scholarly journals Application of Long-Short Memory Neural Networks in Semantic Search Engines Development

2020 ◽  
Vol 169 ◽  
pp. 388-392
Author(s):  
Valentin Klimov ◽  
Anita Balandina ◽  
Artem Chernyshov
Keyword(s):  
Neural Networks ◽  
Search Engines ◽  
Semantic Search ◽  
Short Memory

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

A Review on Semantic Text and Multimedia Retrieval and Recent Trends

2015 ◽  
Vol 6 (1) ◽  
pp. 54-74
Author(s):  
Oğuzhan Menemencioğlu ◽  
İlhami Muharrem Orak
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Search Engines ◽  
Semantic Search ◽  
Multimedia Retrieval ◽  
Web Pages ◽  
The Face ◽  
Recent Trends ◽  
New Applications ◽  
Machine Readable

Semantic web works on producing machine readable data and aims to deal with large amount of data. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that consists in the existing web pages. Semantic search engines are extensions to traditional engines and overcome the difficulties faced by them. This paper summarizes semantic web, concept of traditional and semantic search engines and infrastructure. Also semantic search approaches are detailed. A summary of the literature is provided by touching on the trends. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging. Multimedia retrieval is a newly scope of semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia retrieval is analyzed within semantic search.

Evaluating search features of Google Knowledge Graph and Bing Satori

2015 ◽  
Vol 39 (2) ◽  
pp. 197-213 ◽  
Author(s):  
Ahmet Uyar ◽  
Farouk Musa Aliyu
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Search Engines ◽  
Web Search ◽  
Semantic Search ◽  
Knowledge Graph ◽  
Content Type ◽  
Query Interfaces ◽  
Natural Language Query ◽  
Web Search Engines

Purpose – The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces. Design/methodology/approach – The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography. Findings – The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms. Research limitations/implications – Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research. Practical implications – The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces. Originality/value – As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

A Comparison Between Ontology-Based and Translation-Based Semantic Search Engines for Arabic Blogs

2012 ◽  
Vol 38 (11) ◽  
pp. 2985-2992 ◽  
Author(s):  
Lilac Al-Safadi ◽  
Dalal Al-Rgebh ◽  
Wejdan AlOhali
Keyword(s):  
Search Engines ◽  
Semantic Search

scholarly journals MITTAG-LEFFLER STABILITY ANALYSIS OF TEMPERED FRACTIONAL NEURAL NETWORKS WITH SHORT MEMORY AND VARIABLE-ORDER

Fractals ◽  
2021 ◽  
pp. 2140029
Author(s):  
CHUAN-YUN GU ◽  
FENG-XIA ZHENG ◽  
BABAK SHIRI
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Stability Analysis ◽  
Fixed Point Theorem ◽  
Banach Fixed Point Theorem ◽  
Stability Conditions ◽  
Variable Order ◽  
Numerical Examples ◽  
Short Memory ◽  
Theoretical Results

A class of tempered fractional neural networks is proposed in this paper. Stability conditions for tempered fractional neural networks are provided by using Banach fixed point theorem. Attractivity and Mittag-Leffler stability are given. In order to show the efficiency and convenience of the method used, tempered fractional neural networks with and without delay are discussed, respectively. Furthermore, short memory and variable-order tempered fractional neural networks are proposed under the global conditions. Finally, two numerical examples are used to demonstrate the theoretical results.

Perspectives and Key Technologies of Semantic Web Search

2011 ◽  
pp. 1532-1537 ◽  
Author(s):  
Konstantinos Kotis
Keyword(s):  
Semantic Web ◽  
Search Engines ◽  
Web Search ◽  
Semantic Search ◽  
Web Pages ◽  
Domain Specific ◽  
Web Information ◽  
Natural Language Query ◽  
Web Search Engines ◽  
Semantically Heterogeneous

Current keyword-based Web search engines (e.g. Googlea) provide access to thousands of people for billions of indexed Web pages. Although the amount of irrelevant results returned due to polysemy (one word with several meanings) and synonymy (several words with one meaning) linguistic phenomena tends to be reduced (e.g. by narrowing the search using human- directed topic hierarchies as in Yahoob), still the uncontrolled publication of Web pages requires an alternative to the way Web information is authored and retrieved today. This alternative can be the technologies of the new era of the Semantic Web. The Semantic Web, currently using OWL language to describe content, is an extension and an alternative at the same time to the traditional Web. A Semantic Web Document (SWD) describes its content with semantics, i.e. domain-specific tags related to a specific conceptualization of a domain, adding meaning to the document’s (annotated) content. Ontologies play a key role to providing such description since they provide a standard way for explicit and formal conceptualizations of domains. Since traditional Web search engines cannot easily take advantage of documents’ semantics, e.g. they cannot find documents that describe similar concepts and not just similar words, semantic search engines (e.g. SWOOGLEc, OntoSearchd) and several other semantic search technologies have been proposed (e.g. Semantic Portals (Zhang et al, 2005), Semantic Wikis (Völkel et al, 2006), multi-agent P2P ontology-based semantic routing (of queries) systems (Tamma et al, 2004), and ontology mapping-based query/answering systems (Lopez et al, 2006; Kotis & Vouros, 2006, Bouquet et al, 2004). Within these technologies, queries can be placed as formally described (or annotated) content, and a semantic matching algorithm can provide the exact matching with SWDs that their semantics match the semantics of the query. Although the Semantic Web technology contributes much in the retrieval of Web information, there are some open issues to be tackled. First of all, unstructured (traditional Web) documents must be semantically annotated with domain-specific tags (ontology-based annotation) in order to be utilized by semantic search technologies. This is not an easy task, and requires specific domain ontologies to be developed that will provide such semantics (tags). A fully automatic annotation process is still an open issue. On the other hand, SWDs can be semantically retrieved only by formal queries. The construction of a formal query is also a difficult and time-consuming task since a formal language must be learned. Techniques towards automating the transformation of a natural language query to a formal (structured) one are currently investigated. Nevertheless, more sophisticated technologies such as the mapping of several schemes to a formal query constructed in the form of an ontology must be investigated. The technology is proposed for retrieving heterogeneous and distributed SWDs, since their structure cannot be known a priory (in open environments like the Semantic Web). This article aims to provide an insight on current technologies used in Semantic Web search, focusing on two issues: a) the automatic construction of a formal query (query ontology) and b) the querying of a collection of knowledge sources whose structure is not known a priory (distributed and semantically heterogeneous documents).

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