Sa Gao, Zhenchang Xing, Yukun Ma, Deheng Ye, Shang-Wei Lin
{"title":"通过外部Web资源自动链接增强堆栈溢出中的知识共享","authors":"Sa Gao, Zhenchang Xing, Yukun Ma, Deheng Ye, Shang-Wei Lin","doi":"10.1109/ICECCS.2017.30","DOIUrl":null,"url":null,"abstract":"Referencing URLs of external web resources (e.g., official language references and API documents) is an effective mechanism for knowledge sharing in Q&A websites like Stack Overflow. We show that reference frequencies of URLs follow power law distribution, meaning that web resources that have been referenced frequently will likely to be referenced again. However, there lack of effective methods to manage and reuse already-shared web resources relevant to entities (e.g., APIs or programming concepts) that are mentioned in Q&A discussions. As URL references are done in an ad-hoc manner, large amounts of entity mentions have not been linked to relevant web resources. To enhance management and reuse of alreadyshared web resources in Stack Overflow, we build a knowledge base of official documentation of languages and APIs that have been shared in Stack Overflow, and develop an automatic web resources linking technique to linkify entity mentions to relevant official documentation in the knowledge base. A challenge in automatic web resources linking is that entity mentions often have ambiguity, for example, same programming concepts across different languages, same name APIs in different libraries. To disambiguate the right web resource to link among several URL candidates for an entity mention, our technique examines both the global popularity of the URL candidates for the entity mention and the local context relatedness of the URL candidates with the discussion thread in which the entity is mentioned. We conduct large scale evaluation of the built knowledge base and the performance of our automatic web resource linking technique.","PeriodicalId":114056,"journal":{"name":"2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Enhancing Knowledge Sharing in Stack Overflow via Automatic External Web Resources Linking\",\"authors\":\"Sa Gao, Zhenchang Xing, Yukun Ma, Deheng Ye, Shang-Wei Lin\",\"doi\":\"10.1109/ICECCS.2017.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Referencing URLs of external web resources (e.g., official language references and API documents) is an effective mechanism for knowledge sharing in Q&A websites like Stack Overflow. We show that reference frequencies of URLs follow power law distribution, meaning that web resources that have been referenced frequently will likely to be referenced again. However, there lack of effective methods to manage and reuse already-shared web resources relevant to entities (e.g., APIs or programming concepts) that are mentioned in Q&A discussions. As URL references are done in an ad-hoc manner, large amounts of entity mentions have not been linked to relevant web resources. To enhance management and reuse of alreadyshared web resources in Stack Overflow, we build a knowledge base of official documentation of languages and APIs that have been shared in Stack Overflow, and develop an automatic web resources linking technique to linkify entity mentions to relevant official documentation in the knowledge base. A challenge in automatic web resources linking is that entity mentions often have ambiguity, for example, same programming concepts across different languages, same name APIs in different libraries. To disambiguate the right web resource to link among several URL candidates for an entity mention, our technique examines both the global popularity of the URL candidates for the entity mention and the local context relatedness of the URL candidates with the discussion thread in which the entity is mentioned. We conduct large scale evaluation of the built knowledge base and the performance of our automatic web resource linking technique.\",\"PeriodicalId\":114056,\"journal\":{\"name\":\"2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICECCS.2017.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECCS.2017.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancing Knowledge Sharing in Stack Overflow via Automatic External Web Resources Linking
Referencing URLs of external web resources (e.g., official language references and API documents) is an effective mechanism for knowledge sharing in Q&A websites like Stack Overflow. We show that reference frequencies of URLs follow power law distribution, meaning that web resources that have been referenced frequently will likely to be referenced again. However, there lack of effective methods to manage and reuse already-shared web resources relevant to entities (e.g., APIs or programming concepts) that are mentioned in Q&A discussions. As URL references are done in an ad-hoc manner, large amounts of entity mentions have not been linked to relevant web resources. To enhance management and reuse of alreadyshared web resources in Stack Overflow, we build a knowledge base of official documentation of languages and APIs that have been shared in Stack Overflow, and develop an automatic web resources linking technique to linkify entity mentions to relevant official documentation in the knowledge base. A challenge in automatic web resources linking is that entity mentions often have ambiguity, for example, same programming concepts across different languages, same name APIs in different libraries. To disambiguate the right web resource to link among several URL candidates for an entity mention, our technique examines both the global popularity of the URL candidates for the entity mention and the local context relatedness of the URL candidates with the discussion thread in which the entity is mentioned. We conduct large scale evaluation of the built knowledge base and the performance of our automatic web resource linking technique.