Hey Haifeng, On top of all the excellent answers provided, I'd also add that the answer to your question depends on what you want to use the similarity scores for. For some insight into what it might mean to make choose one approach over another, see this recent publication: https://dl.acm.org/citation.cfm?id=3213769
At a high level, I'd say that there are three ways you might approach article similarity on Wikipedia: * Reader similarity: two articles are similar if the same people who read one also frequently read the other. Navigation embeddings that implement this definition based on page views were generated last in 2017, so newer articles will not be represented, but here is the dataset [ https://figshare.com/articles/Wikipedia_Vectors/3146878 ] and meta page [ https://meta.wikimedia.org/wiki/Research:Wikipedia_Navigation_Vectors ]. The clickstream dataset [ https://dumps.wikimedia.org/other/clickstream/readme.html ], which is more recent, might be used in a similar way. * Content similarity: two articles are similar if they contain similar content -- i.e. in most cases, similar text. This covers most of the suggestions provided to you in this email chain. Some are simpler but are language-specific unless you make substantial modifications (e.g., ESA, the LDA model described here: https://cs.stanford.edu/people/jure/pubs/wikipedia-www17.pdf) while others are more complicated but work across multiple languages (e.g., recent WSDM paper: https://twitter.com/cervisiarius/status/1115510356976242688). * Link similarity: two articles are similar if they link to similar articles. Generally, this approach involves creating a graph of Wikipedia's link structure and then using an approach such as node2vec to reduce the graph to article embeddings. I know less about the current approaches in this space, but some searching should turn up a variety of approaches -- e.g., Milne and Witten's 2008 approach [ http://www.aaai.org/Papers/Workshops/2008/WS-08-15/WS08-15-005.pdf ], which is implemented in WikiBrain as Morten mentioned.
There are also other, more structured approaches like ORES drafttopic, which predicts which topics (based on WikiProjects) are most likely to apply to a given English Wikipedia article: https://www.mediawiki.org/wiki/Talk:ORES/Draft_topic
On Tue, May 7, 2019 at 9:54 AM fn@imm.dtu.dk wrote:
Dear Haifeng,
Would you not be able to use ordinary information retrieval techniques such as bag-of-words/phrases and tfidf? Explicit semantic analysis (ESA) uses this approach (though its primary focus is word semantic similarity).
There are a few papers for ESA: https://tools.wmflabs.org/scholia/topic/Q5421270
I have also used it in "Open semantic analysis: The case of word level semantics in Danish"
http://www2.compute.dtu.dk/pubdb/views/edoc_download.php/7029/pdf/imm7029.pd...
Finn Årup Nielsen http://people.compute.dtu.dk/faan/
On 04/05/2019 13:47, Haifeng Zhang wrote:
Dear folks,
Is there a way to compute content similarity between two Wikipedia
articles?
For example, I can think of representing each article as a vector of
likelihoods over possible topics.
But, I wonder there are other work people have already explored in the
past.
Thanks,
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