Virtual Citation Proximity (VCP): Calculating Co-Citation-Proximity-Based Document Relatedness for Uncited Documents with Machine Learning (preprint)
We published a proposal on ResearchGate (DOI: 10.13140/RG.2.2.18759.39842) for a novel approach to the calculate relatedness of research articles and other document types. The relatedness of research articles, patents, legal documents, web pages, and other documents is often calculated with citation or hyperlink based approaches such as citation proximity analysis (CPA). In contrast to text-based document similarity, citation-based relatedness covers a broader range of relatedness. However, citation-based approaches suffer from the many documents that receive little or no citations, and for which document relatedness hence cannot be calculated. I propose to calculate a machine-learned ‘virtual citation proximity’ (or ‘virtual hyperlink proximity’) that could be calculated for all documents for which textual information (title, abstract …) and metadata (authors, journal name …) is available. The input to the machine learning algorithm would be a large corpus of documents, for which textual information, metadata and citation proximity is available. The citation proximity would serve as ground truth, and the machine-learning algorithm would infer, which textual features correspond to a high proximity of co-citations. After the training phase, the machine-learning algorithm could calculate a virtual citation proximity even for uncited documents. This virtual citation proximity would express in what proximity two documents would likely be cited, if they were cited. The virtual citation proximity then could be used in the same way as “real” citation proximity to calculate document relatedness and would potentially cover a wider range of relatedness than text-based document relatedness.
Retrieving a list of ‘related documents’ – e.g. web pages, patents, or research articles – for a given source document is a common feature of many applications, including recommender systems and search engines (Figure 1). Document relatedness is typically calculated based on documents’ text (title, abstract, full-text) and metadata (authors, journal …), or based on citations/hyperlinks [1–10]. The intuition behind text-based relatedness measures is that two documents are more highly related the more terms they share (in place of terms, concepts, topics, n-grams, embeddings, etc. may also be used [11–17]). The intuition of citation-based relatedness is that authors cite documents because they consider them to be related to the manuscript they are currently writing. Consequently, two documents that are (co-) cited are both related to the citing document and to each other [18–24].
While text-based relatedness is about the similarity of documents, i.e. the proportion of terms they have in common, citation-based relatedness is broader. Two documents can be co-cited, and hence related, for many reasons [25–29]. For example, the two documents may use the same algorithm (to solve the same or different problems); the two documents may be written by the same author; or the two documents may be co-cited for less predictable reasons, for example, if both are examples of well-written academic articles and the citing author is writing a book on academic writing. Today’s text-based methods can hardly calculate such types of semantic relatedness.
Figure 1: Google Scholar’s “Related article” feature
Figure 2: Citation-Proximity Analysis
One particularly effective citation-based approach is citation proximity analysis (CPA) (Figure 2) [30–36]. Its intuition is that the closer the proximity in which two documents are cited in a text, the more highly related the two documents are. For instance, if two documents A and B are cited within the same sentence, they are more highly related than documents A and C that are cited in different paragraphs (Figure 2). Citation-proximity analysis has successfully been used to calculate relatedness of research articles [30–36], as well as the relatedness of Wikipedia articles , , web pages , and authors .
2 Research Problem
Although citation-proximity may be very effective, it suffers from the same problem as other citation-based approaches: To calculate relatedness, documents need to be (frequently) co-cited. However, most research articles are never cited, and it usually takes a year or more before a document receives its first citation , . Consequently, document relatedness based on citation-proximity can only be calculated for few documents in a corpus and tends to work best on older documents.
3 Research Goal
The research goal is to develop a citation-proximity-alike relatedness method that works for less cited and uncited documents. The new method should reflect the variety of relatedness that may be expressed through co-citation proximity. This means the relatedness should not simply be based on the proportion of terms that two documents share and hence only express the degree of similarity.
To achieve the goal, I propose to calculate a ‘virtual citation proximity’ that is machine learned. The input to the machine learning algorithm would be a large document corpus including the documents’ text (title, abstract, full-text, references/citations) and metadata (authors, journal …), and their citation proximities. Based on the co-citation proximity as ground truth and the textual features and metadata, the machine-learning algorithm will infer what features make a co-cited document pair related. Subsequently, the algorithm calculates a virtual citation proximity based only on documents’ textual features and metadata. This virtual citation proximity would express in what proximity two documents would likely be cited, if they were cited. Once the machine learning algorithm is trained on a sufficiently large corpus, it could calculate the virtual proximity for any document pair for which textual information and metadata is available. Once the virtual citation proximity is calculated, it could be used in the same way as the normal citation-proximity to calculate document relatedness (e.g. calculate the citation-proximity index, CPI ).
Although virtual citation proximity is based on textual features and metadata, I hypothesise that it will produce similar results as real citation-proximity, since the machine learning model is based on real citation proximity as ground truth. With the recent advances in machine learning, particularly deep learning, I hypothesise that a (deep) machine-learning algorithm will be able to detect hidden layers in the text. These will allow determining what makes two documents related, more reliable than the typical assumption in text-based approaches that two documents are related when they share the same terms.
Virtual citation proximity will combine the best of both worlds, i.e. it can be calculated for every document (like today’s text-based methods), yet provide a high variety of relatedness and be highly effectiveness (like today’s citation-based approaches). Hence, virtual citation proximity has the potential to advance significantly related-document calculations for search engines and recommender systems. Related-document calculations will not only improve for academic documents but potentially for legal documents, patents, medical documents and web pages, too. In the latter case, the method should be called ‘Virtual Hyperlink Proximity’ or ‘Virtual Link Proximity’.
The method that is closest to using citation-proximity as ground truth is using expert judgements like the biomedical classification MeSH [43–45], the ACM classification , DMOZ , or ACL . The MeSH classification represents the major fields in the biomedical domain and was created by medical experts (Figure 3). New biomedical publications are often classified with MeSH, i.e. they are assigned to one of the MeSH categories, and two documents in the same category are considered to be related. Machine learning algorithms can infer from the existing documents in a category, which textual features make a document likely to belong to a certain category. New documents can then automatically be classified.
Figure 3: Excerpt of MeSH
There are several disadvantages to using expert classifications like MeSH. First, they are one-dimensional, i.e. they provide only one type of relatedness (typically, the overall topic a research article is about). However, there may be many other dimensions of relatedness (e.g. a shared algorithm or shared methodology applied in different domains). Second, most classification schemes allow documents to be in one or two categories only. Especially with today’s increasingly interdisciplinary work, this is often not enough to adequately find all related documents. Third, classification schemes typically have a limited number of categories (a few dozen or hundreds). This means, every category contains thousands of documents that might be somewhat related but only at a broad level. Fourth, the classifications are static, i.e. articles are classified at the time of publication. If a classification scheme is changed, the papers usually cannot be updated. Finally, for many domains, expert classifications do not exist. Hence document relatedness in these domains is difficult to learn.
With citation proximity as ground truth, the mentioned problems could be overcome. (Virtual) citation proximity (1) covers many types of relatedness; (2) allow documents to be in unlimited numbers of co-citation clusters; (3) has no limitations for the number of clusters; (4) is dynamic; and (5) can be learned for any domain that uses citations.
5 Proposed Methodology
To achieve the research goal, a number of tasks need to be undertaken.
- A thorough literature review on citation-proximity analysis, and other methods to calculate document relatedness, both text-based and citation-based. This includes a review of currently used ground-truth such as MeSH but also related fields such as social-tagging , folksonomies , and query log analysis , which are sometimes used to train machine learning algorithms.
- Identification of promising machine-learning algorithms that could be capable of learning and calculating virtual citation proximity. This includes both ‘normal’ machine learning algorithms as well as deep learning algorithms.
- The compilation of a large dataset containing co-citation proximity information and text and metadata of the co-cited documents. The dataset ideally contains tens of millions of articles, and hundreds of millions of citations from different disciplines (e.g. biomedical, computer, and social sciences). Depending on the eventual project scope, the corpus could focus on research articles, legal documents (laws, rulings, …), patents, web pages, or massive content repositories such as Wikipedia.
- Evaluation and fine-tuning the different machine learning algorithms. The evaluation should be performed in two ways. First, using an offline dataset. This means, a part of the previously created dataset will not be used for training the machine learning algorithms, but to evaluate the effectiveness. Second, the algorithms could be evaluated in a live search engine or recommender system (for instance in DLib ).
It could further be interesting to use machine learning to understand why documents are cited in close proximity and incorporate this knowledge into the relatedness-calculation process. Considering citation context [53–55], i.e. the sentences surrounding a citation, might further improve the calculation of virtual citation proximity. It might also be interesting to take bias and motivation to create citations into account , , , e.g. citations that were done for illegitimate reasons should be removed from the training corpus. Finally, the concept of virtual-citation proximity might also be used to improve related applications such as co-authorship analysis  and identifying related authors.
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 In the current proposal, I focus on the relatedness of research articles based on citations. However, the work proposed here could easily be extended or shifted to calculating relatedness of patents, legal documents, and web pages based on citations or hyperlinks.