the relationship between a large number of structured, semi-structured and unstructured data. A knowledge graph is a graph-based data structure that connects different types of information into a semantic network. The knowledge graph is intelligent and efficient, helping users to quickly find and search for the information they want. Today, knowledge graphs are used in a variety of fields, such as smart search, chat bots, finance and medical. Nowadays, the mobile phone has become an indispensable tool for us, but its software and hardware often have various problems. In order to better meet the needs of users, knowledge graph are used for fault diagnosis, analysis and prediction. In this paper, the knowledge graph is discussed in the field of mobile phone faults. This paper first outlines the application of knowledge graph in other fields and methods used to construct knowledge graph in the field, such as finance, biology and medicine; discusses the data sources for knowledge graph construction; discusses the architecture of knowledge graph and related technologies, including The extraction of entities and relationships; finally, some of the challenges and open issues facing the knowledge graph are discussed.

关键词： Fault diagnosis Support vector machines Finance Tools natural language processing Mobile handsets Software