Comparative Study of Semantic and Keyword Based Search Engines

Hussan, Bzar Khidir

doi:10.25046/aj050114

Cited by 4 publications

(3 citation statements)

References 14 publications

(28 reference statements)

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“…The authors state that Google has the best performance, followed by Yahoo and Bing, respectively. Hussan [48] and Jain et al [49] survey semanticbased search engines and point out their pros and cons. The authors state that all the surveyed semantic search engines use mainly semantic web technologies.…”

Section: Current State Of the Art And Related Workmentioning

confidence: 99%

Interactive Search on the Web: The Story So Far

et al. 2022

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Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the search services from the early days of the web to the present into keyword search engines, semantic search engines, question answering systems, dialogue systems and chatbots. As the first generation of search engines, keyword search engines have adopted keyword-based techniques to find the web pages containing the query keywords and ranking search results. In contrast, semantic search engines try to find meaningful and accurate results on the meaning and relations of things. Question-answering systems aim to find precise answers to natural language questions rather than returning a ranked list of relevant sources. As a subset of question answering systems, dialogue systems target to interact with human users through a dialog expressed in natural language. As a subset of dialogue systems, chatbots try to simulate human-like conversations. The paper provides an overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories.

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Section: Current State Of the Art And Related Workmentioning

confidence: 99%

Interactive Search on the Web: The Story So Far

et al. 2022

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“…The ontology defines a common set of terms to represent the basic semantically related concepts built on a limited number of predefined relations and terms of a domain [13]. These terms and concepts can be visualized to represent both syntactic and semantic data [14]. The source [15] reviews how definitions of ontologies changed and evolved over time.…”

Section: Application Technologymentioning

confidence: 99%

Knowledge System Supporting ITS Deployment

et al. 2021

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Intelligent transportation systems are one of the most rapidly evolving areas, requiring an appropriate response from standardization bodies and adequate support from EU regulations. This results in a high and ever-expanding volume of technical standards, which makes their practical use difficult and the harmonization process unsustainable. Therefore, standardization bodies are officially required to supply public information, based on which potential users can decide whether they need to buy or use a particular standard or not. The authors demonstrate how to solve this problem and achieve sustainability in terms of operating in a more intelligent and efficient manner. The proposed solution relies on the creation of standard extracts using a hybrid method that combines syntactic and semantic analysis and assumes human expert involvement. The paper presents the practical experience and results obtained from a long-term national project. A practical example is included so that the reader can comprehend a basic idea of the achieved results. The authors believe the proposed method can be adopted across other professional domains and other European countries.

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“…Search engines are used by millions of users every day to find information, and the method of searching continues to be the same as when the first search engines appeared years ago, relying mainly on keyword search [9]. It has resulted in unsatisfactory search results, as a simple keyword cannot always convey the complex search semantics a user wishes to express, returning irrelevant information and eventually disappointing users.…”

Section: Introductionmentioning

confidence: 99%

Handling Complex Queries Using Query Trees

Vemuru¹,

John²,

Rao³

2021

Preprint

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Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

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Comparative Study of Semantic and Keyword Based Search Engines

Cited by 4 publications

References 14 publications

Interactive Search on the Web: The Story So Far

Interactive Search on the Web: The Story So Far

Knowledge System Supporting ITS Deployment

Handling Complex Queries Using Query Trees

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