Semantic Similarity Methods Research Articles

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Extraction of object-action and object-state associations from Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.

A parametric similarity method: Comparative experiments based on semantically annotated large datasets

We present the parametric method SemSimp aimed at measuring semantic similarity of digital resources. SemSimp is based on the notion of information content, and it leverages a reference ontology and taxonomic reasoning, encompassing different approaches for weighting the concepts of the ontology. In particular, weights can be computed by considering either the available digital resources or the structure of the reference ontology of a given domain. SemSimp is assessed against six representative semantic similarity methods for comparing sets of concepts proposed in the literature, by carrying out an experimentation that includes both a statistical analysis and an expert judgment evaluation. To the purpose of achieving a reliable assessment, we used a real-world large dataset based on the Digital Library of the Association for Computing Machinery (ACM), and a reference ontology derived from the ACM Computing Classification System (ACM-CCS). For each method, we considered two indicators. The first concerns the degree of confidence to identify the similarity among the papers belonging to some special issues selected from the ACM Transactions on Information Systems journal, the second the Pearson correlation with human judgment. The results reveal that one of the configurations of SemSimp outperforms the other assessed methods. An additional experiment performed in the domain of physics shows that, in general, SemSimp provides better results than the other similarity methods.

Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ‘is-a’ and ‘part-whole’ relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia’s semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks.

Spam email classification and sentiment analysis based on semantic similarity methods

Spam email classification and sentiment analysis based on semantic similarity methods

Spam email classification and sentiment analysis based on semantic similarity methods

Spam email classification and sentiment analysis based on semantic similarity methods

RastrOS Project: Natural Language Processing contributions to the development of an eye-tracking corpus with predictability norms for Brazilian Portuguese.

This article presents RastrOS, a new eye-tracking corpus of eye movement data from university students during silent reading of paragraphs of texts in Brazilian Portuguese (BP). The article shows the potential of the corpus for natural language processing (NLP) using it to evaluate the sentence complexity prediction task in BP and it also focuses on the description of NLP resources and methods developed to create the corpus. Specifically, we present: (i) the method used to select the corpus paragraphs from large corpora, using linguistic metrics and clustering algorithms; (ii) the platform for collecting the Cloze test, which is also responsible for creating the project datasets, and (iii) the hybrid semantic similarity method, based on word embedding models and contextualised word representations, used to generate semantic predictability norms. RastrOS can be downloaded from the open science framework repository with the computational infrastructure mentioned above. Datasets with predictability norms of 393 participants and eye-tracking data of 37 participants are available in the OSF repository for this work (https://osf.io/9jxg3/).

NATURAL SEMANTIC INFORMATION-BASED EYE TRACKING USING IMPROVED NEEDLEMAN–WUNSCH AND SUBSMATCH METHODS

This paper proposed an improved natural semantic information-based eye-tracking method combing Needleman–Wunsch and SubsMatch technologies in psychological assessments. The natural semantic information of the self-assessment scale in psychological measurement was combined with the participants’ eye-tracking data, and a time-space similarity method was proposed to calculate the eye-tracking differences between different participants in the questionnaire response and carry out visual pattern recognition to achieve the screening of the target population in psychological measurement. The proposed method was evaluated by screening a sample at high risk of depression. The comparative results showed that the average screening accuracy of the sample under the stimulation of a single topic was 80.13% and increased to 97.37% after a dimensionality reduction. This paper verified the objectivity and effectiveness of the semantic information-based eye-tracking time-space similarity method and proved it to be a promising objective tool to assist psychological measurement.

Evaluating semantic similarity methods for comparison of text-derived phenotype profiles

BackgroundSemantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance ‘patient-like me’ analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or better methods in the area.MethodsWe develop a platform for reproducible benchmarking and comparison of experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from all text narrative associated with admissions in the medical information mart for intensive care (MIMIC-III).Results300 semantic similarity configurations were evaluated, as well as one embedding-based approach. On average, measures that did not make use of an external information content measure performed slightly better, however the best-performing configurations when measured by area under receiver operating characteristic curve and Top Ten Accuracy used term-specificity and annotation-frequency measures.ConclusionWe identified and interpreted the performance of a large number of semantic similarity configurations for the task of classifying diagnosis from text-derived phenotype profiles in one setting. We also provided a basis for further research on other settings and related tasks in the area.

A modern semantic similarity method using multiple resources for enhancing influenza detection

With the widespread of the data resources over the World Wide Web (WWW), there is an overlapping between these resources that contributes to helping researchers in discovering more information and facts. However, extracting information and data and then, calculating the semantic similarity between them is a non-trivial task as such resources have varying ways to describe the information. Thus, such a problem can be overcome by designing a new semantic similarity method which takes into account different factors and not exclusive on the syntactical description of the data.The paper describes a new semantic similarity method which exploits different factors to calculate the semantic similarity between different resources. The factors are the description of the node and the surrounded relations (i.e. ascending and descending) from multiple ontologies. This will contribute to calculating semantic similarity based on many perspectives and will help to strengthen the similarity relations between different resources to discover new semantic similarities between influenza symptoms. This may lead to control the expansion of the influenza outbreak. The method has been applied over a set of influenza ontologies to find new cases of similarity that may not be explicit in the original resources. An off-line evaluation method has been also conducted on a set of ontologies and compared with the baseline and specialist methods where it exceeded both methods and satisfied more accurate similarity scores.

WEB PAGE RECOMMENDATION SYSTEM BY INTEGRATING ONTOLOGY AND STEMMING ALGORITHM

In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.

Predicting metabolic pathway membership with deep neural networks by integrating sequential and ontology information.

BackgroundInference of protein’s membership in metabolic pathways has become an important task in functional annotation of protein. The membership information can provide valuable context to the basic functional annotation and also aid reconstruction of incomplete pathways. Previous works have shown success of inference by using various similarity measures of gene ontology.ResultsIn this work, we set out to explore integrating ontology and sequential information to further improve the accuracy. Specifically, we developed a neural network model with an architecture tailored to facilitate the integration of features from different sources. Furthermore, we built models that are able to perform predictions from pathway-centric or protein-centric perspectives. We tested the classifiers using 5-fold cross validation for all metabolic pathways reported in KEGG database.ConclusionsThe testing results demonstrate that by integrating ontology and sequential information with a tailored architecture our deep neural network method outperforms the existing methods significantly in the pathway-centric mode, and in the protein-centric mode, our method either outperforms or performs comparably with a suite of existing GO term based semantic similarity methods.

A method of core concept extraction based on semantic‐weight ranking

SummaryIn order to improve the efficiency of domain ontology concept extraction, this paper proposes a method of semantic‐weight ranking to extract core concepts. Firstly, we make segmentation processing for corpus texts and obtain the candidate concept set, and then analyze the correlation between the candidate concepts through the method of semantic similarity to form a mesh graph structure. We secondly calculate the semantic‐weight of the concept according to the generated mesh graph. Ultimately, we realize the core concept extraction via ranking different semantic‐weight. At the same time, we give the fuzzy relationship representation of the core concepts through the statistical characteristics of the core concepts' membership. On the foundation of the extraction method, we carried out the experiments with the financial text and had achieved an accuracy rate of 81.8%, which could effectively extract the core concepts and play a positive role in the content construction, knowledge sharing, and knowledge reuse.

Enhanced customer requirement classification for product design using big data and improved Kano model

The classification of customer requirements (CRs) has a significant impact on the solution of product design. Existing CRs classification methods such as the Kano model and IPA model are time-consuming and inaccurate. This paper proposes a CRs classification method for product design using big data of online customer reviews of products to classify CRs accurately and efficiently. Comments of customer reviews are matched to CRs using a hierarchical semantic similarity method. Customer satisfaction degrees are defined based on emotional levels of adjectives and adverbs of customer comments using word vectors. The function implementation degree of each product is determined by specifications crawled from online products. Fitting curves are formed by defined customer satisfaction and function implementation of CRs using polynomial modeling and least square methods. Based on the slope of the fitted curves, CRs are classified to provide the minimum and maximum function implementations of CRs in each CR group to guide a product design process. The proposed method is applied in a case study of defining CRs classifications for design of upper limb rehabilitation devices. For verifying the proposed method, CRs defined by the existing methods are compared with CRs from the proposed method in design of an upper limb rehabilitation device.

Interpretable semantic textual similarity of sentences using alignment of chunks with classification and regression

The proposed work is focused on establishing an interpretable Semantic Textual Similarity (iSTS) method for a pair of sentences, which can clarify why two sentences are completely or partially similar or have some variations. This proposed interpretable approach is a pipeline of five modules that begins with the pre-processing and chunking of text. Further chunks of two sentences are aligned using a one–to–multi (1:M) chunk aligner. Thereafter, support vector, Gaussian Naive Bayes and k–Nearest Neighbours classifiers are then used to create a multiclass classification algorithm, and different class labels are used to define an alignment type. At last, a multivariate regression algorithm is developed to find the semantic equivalence of an alignment with a score (that ranges from 0 to 5). The efficiency of the proposed method is verified on three different datasets and also compared to other state–of–the–art interpretable STS (iSTS) methods. The evaluated results show that the proposed method performs better than other iSTS methods. Most importantly, the modules of the proposed iSTS method are used to develop a Textual Entailment (TE) method. It is found that, when we combined chunk level, alignment, and sentence level features the entailment results significantly improves.

Evolution of Semantic Similarity—A Survey

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

MENDETEKSI REDUNDANSI PASANGAN KALIMAT DALAM DOKUMEN SPESIFIKASI KEBUTUHAN PERANGKAT LUNAK DENGAN PENDEKATAN KEMIRIPAN SEMANTIK

Software Requirements Specification Document must be complete, unambiguous and correct because an incorrect Software Requirements Specification Document can lead to failure in software development. Software Requirements Specification document must contain non-redundant sentences. Meyer's Seven Sins describes several errors in Software Requirements Specification Document, one of which is redundancy which is a variant of noise. Redundancy is the repetition of the same information, but using different terms or phrases, thus giving the impression that new information is available. This study analyzes the performance of the framework and tools for detecting redundancy in sentence pairs in the software requirements specification document using the WordNet-based semantic similarity method. This study tries to develop the framework and tools that have been proposed in previous studies, namely by detecting redundancy in sentence pairs without fact extraction. The proposed approach and evaluation process uses Kappa values ??to determine whether the performance of the framework and assistive tools can be used to detect redundancy in sentence pairs properly.
 The method using literature study, data collection, problem analysis, making a framework, making auxiliary tools, and testing. The test uses the Kappa value with two scenarios, from the results of testing the framework and assistive tools can be used to detect redundancy in the Software Requirements Specification Document with a Kappa value of 0.777 with the interpretation of the kappa value is a substantial agreement at a threshold of 0.73

A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Exploiting non-taxonomic relations for measuring semantic similarity and relatedness in WordNet

Various applications in computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. To our knowledge, research to date rely solely on the taxonomic relation “ISA” to evaluate semantic similarity and relatedness between terms. This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph, to enhance existing semantic similarity and relatedness measures. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure. To demonstrate the benefits of exploiting non-taxonomic relations in a knowledge graph, we used three strategies to deploy non-taxonomic relations at different granularity levels. We conduct experiments on four well-known gold standard datasets. The results of our proposed method demonstrate an improvement over the benchmark semantic similarity methods, including the state-of-the-art knowledge graph embedding techniques, that ranged from 3.8%–23.8%, 1.3%–18.3%, 31.8%–117.2%, and 19.1%–111.1%, on all gold standard datasets MC, RG, WordSim, and Mturk, respectively. These results demonstrate the robustness and scalability of the proposed semantic similarity and relatedness measure, significantly improving existing similarity measures.