An open-source toolkit for mining Wikipedia

By David N. Milne, Ian H. Witten. Artificial Intelligence, 2013

392 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication

View Paper: https://www.semanticscholar.org/paper/9ff9677f294c2d213c23005b57b7b07fe4c35bd7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiawei Han
2. Jianyong Wang
3. Rami Al-Rfou
4. Zihang Dai
5. Mandy Guo
6. Lior Friedman
7. Philip S. Yu
8. Ebrahim Bagheri

In Communication

1. Rami Al-Rfou
2. Zihang Dai
3. Mandy Guo
4. Wei Shen
5. Jiawei Han
6. Ebrahim Bagheri
7. Faezeh Ensan
8. Denny Vrandečić

Citations in the corpus

• Document Retrieval Model Through Semantic Linking (2017, background): “Cornolti et al. [6] provide a benchmarking framework for comparing annotation systems and analyse some of the popular systems such as TagMe [9], Wikipedia-miner [23], and DBpedia Spotlight [20].”
• Wiki-40B: Multilingual Language Model Dataset (2020, methodology): “The available libraries to clean up Wikipedia text (Zesch et al., 2008; Milne and Witten, 2013) do not provide download-able dumps of the results, so we were left with two options: TensorFlow Datasets or Google’s internal cleaned-up version of Wikipedia’s text.”
• Recursive Feature Generation for Knowledge-based Learning (2018, methodology): “To enable linkage between the basic features and the semantic knowledge bases, we use entity linking software (Hoffart, Yosef, Bordino, F¨urstenau, Pinkal, Spaniol, Taneva, Thater, & Weikum, 2011; Milne & Witten, 2013) to transform these words into semantically meaningful entities.”
• Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, methodology): “Additionally, some approaches [58,62,75,76,78,79,91,96] adopt the Wikipedia Link-based Measure (WLM) described in [108,109] to calculate the topical coherence between Wikipedia entities under the assumption that two Wikipedia entities are considered to be semantically related if there are many Wikip”
• "Big Data" : big gaps of knowledge in the field of internet science (2012, background): “Scientists have begun to develop Web services with interfaces to collectors of Big Data sets, e.g., Milne and Witten (2009) for Wikipedia at http://wikipedia-miner.cms.waikato.ac.nz/ and Reips and Garaizar (2011) for Twitter at http://tweetminer.eu.”
• You are what you like! Information leakage through users' Interests (2012, background): “The search returns an article or set of articles that could refer to the given interest.”