BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fash-ion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA.Comment: 1

Concept-based query expansion for retrieving gene related publications from MEDLINE

<p>Abstract</p> <p>Background</p> <p>Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.</p> <p>Results</p> <p>This paper presents QuExT, a new PubMed-based document retrieval and prioritization tool that, from a given list of genes, searches for the most relevant results from the literature. QuExT follows a concept-oriented query expansion methodology to find documents containing concepts related to the genes in the user input, such as protein and pathway names. The retrieved documents are ranked according to user-definable weights assigned to each concept class. By changing these weights, users can modify the ranking of the results in order to focus on documents dealing with a specific concept. The method's performance was evaluated using data from the 2004 TREC genomics track, producing a mean average precision of 0.425, with an average of 4.8 and 31.3 relevant documents within the top 10 and 100 retrieved abstracts, respectively.</p> <p>Conclusions</p> <p>QuExT implements a concept-based query expansion scheme that leverages gene-related information available on a variety of biological resources. The main advantage of the system is to give the user control over the ranking of the results by means of a simple weighting scheme. Using this approach, researchers can effortlessly explore the literature regarding a group of genes and focus on the different aspects relating to these genes.</p

EsdRank: Connecting Query and Documents through External Semi-Structured Data

ABSTRACT This paper presents EsdRank, a new technique for improving ranking using external semi-structured data such as controlled vocabularies and knowledge bases. EsdRank treats vocabularies, terms and entities from external data, as objects connecting query and documents. Evidence used to link query to objects, and to rank documents are incorporated as features between query-object and object-document correspondingly. A latent listwise learning to rank algorithm, Latent-ListMLE, models the objects as latent space between query and documents, and learns how to handle all evidence in a unified procedure from document relevance judgments. EsdRank is tested in two scenarios: Using a knowledge base for web search, and using a controlled vocabulary for medical search. Experiments on TREC Web Track and OHSUMED data show significant improvements over state-of-the-art baselines

Using community trained recommender models for enhanced information retrieval

Research in Information Retrieval (IR) seeks to develop methods which better assist users in finding information which is relevant to their current information needs. Personalization is a significant focus of research for the development of next generation of IR systems. Commercial search engines are exploring methods to incorporate models of the user’s interests to facilitate personalization in IR to improve retrieval effectiveness. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. This is a significant challenge for IR researchers attempting to improve search effectiveness by exploiting user search behaviour. We propose a solution to this problem based on recommender systems (RSs) in a novel IR model which combines a recommender model with traditional IR methods to improve retrieval results for search tasks, where the IR system has no opportunity to acquire prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search behaviour data from other previous users to build topic category models based on topic interests. When a user enters a query on a topic which is new to this user, but related to a topical search category, the appropriate topic category model is selected and used to predict a ranking which this user may find interesting based on previous search behaviour. The recommender outputs are used in combination with the output of a standard IR system to produce the overall output to the user. In this thesis, the IR and recommender components of this integrated model are investigated