News Topic Detection Based on Capsule Semantic Graph

Most news topic detection methods use word-based methods,which easily ignore the relationship among words and have semantic sparsity,resulting in low topic detection accuracy.In addition,the current mainstream probability methods and graph analysis methods for topic detection have high time complexity.For these reasons,we present a news topic detection model on the basis of capsule semantic graph(CSG).The keywords that appear in each text at the same time are modeled as a keyword graph,which is divided into multiple subgraphs through community detection.Each subgraph contains a group of closely related keywords.The graph is used as the vertex of CSG.The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex.At the same time,the news text is clustered using the incremental clustering method,where each text uses CSG;that is,the similarity among texts is calculated by the graph kernel.The relationship between vertices and edges is also considered when calculating the similarity.Experimental results on three standard datasets show that CSG can obtain higher precision,recall,and F1 values than several latest methods.Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.

Graph reasoning over explicit semantic relation

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research,particularly with the reliance on deep neural networks.These networks are integral to text reasoning processes,yet they present challenges in extracting and representing domain or commonsense knowledge,and they often lack robust logical reasoning capabilities.To address these issues,we introduce an innovative text reasoning framework.This framework is grounded in the use of a semantic relation graph and a graph neural network,designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.Our framework operates by extracting knowledge from a broad range of texts.It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process.Beginning with the core question,the framework methodically deduces key knowledge,using it as a guide to iteratively establish a complete evidence chain,thereby determining the final answer.Leveraging the advanced reasoning capabilities of the graph neural network,this approach is adept at multi-hop logical reasoning.It demonstrates strong performance in tasks like machine reading comprehension and question answering,while also clearly delineating the path of logical reasoning.

Chinese Semantic Parsing Based on Feature Structure with Recursive Directed Graph

It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.

A survey of syntactic-semantic parsing based on constituent and dependency structures

Syntactic and semantic parsing has been investigated for decades,which is one primary topic in the natural language processing community.This article aims for a brief survey on this topic.The parsing community includes many tasks,which are difficult to be covered fully.Here we focus on two of the most popular formalizations of parsing:constituent parsing and dependency parsing.Constituent parsing is majorly targeted to syntactic analysis,and dependency parsing can handle both syntactic and semantic analysis.This article briefly reviews the representative models of constituent parsing and dependency parsing,and also dependency graph parsing with rich semantics.Besides,we also review the closely-related topics such as cross-domain,cross-lingual and joint parsing models,parser application as well as corpus development of parsing in the article.

高精地图的知识图谱表达

高精地图是自动驾驶的“传感器”,为自动驾驶提供必要的先验数据以及相应的超视距感知、校验定位、动态规划和决策控制。然而,高精地图数据供给与自动驾驶知识需求仍存在鸿沟,包括数据量大导致查询困难、数据关联弱导致语义理解和智能决策困难。知识图谱是将知识以图的结构表达出来,以描述实体及其关系,涉及实体抽取和关系抽取。为此,在高精地图数据基础上,引入知识图谱,提出高精地图知识图谱的构建方法,以架起地图数据供给与驾驶知识需求之间的桥梁,支撑高精地图数据到自动驾驶知识的转化。构建的知识图谱实例,一方面将高精地图海量数据采用图进行了二次表达,建立了类似于索引的结构;另一方面显式表达了面向自动驾驶需求的语义关系。实验结果表明,知识图谱能为高精地图的语义查询、知识推理和局部决策规划提供基础。所提出的方法能实现高精地图先验数据的语义结构化,推进高精地图由数据到信息到知识的跨越,为自动驾驶的落地贡献先验知识。

Ontology-Driven Mashup Auto-Completion on a Data API Network

The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network （sBN） and a semantic graph template were used for the link prediction on the Semantic Web and to construct a data API network denoted as Np. The performance is improved by a semi-supervised learning method which uses both labeled and unlabeled data. Then, this network is used to build an ontology-driven mashup auto-completion system to help users build mashups by providing three kinds of recommendations. Tests demonstrate that the approach has a precisionp of about 80%, recallp of about 60%, and F0.5 of about 70% for predicting links between APIs. Compared with the API network Ne com-posed of existing links on the current Web, Np contains more links including those that should but do not exist. The ontology-driven mashup auto-completion system gives a much better recallr and discounted cumula-tive gain （DCG） on Np than on Ne. The tests suggest that this approach gives users more creativity by constructing the API network through predicting mashup APIs rather than using only existing links on the Web.

Tag recommendation for open source software

Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.