LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

一种使用多跳事实的端到端知识库实体描述生成方法

自动化实体描述生成有助于进一步提升知识图谱的应用价值,而流畅度高是实体描述文本的重要质量指标之一。该文提出使用知识库上多跳的事实来进行实体描述生成,从而贴近人工编撰的实体描述的行文风格,提升实体描述的流畅度。该文使用编码器—解码器框架,提出了一个端到端的神经网络模型,可以编码多跳的事实,并在解码器中使用关注机制对多跳事实进行表示。该文的实验结果表明,与基线模型相比,引入多跳事实后模型的BLEU-2和ROUGE-L等自动化指标分别提升约8.9个百分点和7.3个百分点。     

石化企业实时数据管理引擎设计与实现

实时数据管理系统是流程企业控制信息网络与管理信息网络平滑集成的基础，为两者的数据通讯提供桥梁。介绍了笔者在炼油化工C企业IMS建设中的实时数据管理引擎设计与开发，具有其先进性与实用性。     

原油基压裂液研究

研制了一种适用于低压、低渗、水敏性储集层的新型原油基压裂液体系。根据基液原油的组分合成相应的增稠剂，增稠剂在原油中与交联剂反应，形成网状结构，使原油成为黏度可以调控的冻胶，从根本上改变了油基压裂液以柴油、煤油为基液的现状，降低了油基压裂液的成本，同时还提高了压裂液的抗温、抗剪切和破胶性能，并将交联时间缩短到2h以内，可以满足现场压裂施工的要求，大大降低了施工强度和压裂改造的综合成本。在青海油田进行的现场先导试验取得成功。图9表1参5     

O—O数据模型中实体联系的表达方法

具有对实体和实体间联系的描述能力，是数据库的基本，吼是对数据库进逻辑设计的基本出发点。Ｏ－Ｏ数据模型不具备显式描术这实体联系的手段。且优于关系模型。     

外购粗铅检验准确性测试

通过对外购粗铅的整体熔化，测试出外购粗铅中金、银的含量，以验证外购粗铅检验的准确性，确保铅冶炼金银平衡。     

Binary rewriting and call interception for efficient runtime protection against buffer overflows

Buffer overflow vulnerabilities are one of the most commonly and widely exploited security vulnerabilities in programs. Most existing solutions for avoiding buffer overflows are either inadequate, inefficient or incompatible with existing code. In this paper, we present a novel approach for transparent and efficient runtime protection against buffer overflows. The approach is implemented by two tools: Type Information Extractor and Depositor (TIED) and LibsafePlus. TIED is first used on a binary executable or shared library file to extract type information from the debugging information inserted in the file by the compiler and reinsert it in the file as a data structure available at runtime. LibsafePlus is a shared library that is preloaded when the program is run. LibsafePlus intercepts unsafe C library calls such as strcpy and uses the type information made available by TIED at runtime to determine whether it would be ‘safe’ to carry out the operation. With our simple design we are able to protect most applications with a performance overhead of less than 10%. Copyright © 2006 John Wiley & Sons, Ltd.     

A method for quantifying cell size from differential interference contrast images: validation and application to osmotically stressed chondrocytes

An automatic image analysis method was developed to determine the shape and size of spheroidal cells from a time series of differential interference contrast (DIC) images. The program incorporates an edge detection algorithm and dynamic programming for edge linking. To assess the accuracy and working range of the method, results from DIC images of different focal planes and resolutions were compared to confocal images in which the cell membrane was fluorescently labelled. The results indicate that a 1‐µm focal drift from the in‐focus plane can lead to an overestimation of cell volume up to 14.1%, mostly due to shadowing effects of DIC microscopy. DIC images allow for accurate measurements when the focal plane lies in a zone slightly above the centre of a spherical cell. In this range the method performs with 1.9% overall volume error without taking into account the error introduced by the representation of the cell as a sphere. As a test case, the method was applied to quantify volume changes due to acute changes of osmotic stress.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.