Searching an Appropriate Journal for your Paper - an Approach Inspired by Expert Search and Data Fusion

On an abstract level, one is often confronted with some type of classification problem where we have one example instance or a textual query and we are looking for the class most appropriate for this instance or query. More concretely, we consider journals as classes and the papers published in certain journals as constituting and describing the respective class. In this scenario two information needs are conceivable: (1) We know one paper and we are looking for all journals which could potentially contain similar work. (2) We want to write a paper, have a first working title, and are looking for journals which could be potential targets for a submission of that paper. In this work, we transfer methods used in expert search and data fusion to find appropriate journals: Using a flat, title based search query for articles we examine voting models used in expertise retrieval with its different data fusion techniques to find and rank journals associated with the matching articles that potentially contain most suitable other articles. To evaluate the ranking of found journals, we remove several test articles from the applied collection and utilize them as request items with their titles. We assume that—on average—the journals where these test articles have been published should be among the top ranked journals to provide a suitable result. This fully automated evaluation provides the opportunity to execute a huge number of requests against the collection of articles and to evaluate the different voting techniques transferred from expert search.