Formalization of graph search algorithms and its applications

BACKGROUND Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. RESULTS Our experiments show promising results with an F1 of 69% on the test dataset. CONCLUSIONS To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

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Facilitating Large-Scale Graph Search Algorithms with Lock-Free Concurrent Pairing Heaps

10.14293/s2199-1006.1.sor-.ppn6ong.v1 ◽

2019 ◽

Author(s):

Jeremy Mayeres ◽

Charles Newton ◽

Helena Arpudaraj

Keyword(s):

Data Structures ◽

Shortest Path ◽

Large Scale ◽

Search Algorithm ◽

Search Algorithms ◽

Graph Search ◽

Dijkstra’S Algorithm ◽

Single Source ◽

Dijkstra's Algorithm ◽

Graph Search Algorithms

This paper introduces a lock-free version of a Pairing heap. Dijkstra's algorithm is a search algorithm to solve the single-source shortest path problem. The performance of Dijkstra's algorithm improves when threads can also perform work concurrently (in particular, when decreaseKey calls occur concurrently.) However, current implementations of decreaseKey on popular backing data structures such as Pairing heaps and Fibonacci heaps severely limit concurrency. Lock-free techniques can improve the concurrency of search structures such as heaps. In this paper we introduce decreaseKey and insert operators for Pairing heaps that provide lock-free guarantees while still running in constant time.

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