Semantic Search Method Research Articles

Development of a semantic search method for retrieving food related verses & concepts from the holy Quran using ontology

Qur’an is Allah (SWT)’s greatest miracle and is the source of all knowledge and information. As a Muslim, obligation is not only to recite the Qur’an but also it is important to gain knowledge from the Qur’an. Ontology is the best way to retrieve Quranic knowledge in a technical way. With the help of internet many search engines are found to discover information from Qur’an. But most of the search engines or information retrieval systems are keyword-based which can often lead to irrelevant results. To overcome this problem, semantic search is most useful in this case. The aim of research is to develop an ontological semantic based method to retrieve the food related verses and concepts from holy Qur’an by using natural language query. In this work, triplet extraction algorithm has been used for generating triple, the protege OWL editor 4.3 version used to create food ontology and the SPARQL Apache Jena fuseki server was used for querying. Quranic data are collected from English translation of the holy Qur’an. IIUC Studies Vol.18, December 2021: 101-122

Using search engines as a retrieval tool for translating newly coined expressions and terminology between Chinese and English

Abstract Translating terminology and newly coined expressions is a daunting challenge for technical translators. Translation of such words or phrases between, e.g. Chinese and English has received little help from dictionaries. With this as motivation, this study explores four methods by analysing results from an experiment with search engines as a retrieval tool for tackling such translation tasks. Specifically, taking 700 new terminologies from The Oxford English Dictionary as the research objects, this study experimentally examines the four terminology translation extraction methods. For each method, the effectiveness is assessed. This article finds that template-based and dictionary-based translation extraction methods have the highest accuracy rates of 86 and 89%, respectively. The semantic prediction search method proves to be the most flexible with the highest retrieval rate of 95%, although its results are not always accurate. Based on the results, this study proposes a model for quantitatively and qualitatively verifying the accuracy of translation results obtained from search engines. The quantitative analysis examines the use frequency of all translations obtained, while the qualitative analysis looks into their contexts. These findings have implications for translating, via search engine retrieval tools, terminology between other language pairs.

Сurrent state of clinical significance of early kidney injury markers

Study objective was to reflect the current state of the early kidney damage markers clinical significance in patients with urolithiasis. Authors performed a literature review of articles (original studies and reviews) published between 2018 and February 2022 on the study of pro‑inflammatory, profibrotic and angiogenic factors in urolithiasis. Bibliographic, informational and semantic source search methods in Google Scholar, PubMed, Scopus and Embase databases were used. Analysis of a legalistic and systematic sources was carried out.Proteinuria, serum creatinine and glomerular filtration rate are now accepted as the main diagnostic markers of renal lesions. These markers already allow to reveal lesions at the stage of chronic kidney disease, when the lesion process is practically irreversible. Today it is most relevant to conduct the search for biomarkers of acute and chronic renal lesions at patients with urolithiasis to detect pathological changes in the kidneys and to determine their nature at an early stage of diagnosis.Improved diagnostic techniques destined to provide a detailed picture of the development of the pathological process in patients with urolithiasis will make it possible to prevent renal failure development at an early stage in this category of patients.

Designing An Information Framework For Semantic Search

New generation information retrieval procedures provide complex tools to remodel the design of search engines. Even though semantic analysis is gradually adopted by corporations, complex behavior of knowledge behind the information entails subsequent data learning models. Text models are currently in use through lexical features. Search engines with lexical methods lack contextual and semantic information. This barrier has been overcome with the development of deep learning methods. More accurate results can be retrieved by obtaining contextual information of different types of content such as text, image, video with neural models. In this study, a broad perspective of search engines was considered through lexical and semantic features. Semantic search methods were experimented then compared with lexical methods in data sets consisting of scientific documents. Since scientific documents are relatively well-formatted datasets and do not contain irrelevant content, the focus was on comparing semantic search methods and neural models throughout the study, without dealing with out-of-context data and semantic conflicts. As a result, semantic search methods performed better than lexical search. We conclude that current search-retrieval tasks require new perspectives in semantics where multimodal information is handled with deep learning strategies.

Semantic Reasoning of Product Biologically Inspired Design Based on BERT

Bionic reasoning is a significant process in product biologically inspired design (BID), in which designers search for creatures and products that are matched for design. Several studies have tried to assist designers in bionic reasoning, but there are still limits. Designers’ bionic reasoning thinking in product BID is vague, and there is a lack of fuzzy semantic search methods at the sentence level. This study tries to assist designers’ bionic semantic reasoning in product BID. First, experiments were conducted to determine the designer’s bionic reasoning thinking in top-down and bottom-up processes. Bionic mapping relationships, including affective perception, form, function, material, and environment, were obtained. Second, the bidirectional encoder representations from transformers (BERT) pretraining model was used to calculate the semantic similarity of product description sentences and biological sentences so that designers could choose the high-ranked results to finish bionic reasoning. Finally, we used a product BID example to show the bionic semantic reasoning process and verify the feasibility of the method.

Picture semantic similarity search based on bipartite network of picture-tag type.

Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by "description" relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that "similar pictures contain similar tags, and similar tags describe similar pictures", which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.

Dynamic semantic description and search methods for heterogeneous learning resources

Dynamic semantic description and search methods for heterogeneous learning resources

An Empirical Study of Online Terminology Translation Retrieval:Using Search Engines as a Tool

Online search engines and other computer-aided tools are playing an increasingly important role in technical translation, especially in the translation of out-of-vocabulary new terms. Dictionaries, at times, prove to be of little help to term translation where out-of-vocabulary terms rely on polysemy for heuristic effects. This paper explores the role of online search engines in terminology translation by analyzing the effectiveness of four terminology translation extraction methods which utilize online search engines as search tools, thereby generating suggestions on how to use search result for reference. An experiment with some new terms was conducted to examine the effectiveness of each method. This study revealed that template-based term translation extraction and dictionary-based translation extraction have the highest accuracy rate of 86% and 89% respectively while semantic prediction search method is the most flexible method with the highest retrieval rate of 95% although its results are not always accurate. Therefore, a verification model was proposed, which utilizes quantitative-and-qualitative context analysis to verify the accuracy of translation results obtained from a search engine.

Cемантический поиск учебных дисциплин под требования рынка труда на основе нейросетевых моделей языка

With the growth of open educational content, growing demand for professional education from the labor market, and the development of the concept of lifelong learning, the task of updating the content of educational programs today is extremely important. The article discusses the semantic search method to retrieval and ranking of educational content for the specified requirements of the labor market, determined by professional standards. In contrast to traditional approaches of matching and analyzing the content of educational programs based on ontological models and rules, we propose the usage of word embedding and well-known neural network language models word2vec and fastText. The initial requests are specific requirements for knowledge, skills and descriptions of labor activities and labor functions extracted from professional standards. The search results are the descriptions of academic disciplines and online courses, including goals and objectives, learning outcomes, the structure and content of the main topics. We include the results of the expert evaluation of the ranking quality for the semantic search by metrics NDCG (Normalized Discounted Cumulative Gain) and MAP (Mean Average Precision) on the representative corpus of IT disciplines programmes of universities and massive open online courses (MOOC). The best results for the search are shown by the word2vec and fastText models, which are trained without supervision on large specially prepared corpuses of curriculum programs and descriptions of online courses. To move from word vectors to document vectors various combinations of neural network models with the TF-IDF weighting scheme are investigated.

The Social and Conceptual Semantic Extended Search Method for Microblog Short Text

The Social and Conceptual Semantic Extended Search Method for Microblog Short Text

PENERAPAN METODE SEMANTIK SEARCH DALAM SISTEM INFORMASI PENCARIAN DOKUMEN KERJA PRAKTEK DAN SKRIPSI BERBASIS WEB

This writing discusses the making of the application search documents practical work and thesis in the Department of Informatics Engineering Faculty of Computer Science Univestias Muhammadiyah Riau. This web-based application uses semantic search method in its search results. This app is designed to generate relevant or easy-to-understand search words by students and also generate words related to search keywords. The goal is to facilitate students in finding reference documents working practice and thesis and to avoid similarity with the previous student topic.

Теоретичні засади застосування онтологій для семантизації ресурсів Web

In this paper we show the main directions of semantization of the Web information resources and their theoretical bases. We consider the semantization problems associated with the open environment information at the knowledge level. This analysis substantiates the need for the Web semantic search methods and means. Modern principle tendencies in the personified semantic deal with the ontology-based formalization of knowledge about the information needs of users and their current tasks. Our approach is based on use of the semantically marked Wiki-resources as a source of knowledge for constructing of ontologies of individual task. Each task ontology is considered as a small subset of domain ontology with large number of terms volume and high complexity. We develop the formal model of the task ontology as a special case of the formal model of domain ontology and propose methods for constructing of this task ontology on base of the set of semantically marked Wiki pages. The software implementation of the retrieval systems capable to process such task ontologies is analyzed on example of MAIPS – semantic retrieval system which provides personalized satisfaction of user information needs. Such personification is based on knowledge contained into the user task thesauri that are built on base of task ontologies. Problems in programming 2018; 2-3: 197-203

Enriching semantic search with preference and quality scores

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.

텐서공간모델 기반 시멘틱 검색 기법

Semantic search is known as a series of activities and techniques to improve the search accuracy by clearly understanding users’ search intent without big cognitive efforts. Usually, semantic search engines requires ontology and semantic metadata to analyze user queries. However, building a particular ontology and semantic metadata intended for large amounts of data is a very time-consuming and costly task. This is why commercialization practices of semantic search are insufficient. In order to resolve this problem, we propose a novel semantic search method which takes advantage of our previous semantic tensor space model. Since each term is represented as the 2nd-order ‘document-by-concept’ tensor (i.e., matrix), and each concept as the 2nd-order ‘document-by-term’ tensor in the model, our proposed semantic search method does not require to build ontology. Nevertheless, through extensive experiments using the OHSUMED document collection and SCOPUS journal abstract data, we show that our proposed method outperforms the vector space model-based search method.

ELSE: an ontology-based system integrating semantic search and e-learning technologies

ABSTRACTELSE (E-Learning for the Semantic ECM) is an ontology-based system which integrates semantic search methodologies and e-learning technologies. It has been developed within a project of the CME (Continuing Medical Education) program – ECM (Educazione Continua nella Medicina) for Italian participants. ELSE allows the creation of e-learning courses which are customized according to the needs and learning preferences of the user. It 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 SemSim semantic search method allows the selection of the set of RLOs that satisfy the user request at best in efficient way. This set is successively reorganized according to the learning preferences of the user. ELSE has been validated and experimented in the domain of osteoporosis, and the overall judgment about it is very positive, both in terms of usability and effectiveness of customization.

Metadata Topic Harmonization and Semantic Search for Linked‐Data‐Driven Geoportals: A Case Study Using ArcGIS Online

AbstractGeoportals provide integrated access to geospatial resources, and enable both authorities and the general public to contribute and share data and services. An essential goal of geoportals is to facilitate the discovery of the available resources. Such a process relies heavily on the quality of metadata. While multiple metadata standards have been established, data contributers may adopt different standards when sharing their data via the same geoportal. This is especially the case for user‐generated content where various terms and topics can be introduced to describe similar datasets. While this heterogeneity provides a wealth of perspectives, it also complicates resource discovery. With the fast development of the Semantic Web technologies, there is a rise of Linked‐Data‐driven portals. Although these novel portals open up new ways to organize metadata and retrieve resources, they lack effective semantic search methods. This article addresses the two challenges discussed above, namely the topic heterogeneity brought by multiple metadata standards and the lack of established semantic search in Linked‐Data‐driven geoportals. To harmonize the metadata topics, we employ a natural language processing method, namely Labeled Latent Dirichlet Allocation (LLDA), and train it using standardized metadata from Data.gov. With respect to semantic search, we construct thematic and geographic matching features from the textual metadata descriptions, and train a regression model via a human participants experiment. We evaluate our methods by examining their performances in addressing the two issues. Finally, we implement a semantics‐enabled and Linked‐Data‐driven prototypical geoportal using a sample dataset from Esri's ArcGIS Online.

Semantic Enrichment and Search: A Case Study on Environmental Science Literature

As information discovery needs become more and more challenging, traditional keyword-based information retrieval methods are increasingly falling short in providing adequate support. The problem is often compounded by the poor quality of article metadata in some digital collections. This paper investigates automatic semantic enrichment and search methods, as ways to meet these challenges. In particular, the benefits of enriching articles with knowledge from Linked Open Data resources are investigated, with focus on the domain of environmental science. In order to facilitate environmental science researchers in carrying out better semantic searches, a form-based semantic search interface is proposed. It helps researchers to benefit from the semantically enriched content, e.g. to carry out sophisticated location-based searches. The usability and ease of learning of this web interface were evaluated in a user-based study, the results of which are also reported.

온톨로지를 이용한 웹문서의 시맨틱 검색

사용자들에게 좀 더 정확하고 편리한 검색결과를 제공하기 위하여 정보의 구조적인 특징 등을 사용하는 시맨틱 검색의 개념이 널리 연구되고 있다. 이를 위하여, 최근의 정보검색분야와 데이터구축 분야의 연구에서는 데이터의 구조적인 표현과 검색 메카니즘을 구현하기 위하여 온톨로지를 강조하고 있다. 본 연구에서는 웹 환경에서의 검색 정확도와 만족도를 향상시키기 위하여 온톨로지를 이용한 시맨틱 검색 방법을 제안한다. 온톨로지와 KB(KnowledgeBase)를 이용하여 검색 대상을 키워드간의 관계를 유추한 사실(fact)과 관계키워드들을 지니는 웹문서들로 크게 나누고 이들을 서로 유기적으로 검색을 진행하는 시맨틱 검색 질의 처리기법을 제안하였다. 또한 결과에 대한 사용자의 검색 만족도를 높이기 위하여 결과 문서와 사실에 대한 랭킹 방법을 제안하였다. 실험을 통하여 주어진 식의 값을 달리하여 랭킹을 올바로 구현하는 요소로 키워드의 빈도와 온톨로지상의 클래스 레벨이 영향을 미치는 것을 확인 할 수 있었고, 이를 통하여 적합한 형태의 계수 값을 제시하였다. To provide efficient and correct search results, ontology which use the structure of information, is considered as a main mechanism in the semantic web. Therefore, recent research in information retrieval and data construction have emphasized the use of ontologies as a data representation and search mechanism. In this paper, we propose a semantic search method using ontology to improve search ability in web environment. Ontology and knowledge base is used to represent semantic meaning of the data and provide related web documents and facts as results. Also, search result ranking mechanism is proposed. The mechanism use cardinality of the keyword in the contents and structural information of ontology. Experimental results with several query processing indicate that different coefficient value in the expression gives different results in sample ontology system and we propose appropriate values of the coefficient.

A Semantic Retrieval Method Based on Ontology

Semantic retrieval method stands at the crossroads between Natural Language Processing and Machine Intelligent. This paper makes analysis on the semantic search method and research on concept similarity algorithm, and discusses the factor of weight’s influence on concept similarity as well. On this basis, this paper proposed a new semantic search method based on ontology, and apply it to the tourism information retrieval, which intellectualized tourism information retrieval service.

Ranking methods for entity‐oriented semantic web search

This article provides a technical review of semantic search methods used to support text‐based search over formal Semantic Web knowledge bases. Our focus is on ranking methods and auxiliary processes explored by existing semantic search systems, outlined within broad areas of classification. We present reflective examples from the literature in some detail, which should appeal to readers interested in a deeper perspective on the various methods and systems implemented in the outlined literature. The presentation covers graph exploration and propagation methods, adaptations of classic probabilistic retrieval models, and query‐independent link analysis via flexible extensions to the PageRank algorithm. Future research directions are discussed, including development of more cohesive retrieval models to unlock further potentials and uses, data indexing schemes, integration with user interfaces, and building community consensus for more systematic evaluation and gradual development.