Exploring Query Expansion for Entity Searches in PubMed

Huang, Chung-Chi; Lu, Zhiyong

doi:10.18653/v1/w16-6114

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge graphs provide a more effective way to express, organize, manage, and utilize massive, heterogeneous, and dynamic medical big data in medical information systems, making the systems more intelligent and closer to human cognitive thinking. Currently, medical knowledge graph technology is mainly used in clinical decision support systems [18], medical intelligent semantic search engines [19], medical question and answer systems [20], chronic disease management systems, medical guidance systems, and adverse drug reactions [21]. Medical question and answer systems were an advanced form of medical information retrieval that could provide users with answers in an accurate and brief natural language form.…”

Section: The Application Of Question and Answermentioning

confidence: 99%

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In the existing medical knowledge graphs, there are problems concerning inadequate knowledge discovery strategies and the use of single sources of medical data. Therefore, this paper proposed a research method for multi-data-source medical knowledge graphs based on the data, information, knowledge, and wisdom (DIKW) system to address these issues. Firstly, a reliable data source selection strategy was used to assign priorities to the data sources. Secondly, a two-step data fusion strategy was developed to effectively fuse the processed medical data, which is conducive to improving the quality of medical knowledge graphs. The proposed research method is for the design of a multi-data-source medical knowledge graph based on the DIKW system. The method was used to design a set of DIK three-layer knowledge graph architectures according to the DIKW system in line with the medical knowledge discovery strategy, employing a scientific method for expanding and updating knowledge at each level of the knowledge graph. Finally, question and answer experiments were used to compare the two different ways of constructing knowledge graphs, validating the effectiveness of the two-step data fusion strategy and the DIK three-layer knowledge graph.

show abstract

Section: The Application Of Question and Answermentioning

confidence: 99%

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Liu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Regarding medical ontologies like MeSH or the wider UMLS, we can consider as profitable and enriched source of semantic content, not only for finding papers in specialized literature [16], but also for clinical record retrieval [17,18]. As can be checked from these references, the most common method for integrating medical ontologies in the retrieval process is by query expansion [19]. It involves recognizing entities in the query keywords and adding new concepts according to proximity in meaning or other taxonomic relations like meronymy or holomyny, for example.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Biomedical Term Identification Systems

et al. 2020

View full text Add to dashboard Cite

In this paper a novel architecture to build biomedical term identification systems is presented. The architecture combines several sources of information and knowledge bases to provide practical and exploration-enabled biomedical term identification systems. We have implemented a system to evidence the convenience of the different modules considered in the architecture. Our system includes medical term identification, retrieval of specialized literature and semantic concept browsing from medical ontologies. By applying several Natural Language Processing (NLP) technologies, we have developed a prototype that offers an easy interface for helping to understand biomedical specialized terminology present in Spanish medical texts. The result is a system that performs term identification of medical concepts over any textual document written in Spanish. It is possible to perform a sub-concept selection using the previously identified terms to accomplish a fine-tune retrieval process over resources like SciELO, Google Scholar and MedLine. Moreover, the system generates a conceptual graph which semantically relates all the terms found in the text. In order to evaluate our proposal on medical term identification, we present the results obtained by our system using the MANTRA corpus and compare its performance with the Freeling-Med tool.

show abstract

“…UMLS lexicon) that is provided by the National Library of Medicine (NLM) [22]. This lexicon is one of the richest available sources of medical lexical information and has been employed for the purpose of analyzing medical text [23]. In the context of our work, the UMLS lexicon is used to carry out the following tasks:…”

Section: Introductionmentioning

confidence: 99%

Head Concepts Selection for Verbose Medical Queries Expansion

et al. 2020

View full text Add to dashboard Cite

Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users' query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.

show abstract

Exploring Query Expansion for Entity Searches in PubMed

Cited by 3 publications

References 36 publications

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

Research on Hierarchical Knowledge Graphs of Data, Information, and Knowledge Based on Multiple Data Sources

An Integrated Approach to Biomedical Term Identification Systems

Head Concepts Selection for Verbose Medical Queries Expansion

Contact Info

Product

Resources

About