Hybrid Semantic Similarity Research Articles

Retraction notice to “A Hybrid Semantic Similarity Measurement for Geospatial Entities” [Microprocessors and Microsystems 80 (2021) 103526]

Retraction notice to “A Hybrid Semantic Similarity Measurement for Geospatial Entities” [Microprocessors and Microsystems 80 (2021) 103526]

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/).

CAFOB: Context-aware fuzzy-ontology-based tourism recommendation system

The ever-increasing volume of information available on tourist attractions in cyberspace has made the tourist decision-making process a crucial task. Therefore, tourism recommendation systems can significantly benefit tourists in terms of comfort and satisfaction. In this paper, a context-aware fuzzy-ontology-based tourism recommendation system is proposed. In the proposed system, we have two new propositions that can be individually used in other tourism recommendation systems: a fuzzy-weighted ontology and a new sentiment/emotion score scheme. In CAFOB, the ontology-based scores of a user’s reviews are then multiplicatively modulated by sentiment/emotion scores to generate the total scores of the reviews’ ontology words, representing the user preferences. Then, the nearby-open-3+-bubbled touristic attractions’ characteristics are extracted based on their past visitors’ reviews. Finally, a recommendation list is produced using a maximum hybrid semantic similarity between the user preferences and attractions’ characteristics. The employment of contextual information, including weather, location, and time makes CAFOB context-aware and improves the accuracy and quality of the recommendations. The F-measure, NDCG, and MRR results show the outperformance of CAFOB against the state-of-the-art tourism recommendation systems for all relevant Top N recommendation options and different geographical spans of interest.

Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures

Because of the massive amount of textual information accessible today, automated extraction text summarization is one of the most extensively used ways to organize the information. The summarization mechanisms help to extract the important topics of data from a given set of documents. Extractive summarization is one method for providing a representative summary of a text by choosing the most pertinent sentences from the original text. Extractive multi-document text summarization systems' primary goal is to decrease the quantity of textual information in a document collection by concentrating on the most crucial subjects and removing irrelevant material. In the previous research, there are several methods such as term-weighting schemes and similarity metrics used for constructing an automated summary system. There are few studies that look at the performance of combining various Semantic similarity and word weighting techniques in automatic text summarization. We evaluated numerous semantic similarity metrics in extractive multi-document text summarization in this research. In the extractive multi-document text summarization discussed in this research, we looked at numerous semantic similarity metrics. ROUGE metrics have been used to evaluate the model performance in experiments using DUC datasets. Even more, the combination formed by different semantic similarity measures obtained the highest results in comparison with the other models.

RETRACTED: A Hybrid Semantic Similarity Measurement for Geospatial Entities

Semantic similarity plays a critical role in geospatial cognition, semantic interoperability, information integration and information retrieval and reasoning in geographic information science. Although some computational models for semantic similarity measurement have been proposed in literature, these models overlook spatial distribution characteristics or geometric features and pay little attention to the types and ranges of properties. This paper presents a novel semantic similarity measurement approach that employs a richer structured semantic description containing properties as well as relations. This approach captures the geo-semantic similarity more accurately and effectively by evaluating the contributions for ontological properties, measuring the effect of the relative position in the ontology hierarchy structure and computing the geometric feature similarity for geospatial entities. A water body ontology is used to illustrate the approach in a case study. A human-subject experiment was carried out and the experiment results shows that this proposed approach has a good performance based on the high correlation between its computed similarity results and human's judgements of similarity.

Mining information from sentences through Semantic Web data and Information Extraction tasks

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

Assessing semantic similarity between concepts: A weighted‐feature‐based approach

SummaryTraditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and TFIDF, we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.

An approach to merge domain ontologies using granular computing

Granular computing is the emerging technique which performs data processing through making multiple levels of descriptions. Each level of description is expressed through granules or chunks of data also defined as information granules. The granule, the granule structure, and the granule layer are the heart of granular computing. Ontologies are vital information archives. On all disciplines of science and technology, ontologies are developed according to the requirements. Hence, the huge number of ontologies is available in the concerned domain which creates information duplication and storage problem. Merging of existing ontologies overcomes these issues. There are many merging approaches available. The existing approaches do not use granular computing for merging the ontologies. The proposed approach employs granular computing for merging the existing domain ontologies, thereby unifying multiple domain ontologies into a single representative domain ontology. For that, this research work proposes the following four granular computing processes, namely, association, isolation, purification, and reduction which can be applied over a group of similar nodes in the ontologies thereby unifying them. The proposed method achieves the ontology merging by performing two phases, namely similarity calculation phase and granular computing phase. The similarity calculation phase identifies the inter-label similarity between the labels of ontologies and computes the relevant group of nodes. Subsequently, granular computing applies association, isolation, purification, and reduction over a group of relevant nodes. The proposed approach is validated using the film industry and transportation domain ontologies and compared against its counterpart hybrid semantic similarity measure (HSSM). The results concluded that the proposed approach outperforms HSSM.

Hyb-Tvx: A Hybrid Semantic Similarity Feature-Based Measurement for Multiple Ontologies

Hyb-Tvx: A Hybrid Semantic Similarity Feature-Based Measurement for Multiple Ontologies

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

PurposeThe purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure.Design/methodology/approachThe HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part.FindingsThe experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain.Practical implicationsIn this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers.Originality/valueThe HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Background: Gene ontology (GO) is a well-structured knowledge of biological terms that describes roles of genes and their products in a standardized and organized controlled vocabulary format. Over the last decade, many measures are developed to exploit GO advantages to determine semantic similarities between biological entities. Using GO ontologies, there are some constraints that existing GO-based semantic similarity measures try to address them. For instance, (1) edges in a GO graph, do not indicate uniform distances and also have different densities, and (2) ignoring term levels in an ontology makes “shallow annotation” drawback, i.e., two terms with a certain distance near the root of GO graph have equal semantic similarity with two terms with the same distance but far from the root. Methods: Here, we present wAIC, a two-stage hybrid semantic similarity measure using weighted aggregation of information contents. In wAIC, the impact of each common ancestor on semantic similarity value is determined according to the location of the ancestor in the ontology graph. wAIC, also, filters (from annotating term set) terms that are in upper levels of the graph ontology to reduce shallow annotation constraints. Results: Experimental results confirm that the proposed measure is more consistent with major related constraints, such that, wAIC semantic similarity values have more correlation with both sequence similarity values and gene expression based similarity values than state-of-the-art semantic similarity measures. Conclusions: WAIC show using a weighted aggregation of common ancestors is completely consistent with the human perception and can improve accuracy of gene similarity measurement.

Matching Mechanism of Web Service Based on Hybrid Semantic Similarity Computation

Fuzzy search algorithm with hybrid semantic similarity is proposed aiming at serious performance issues in information retrieval, which results from fuzzy search condition, in search engine land. First of all, new conceptual extraction method is proposed according to similarity calculation concept; after that, TF-IQF is adopted to divide the link into tabs, and the set comprised of these tabs is used for indicating the query; ultimately, binary graph is constructed to identify related queries and is employed to compute query similarity. Experimental results show that proposed algorithm has achieved better recall ratio, retrieval precision and F-measure compared with clicking document, related queries and reverse query algorithm.

Towards a hybrid semantic similarity measure to set the conceptual relatedness in a hierarchy

Assessment of semantic similarity between concepts is of great importance in many applications dealing with textual data, such as natural language processing, knowledge acquisition, document semantic annotation and information retrieval systems. Moreover, to extract similar concepts from multiple ontologies, there is a real need to develop a conceptual similarity measure, the intention of finding semantic similarity in a given hierarchy, is to enhance the integration and retrieval of heterogeneous information resources in a more meaningful and accurate way. In this paper we present a hybrid approach for measuring the semantic similarity, in an attempt to address a new issue that focuses on the sensitivity of the similarity measure between concepts in a hierarchy. Based on the notions of both distance and the information content, the measure is expected to provide more consistent and accurate measures.

An Optimization for Hybrid Semantic Similarity Computation

Semantic similarity computation is of great importance in many applications such as natural language processing, knowledge acquisition and information retrieval. In recent years, many concept similarity measures have been developed for ontology and lexical taxonomy. Generally speaking, ontology concepts semantic similarity computation is tedious and time-consuming. This paper puts forward an optimization algorithm to simplify semantic similarity computation. The optimization algorithm utilizes hierarchical relationship between concepts to simplify similarity computation process. Simulation experiments showed the optimization algorithm could make similarity computation simple and convenient, and similarity computation speed was improved by one time. The more complexity an ontology structure, and the bigger the maximum depth of ontology, the more significantly the performance improved.

Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure.

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is fundamentally related to cognitive science. We approach the problem with a new angle by perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data. Extensive experiments show that the proposed semantic similarity measure outperforms four strong baselines with more than 47 % gain in clustering accuracy. In addition, we discovered in the human quiz test that the model accuracy indeed shows a strong correlation with the pairwise quiz similarity.

Reconciliation model of heterogeneous information in decisional information systems

In this paper, we propose a reconciliation and cohabitation model of heterogeneous information in Decisional Information Systems DIS. Among this heterogeneous information are data that represent the analysis basis in DIS systems and metadata that represent a very important role to interpret the results and lead to a good analysis. In the current generation of DIS systems, we can find knowledge represented by ontologies, which is used to enjoy the semantic side offered by these systems. Our reconciliation model focuses on semantic mediation approach, based on the hybrid ontology architecture and semantic similarity measures to integrate each information type. Our modelling is based on a common model that represents a global view of heterogeneous information, allowing to find the mapping between different information types, to respond effectively to a posed query. To validate our work, we have applied our model on real business information, containing data, metadata and knowledge.

Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering

This paper proposes a fuzzy control genetic algorithm (GA) in conjunction with a novel hybrid semantic similarity measure for document clustering. Since the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms being ignored, we use semantic similarity measures to solve this problem. In general, the semantic similarity measures can be extensively categorized into two kinds: thesaurus-based methods and corpus-based methods. However, in practice the corpus-based method is rather complicated to tackle. We propose and demonstrate a semantic space model (SSM) as the corpus-based method, where the appropriately reduced dimensions in SSM can capture the true relationship between documents in terms of concepts, rather than specific terms. Thus, the thesaurus-based method is combined with our SSM as a hybrid strategy to represent the semantic similarity measure. In GA field, the balance between the capability to converge to an optimum and the capacity to explore new solutions affects the success of search for the global optimum. We utilize a fuzzy control GA to adaptively adjust the influence between these two factors. Two textual data sets from Reuter document collection and 20-newsgroup corpus are tested in our experiments, and the results show that our fuzzy control GA combined with the hybrid semantic similarity strategy apparently outperforms the conventional GA, FCM and K-means with the traditional cosine similarity in VSM. Moreover, the superiorities of the fuzzy control GA and our hybrid semantic strategy are demonstrated by their better performance, in comparison with conventional GA with the same similarity measures.

Improved computation method for semantic similarity between gene ontology terms

The current hybrid semantic similarity computation methods do not take full consideration of related factors.Concerning this problem,this paper proposed an integrated method based on multiple factors that affect the calculation of the semantic similarity of Gene Ontology(GO) terms.This method integrated the semantic level,semantic distance and local semantic density,and utilized the semantic information of ontology to fully compute the semantic similarity of gene terms.The experimental results show that the method has higher correlation coefficient with manual scoring than other methods.

Semantic Similarity Models for Geo-spatial Information System

similarity measurement is the practice of estimating the relatedness of the concept based on the likeness of their meaning or their semantic content. Today's increasing interest on the geospatial information system, leads to development of the query system which needs to provide efficient geo-spatial information retrieval. The properties and spatial relations between the geo-spatial concepts must be taken into account for retrieving geo-spatial information efficiently. This paper provides the survey on various models such as geometric model, network model, transformation model, hybrid model, etc, for discovering the semantic similarity between the geo-spatial concepts. It also focuses the drawbacks of each semantic similarity model and depicts how the hybrid model works well when compared with other models in retrieving the Geo-spatial information efficiently. The main objective of this paper is to propose Hybrid semantic similarity model using Manhattan distance and by considering contexts of the Geo-spatial concepts. The Manhattan distance method is used to estimate the semantic distance between the Geo-spatial concepts and to retrieve the Geo-spatial information efficiently. The position of the locations is identified by using the map similarity.

Spam filtering using semantic similarity approach and adaptive BPNN

This paper proposes a novel approach for spam filtering based on various semantic similarity measures and an adaptive back propagation neural network (ABPNN). Semantic similarity approach is a promising avenue that addresses the problems for keyword based spam filtering model. In this paper, we propose a new method that integrates three kinds of semantic similarity approaches for spam filtering as a case study of data mining application. First, to construct a latent semantic feature space from training data with a statistical method. Second, to build a corpus based thesaurus by extracting the relationship between words based on its co-occurrence in the documents. Third, to combine the latent semantic feature space with the corpus based thesaurus. Back propagation neural network is one of the efficient approaches for classification. However, the traditional BPNN has the problems of slow learning and easy to trap into a local minimum. In this paper, we adopt an adaptive algorithm to improve the traditional BPNN that can overcome these problems. To investigate the effectiveness of our methods, we conduct extensive experiments on ling-spam, PU1 and PU3 data sets. Experimental results show that the proposed system is able to achieve higher performance, especially for the combination of the hybrid semantic similarity approach and the adaptive back propagation neural network.