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Semantic similarity measure
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The Human Phenotype Ontology (HPO) is one of the most popular bioinformatics resources. Recently, HPO-based phenotype semantic similarity has been effectively applied to model patient phenotype data. However, the existing tools are revised based on the Gene Ontology (GO)-based term similarity. The design of the models are not optimized for the unique features of HPO. In addition, existing tools only allow HPO terms as input and only provide pure text-based outputs.
9p
vitzuyu2711
29-09-2021
19
1
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The thesis researches and builds an entity search technique based on implicit semantic relations using clustering methods to improve search efficiency. Apply context-aware techniques, build an vertical search engine that applies context-aware in its own knowledge base domain (aviation data). Propose to measure combinatorial similarity in the contextual query suggestion problem to improve the quality of suggestion.
27p
capheviahe27
23-02-2021
17
3
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Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods.
13p
viwyoming2711
16-12-2020
6
1
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The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses.
13p
viwyoming2711
16-12-2020
10
1
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Measuring semantic similarities by combining gene ontology annotations and gene co-function networks
Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms.
14p
vikentucky2711
26-11-2020
13
1
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The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: Biological process, molecular function and cellular component.
14p
vioklahoma2711
19-11-2020
13
2
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In this paper, we present a novel method to measure the semantic similarity between elements in different RDF ontologies. This measure is designed so as to enable extraction of information encoded in RDF element descriptions and to take into account the element relationships with its ancestors and children. We evaluate the proposed measures in the context of matching two RDF ontologies to determine the number of matches between them and then compare with human estimation and the related methods.
9p
quenchua9
20-11-2020
21
3
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Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs.
12p
vicoachella2711
27-10-2020
7
1
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Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants.
8p
vicoachella2711
27-10-2020
3
0
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Biological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete. Recent studies have reported that biases in available GO annotations result in biased estimates of functional similarities of genes, but it is still unclear what the effect of incompleteness itself may be, even in the absence of bias.
15p
vijisoo2711
27-10-2020
12
0
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In this paper, we introduce the indirect association measures of Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC), and compare them to Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and direct co-occurrence vector cosine.
19p
vicolorado2711
23-10-2020
6
0
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Biomedical literature concerns a wide range of concepts, requiring controlled vocabularies to maintain a consistent terminology across different research groups. However, as new concepts are introduced, biomedical literature is prone to ambiguity, specifically in fields that are advancing more rapidly, for example, drug design and development.
12p
vicolorado2711
23-10-2020
12
1
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In this paper, we propose the Semantic-Based Image Retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents.
18p
viconandoyle2711
29-08-2019
15
2
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Enormous amount of available data in relational database (RDB) format creates a demand for automatic transforming them into Web Ontology Language (OWL) ontology to reuse in the Semantic Web. Many approaches have been proposed, however, most of them simply generate output ontology as the same flat structure with the original database and result in redundancy of ontology data.
12p
viconandoyle2711
29-08-2019
11
0
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The measure of sentence similarity is useful in various research fields, such as artificial intelligence, knowledge management, and information retrieval. Several methods have been proposed to measure the sentence similarity based on syntactic and/or semantic knowledge.
10p
vititan2711
13-08-2019
10
0
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Enormous amount of available data in relational database (RDB) format creates a demand for automatic transforming them into Web Ontology Language (OWL) ontology to reuse in the Semantic Web. Many approaches have been proposed, however, most of them simply generate output ontology as the same flat structure with the original database and result in redundancy of ontology data. As an attempt to resolve the redundant problem, we propose a novel approach to generate OWL ontology from relational database while focusing on the similarity measure of duplicate attributes in relational tables.
12p
shiwo_ding7
05-06-2019
13
0
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This approach is based on text snippets and page counts. These two measures are taken from the results of a search engine like Google. To achieve the aim of this paper, lexical patterns are extracted from text snippets and word co-occurrence measures are defined using page counts. The results of these two are combined.
6p
byphasse043256
24-03-2019
17
0
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This paper proposes a method for measuring semantic similarity between words as a new tool for text analysis. The similarity is measured on a semantic network constructed systematically from a subset of the English dictionary, LDOCE (Long-man Dictionary of Contemporary English). Spreading activation on the network can directly compute the similarity between any two words in the Longman Defining Vocabulary, and indirectly the similarity of all the other words in LDOCE.
8p
buncha_1
08-05-2013
51
2
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In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past. ...
9p
bunthai_1
06-05-2013
40
2
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The paper addresses the problem of automatic enrichment of a thesaurus by classifying new words into its classes. The proposed classification method makes use of both the distributional data about a new word and the strength of the semantic relatedness of its target class to other likely candidate classes.
4p
bunthai_1
06-05-2013
47
2
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