Stochastic reranking of biomedical search results based on extracted entities

Fafalios, Pavlos; Tzitzikas, Yannis

doi:10.1002/asi.23877

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Future research may exploit it to further improve the efficiency of the proposed framework. In [50], a bipartite graph is generated between top retrieved documents and entities extracted from the documents to measure entity importance of the candidate entities, and those importance scores were used to re-rank the initial search results. Although we focus more on query extension rather than on re-ranking, this graph-based approach can still be adapted to extract candidate terms from the knowledge structure additionally by measuring the term importance as well as co-occurrences of the terms.…”

Section: Discussionmentioning

confidence: 99%

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

Han

Shim

2018

Journal of Healthcare Engineering

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Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.

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Section: Discussionmentioning

confidence: 99%

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

Han

Shim

2018

Journal of Healthcare Engineering

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“…Then, we propose a biased (personalized-like) PageRank algorithm for analyzing the graph and scoring its nodes. Similar modeling and scoring methods have been applied for the problems of results re-ranking [13] and enrichment [12] in information retrieval.…”

Section: Stochastic Modelingmentioning

confidence: 99%

Ranking Archived Documents for Structured Queries on Semantic Layers

Fafalios

Kasturia

Nejdl

2018

Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

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Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitations.

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“…Advancing Information Retrieval. Recent works have shown that the exploitation of entities extracted from search results can enhance the effectiveness of keywordbased search systems in different contexts, like in biomedical [12] and academic [50] search. Consequently, a semantic layer built on top of a collection of archived documents can also serve a search system operating over the same collection.…”

Section: Other Exploitation Scenariosmentioning

confidence: 99%

Building and Querying Semantic Layers for Web Archives (Extended Version)

Fafalios,

Holzmann,

Kasturia

et al. 2018

Preprint

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Web archiving is the process of collecting portions of the Web to ensure that the information is preserved for future exploitation. However, despite the increasing number of web archives worldwide, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into a usable and useful information source. In this paper, we focus on this problem and propose an RDF/S model and a distributed framework for building semantic profiles ("layers") that describe semantic information about the contents of web archives. A semantic layer allows describing metadata information about the archived documents, annotating them with useful semantic information (like entities, concepts and events), and publishing all this data on the Web as Linked Data. Such structured repositories offer advanced query and integration capabilities, and make web archives directly exploitable by other systems and tools. To demonstrate their query capabilities, we build and query semantic layers for three different types of web archives. An experimental evaluation showed that a semantic layer can answer information needs that existing keyword-based systems are not able to sufficiently satisfy.

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Stochastic reranking of biomedical search results based on extracted entities

Cited by 7 publications

References 32 publications

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

Ranking Archived Documents for Structured Queries on Semantic Layers

Building and Querying Semantic Layers for Web Archives (Extended Version)

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