scholarly journals Semantic Search Enhanced with Rating Scores

2020 ◽  
Vol 12 (4) ◽  
pp. 67
Author(s):  
Anna Formica ◽  
Elaheh Pourabbas ◽  
Francesco Taglino
Keyword(s):  
Semantic Similarity ◽  
Semantic Search ◽  
Digital Resources ◽  
User Request ◽  
Reference Ontology ◽  
Better Than

This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSime has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as S e m S i m R V .

Enriching semantic search with preference and quality scores

2017 ◽  
Vol 01 (01) ◽  
pp. 1650002
Author(s):  
Michele Missikoff ◽  
Anna Formica ◽  
Elaheh Pourabbas ◽  
Francesco Taglino
Keyword(s):  
Search Space ◽  
Search Method ◽  
Semantic Search ◽  
Conceptual Similarity ◽  
Web Information ◽  
User Request ◽  
Reference Ontology ◽  
Web Information Systems ◽  
Searching Method

This paper proposes an advanced searching method, aimed at improving Web Information Systems by adopting semantic technology solutions. In particular, it first illustrates the main solutions for semantic search and then proposes the semantic search method [Formula: see text] that represents an evolution of the original SemSim method. The latter is based on the annotation of the resources in a given search space by means of Ontology Feature Vectors ([Formula: see text]), built starting from a reference ontology. Analogously, a user request is expressed as a set of keywords (concepts) selected from the reference ontology, that represent the desired characteristics of the searched resources. Then, the searching method consists in extracting the resources having the [Formula: see text] that exhibit the highest conceptual similarity to the user request. The new method, [Formula: see text], improves the above mechanism by enriching the [Formula: see text] with scores. In the user request, a score (High, Medium, Low) is associated with a concept and indicates the preference (i.e., the priority) that the user assigns to the different concepts in searching for resources. In the resource annotation, the score indicates the level of quality of the concept used to characterize the resource. The [Formula: see text] method has been experimented and the results show that it outperforms the SemSim method and, therefore, also the most representative similarity methods proposed in the literature, as already shown in previous works of the authors.

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

scholarly journals DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers

2021 ◽  
Author(s):  
Abdul Wahab ◽  
Rafet Sifa
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Syntactic Structure ◽  
Language Modeling ◽  
Benchmark Dataset ◽  
Fine Tuning ◽  
New Model ◽  
Dependency Tree ◽  
Better Than

<div> <div> <div> <p> </p><div> <div> <div> <p>In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT and has an additional third objective called Parent Prediction (PP) apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both BERT- Base and DIBERT. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis. </p> </div> </div> </div> </div> </div> </div>

scholarly journals SAlign–a structure aware method for global PPI network alignment

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Umair Ayub ◽  
Imran Haider ◽  
Hammad Naveed
Keyword(s):  
Semantic Similarity ◽  
Protein Interaction ◽  
Network Alignment ◽  
Protein Networks ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Alignment Algorithm ◽  
Ppi Network ◽  
Ppi Networks ◽  
Better Than

Abstract Background High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein–protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. Results This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3–63% semantically better than all existing techniques. Furthermore, it also aligns 5–14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce $$\hbox {SAlign}^{\mathrm{mc}}$$ SAlign mc , a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. Conclusion The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

scholarly journals INTEGRATING SEMANTIC SEARCH IN E-LEARNING TECHNOLOGIES: THE ELSE SYSTEM

2020 ◽  
Vol 78 (4) ◽  
pp. 237-248
Author(s):  
Анна Форміка ◽  
Алессія Барбагалло
Keyword(s):  
Semantic Web ◽  
Health Professionals ◽  
Learning Objects ◽  
Learning Preferences ◽  
Learning Technologies ◽  
Semantic Search ◽  
Medical Education Program ◽  
User Request ◽  
E Learning ◽  
Definition Of

The integration of semantic web methodologies and e-learning technologies is a challenge that has attracted a lot of attention for a decade. Given this, the purpose of this paper is the definition of a new e-learning semantic web methodology for the development of courses for health professionals in both distance and residential learning modes. ELSE is an ontology-based system which allows the construction of customized e-learning courses according to the needs and learning preferences of the user. It integrates semantic search methodologies and e-learning technologies. The underlying methodology relies on a reference domain ontology and teaching multimedial interactive modules, referred to as Reusable Learning Objects (RLOs), which are annotated according to the concepts of the ontology. The user can specify his/her training needs by selecting a set of concepts from the ontology, and the SemSim semantic search engine allows the identification of the set of RLOs that satisfy the user request at best, in efficient way. SemSim is a semantic similarity method which has been extensively experimented with and shows a higher correlation with human judgment with respect to the most relevant similarity methods defined in the literature. The set of RLOs is successively reorganized according to the learning preferences of the user. ELSE has been developed within a project of the CME (Continuing Medical Education) program - ECM for Italian participants - whose goal is the introduction of new methodologies and tools to keep updated health professionals and, in particular, medical specialists, in order to ensure effectiveness, safety, and efficiency of the national health service. ELSE has been tested and validated in the domain of osteoporosis, and the overall judgment about the system is very positive, both in terms of usability and effectiveness of customization. The system has been developed in cooperation with the ECM provider SPES S.c.p.A., accredited by the Italian Ministry of Health.

Information Retrieval by Semantic Similarity

2011 ◽  
pp. 647-665 ◽  
Author(s):  
Angelos Hliaoutakis ◽  
Giannis Varelas ◽  
Epimenidis Voutsakis ◽  
Euripides G.M. Petrakis ◽  
Evangelos Milios
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Retrieval Method ◽  
Space Model ◽  
Performance Improvements ◽  
Reference Ontology ◽  
Information Retrieval Methods ◽  
Similarity Method ◽  
The Web

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-theart semantic similarity retrieval methods utilizing ontologies.

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