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Most published Xplore authors for Semantic Search

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Xplore Articles related to Semantic Search

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A study on web semantics and services

2017 International Conference on Intelligent Sustainable Systems (ICISS), 2017

Information search and its retrieval methods are the main concern of semantics of web. Web semantics improvesthe information retrieval methods. Various semantics search implementation framework have been seen in the past few years. In semantic search we have to investigate number of pilot projects that focus in on methodologies. Research on semantic search system is continue to find the framework ...


Classification of TurkishTweet emotions by n- stage Latent Dirichlet Allocation

2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2018

The classification of the emotions contained in the social media is of great importance in terms of its use in related fields such as media as well as developing technology. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. Dataset consists of angry, fear, happy, sadness ...


Rule-based reasoning for resource recommendation in personalized e-learning

2018 International Conference on Information and Computer Technologies (ICICT), 2018

With the increasing of sharing learning resources to enable the resources discovering published on the e-learning systems. The finding suitable learning resource takes too much time because a system retrieves similar resources for all users (or learners) without considering the needs of individual users. This paper proposes a resource recommendation approach for the personalized e-learning based on reasoning rules. The ...


An Approach of Constructing Knowledge Graph of the Hundred Schools of Thought in Ancient China

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2019

This paper takes the creation of knowledge graph of the Hundred Schools of Thought an example to discuss the application value and realization path of knowledge graph in knowledge organization of digital humanities. We have formed the idea of establishing the knowledge graph of the Hundred Schools of Thought, which consists of four steps: knowledge representation, knowledge extraction, knowledge storage, ...


The Study on Quranic Surahs' Topic Sameness Using NLP Techniques

2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), 2018

Study of the structured-ness of Quranic surahs has attracted the attention of some researchers in recent years. One of the theories herein is the theory of Topic Sameness which acknowledges that the inner elements of surahs have tight relationship with each other and that each surah of Quran has formed on a single core topic. In this paper, we intend ...


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Educational Resources on Semantic Search

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IEEE-USA E-Books

  • A study on web semantics and services

    Information search and its retrieval methods are the main concern of semantics of web. Web semantics improvesthe information retrieval methods. Various semantics search implementation framework have been seen in the past few years. In semantic search we have to investigate number of pilot projects that focus in on methodologies. Research on semantic search system is continue to find the framework on which semantic framework is formalized. Web services and semantics are used to create the web of distributed machine forproper understanding of data. This paper presents a review on web semantics and search. It described two semantic search systems implementation, based on search query, augment traditional search with relevant data are hated from source. This paper covers the searching and semantic issue ofweb technologyto outline the semantics of the of the search terms for predicting better results.

  • Classification of TurkishTweet emotions by n- stage Latent Dirichlet Allocation

    The classification of the emotions contained in the social media is of great importance in terms of its use in related fields such as media as well as developing technology. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. Dataset consists of angry, fear, happy, sadness and surprise, 5 emotions and 4000 tweets. Zemberek, Snowball and the first 5 letter root extraction methods are used to create the model. The generated models were tested with the n-stage GDA method we developed and compared with the GDA. For the 5 classes of normal GDA method, the highest 60.4% success was achieved; 70.5% for 2-stage GDA and 76.4% for 3-stage GDA.

  • Rule-based reasoning for resource recommendation in personalized e-learning

    With the increasing of sharing learning resources to enable the resources discovering published on the e-learning systems. The finding suitable learning resource takes too much time because a system retrieves similar resources for all users (or learners) without considering the needs of individual users. This paper proposes a resource recommendation approach for the personalized e-learning based on reasoning rules. The proposed approach designs ontology as a reference ontology which concentrates on describing the learning style appropriate to each learner. The Personalization Rules are defined to support personalized semantic search for heterogeneous learning resources, which deduced by a reasoning engine. Experimental results demonstrate that the proposed approach enables the resource recommendation to individual users, which is originated from multiple sources.

  • An Approach of Constructing Knowledge Graph of the Hundred Schools of Thought in Ancient China

    This paper takes the creation of knowledge graph of the Hundred Schools of Thought an example to discuss the application value and realization path of knowledge graph in knowledge organization of digital humanities. We have formed the idea of establishing the knowledge graph of the Hundred Schools of Thought, which consists of four steps: knowledge representation, knowledge extraction, knowledge storage, semantic search and knowledge visualization. At present, this paper has completed the ontology construction of Confucius, Laozi and Mozi, which are the three core figures of the Hundred Schools of Thought. This knowledge graph will be further realized by using web crawler and neo4j.

  • The Study on Quranic Surahs' Topic Sameness Using NLP Techniques

    Study of the structured-ness of Quranic surahs has attracted the attention of some researchers in recent years. One of the theories herein is the theory of Topic Sameness which acknowledges that the inner elements of surahs have tight relationship with each other and that each surah of Quran has formed on a single core topic. In this paper, we intend to study the topic sameness in Quranic surahs using natural language processing methods. In this regard, based on the two methods of word2vec and Roots' accompaniment in Verses, the similarity of Quranic roots is calculated. Then, the amount of similarity between surahs' title and the concepts within the surahs is studied. Afterwards, the amount of similarity of the concepts within chapters to each other is calculated and compared with the random mode. The results show that the choice of the surah's title is based on rational logic, and that could not have been done by the ordinary public of the early Islamic era. In addition, the surahs hold the inner coherence between the concepts so that they have formed on a single topic or a few topics tightly related to each other.

  • Towards Semantic Search for Mathematical Notation

    The paper concerns the design and implementation of a search engine for mathematical expressions given by the user in a convenient form of natural language or visual queries. Proper presentation and transcription of the mathematical notation is substantial for further processing and the adequate choice of the word distance measure for string comparison is an important issue as well. Within this project a complete solution for acquiring and processing the mathematical query and a searching algorithm is elaborated. We present results of exemplary search queries obtained for different types of input data format with application of two different word distance measures and discuss briefly the observed properties.

  • Ontology-Based Semantic Search for Open Government Data

    Open data are increasingly available in amount, but often with unprecise or incomplete description. It is time consuming and difficult to discover relevant datasets. Current open data catalogues provide mostly keyword-based search without the ability to understand the user's intent and the contextual meaning of the datasets. Ontology-based semantic search has been well explored in semantic web as an attempt to improve the quality of search for relevant documents and web pages. This paper applies semantic and machine learning technologies to open data. It presents an approach for search of open government datasets, a relatively underexplored domain, where the semantics of data relies on metadata that describes the data. The idea is to link the published datasets with concepts from a well-defined ontology and allow searching based on hybrid indexing. A simplified ontology for the transport domain is constructed to demonstrate and test the idea. A prototype search engine has been implemented which supports both manual and automatic linking to concepts in the ontology and exploits hybrid indexing based on these linking methods. Natural language processing (NLP) techniques are applied to dataset linking and indexing and enable the independency of the natural language used for describing the datasets. The manual linking of datasets to ontology concepts is intended for domain experts and data publishers, while the automatic linking is based on the provided dataset descriptions. The automatic linking reduces the overhead of manual concepts linking and the dependency on domain experts. Preliminary results have indicated that semantic search based on ontologies is a promising approach to increase search quality and efficiency for open data search. The success of the automatic mechanism does however depend on the quality and comprehensiveness of the dataset descriptions.

  • An Extensible Semantic Search Engine for Biomedical Publications

    The ever increasing amount of publications in the biomedical domain leads to the challenge of finding the right answer to a specific question within a vast sea of information. For instance, the biomedical search engine PubMed has an index of over 27 million publications. PubMed implements a keyword-based search. While easy to operate, the results are shown as a paginated textual list, which is time-consuming to navigate. To give the users a more convenient way of searching for information and displaying it, a variety of interfaces which operate on top of PubMed have emerged. One of those platforms is ViLiP, developed as visual exploratory interface to PubMed and primarily used in the neuroscience domain. ViLiP presents the result of a user's query in form of an in-situ heat map. In this work, ViLiP is extended by an NLP-based semantic search engine, for the use-case of detecting drug information within a query. Based on linguistic annotations, potential candidates for drug names are selected from an RDF data source, and matching publications are searched for.

  • Automatically Semantic Annotation of Network Document Based on Domain Knowledge Graph

    Massive network document resources provide abundant retrieving and reading information, but it is consuming and exhausting to quickly search, understand and analyze those documents. In order to seek semantic support for searching, understanding, analyzing, and mining, this paper proposes a more convenient way which based on domain knowledge graph to annotate network document automatically. The method firstly adopts an upgraded TF-IDF model based on the contribution to quantify instances in knowledge graph, then analyzes the semantic similarity between unannotated documents and instances based on Jaccard distance and lexicographic tree distance comprehensively. After the accuracy tests conducted by collecting network documents, the results show the initial marking accuracy is up to 74%, successfully certifying the method being able to automatically annotate network documents in terms of semantics from the domain knowledge graph.

  • An Arabic semantic search engine for large governmental organization

    Large organizations contain huge structured and unstructured data. This data need to be analyzed and retrieved as a part of their daily business. Data extractor that depends on entity recognition to extract data from documents and converts it into structured database can solve the problem of searching in unstructured data. In addition, semantic search engines that use query expansion to extract results that are more informative can solve the problem of polysemy and synonymy. This paper proposes a complete solution to solve these problems. An Arabic semantic search engine is proposed which consists of four components (data extractor, taxonomy builder, database indexer, and search engine). The system is applied on a real case study of a large governmental organization's database. The results show superior performance compared to other solutions. It gives good measures for the F-score and gives a mean average precision of 0.8.



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