Building a Large-scale Software Programming Taxonomy from Stackoverflow

Zhu, Jingtao; Shen, Beijun; Cai, Xuyang; Wang, Haofen

doi:10.18293/seke2015-135

Cited by 23 publications

(20 citation statements)

References 16 publications

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“…As there are no public knowledge bases in the field of coding convention, we compare CCBase with related subsets of a software engineering knowledge bases such as SEBase [5] and software.zhishi.schema [19]. We also compare it with YAGO [18], a general knowledge base.…”

Section: B Comparison With Other Knowledge Basesmentioning

confidence: 99%

Constructing a Knowledge Base of Coding Conventions from Online Resources

Cao

Shen

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Coding conventions are a set of coding guidelines used by software developers to improve the readability of source code, increase software maintainability, and promote the reuse of coding patterns. In this paper, we introduce CCBase, a knowledge base of coding conventions, that was constructed from online resources. Specifically, CCBase was constructed as follows. We designed the ontology of the coding convention domain, crawled data related to coding conventions from a variety of online resources, and then extracted entities and relations using an NLP-enabled rule matching method. To uncover the latent relations, we further proposed a similarity metric to reveal the similar-to and relate-to relations, and developed a RCE algorithm to establish a unified type hierarchy of coding conventions. The resulting knowledge base contains 3139 coding conventions for Java and C++, with 3761 entities and 767 relations. Furthermore, we have extended the usability of CCBase by developing a question answering system on the base. We have conducted experiments to evaluate CCBase. The experimental results show that CCBase has a wide coverage on entities and relations in coding conventions domain, and the QA system achieves an F1 score of 84.5% on 214 questions raised in StackOverflow.

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Section: B Comparison With Other Knowledge Basesmentioning

confidence: 99%

Constructing a Knowledge Base of Coding Conventions from Online Resources

Cao

Shen

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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“…knowledge base data, which are usually stored in graphs, most notably DBPedia [12], YAGO [13], and Freebase [14]. In the Chinese language field, Zhishi.me [15] is a knowledge base for extracting structured data from open encyclopedia data. It integrates three Chinese encyclopedias, including Baidu Encyclopedia, Interactive Encyclopedia and Wikipedia and it has over 5 million entities.…”

Section: Semi-structured Data Includes Open-link Data and Openmentioning

confidence: 99%

A Semantic Path Based Approach to Match Subgraphs from Large Financial Knowledge Graph

Wang¹,

Zhang²,

Hu³

2019

Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)

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In the past, people studied the stock market based on the assumption that the stock entity is known to be affected by the news. However, due to this assumption, these methods inevitably ignore the news without stock entities, and many news without stock entities will also have a significant impact on financial markets. In order to solve this problem, this paper proposes a subgraph matching algorithm based on semantic paths. Matching subgraphs on a knowledge graph that collects a large amount of stock market information and matching the affected stock entities from the semantic level can make a comprehensive analysis on Various news with or without entities. The main research work and achievements of this paper are as follows: First, starting with structured data, the paper complements semi-structured data and unstructured data to build a knowledge graph of the stock market and covering most of the stock market entities. Secondly, based on the analysis of LDA topic model, this dissertation extracts useful topics from financial news and constructs a news graph. A subgraph matching algorithm based on semantic path is proposed. From the knowledge graph, subgraphs matching with news graph are searched for mining the associated entities in the financial news. Finally, according to the result of subgraph matching, experiments and simulated investment are designed. The strategy achieved 15.96% excess return relative to the benchmark. The effectiveness of the subgraph matching algorithm based on semantic path is verified, and the feasibility of the algorithm in actual investment is proved.

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“…[13] constructed a programming language ontology. Software.zhishi.schema [14] uses tags of Stackoverflow build a software engineering knowledge base. We broaden the scale of concepts based on Software.zhishi.schema by interlinking Stackoverflow tags to Wikipedia entities.…”

Section: B Software Engineering Knowledge Basementioning

confidence: 99%

Building a Domain Knowledge Base from Wikipedia: a Semi-supervised Approach

Chen¹,

Dong²,

Zhu³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Knowledge bases are becoming indispensable to software engineering and knowledge engineering. However, the existing domain knowledge bases are always artificially constructed and small-scale. In this paper, we propose a semi-supervised approach to domain concepts detection and software engineering knowledge base construction from Wikipedia. First, the approach selects domain relevant tags from Stackoverflow. Then, it matches Wikipedia entities and expands the concept set through an improved label propagation algorithm. A rule-based method is designed to discover semantic relations including relate, subclassOf and equal by analyzing structural information of Wikipedia. A relation derivation mechanism is presented to optimize the relation set. We finally construct SEBase, a domainspecific knowledge base of software engineering. Experimental results show the high accuracy of the integrated concepts and relations. Compared with other knowledge bases, SEBase has the widest coverage of concepts and relations in software engineering.

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Building a Large-scale Software Programming Taxonomy from Stackoverflow

Cited by 23 publications

References 16 publications

Constructing a Knowledge Base of Coding Conventions from Online Resources

Constructing a Knowledge Base of Coding Conventions from Online Resources

A Semantic Path Based Approach to Match Subgraphs from Large Financial Knowledge Graph

Building a Domain Knowledge Base from Wikipedia: a Semi-supervised Approach

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