scholarly journals Semantic Search in Millions of Equations

2020 ◽  
Author(s):  
Lukas Pfahler ◽  
Katharina Morik
Keyword(s):  
Semantic Search

Framework for Semantic Search Enabled Software Component Repository (SSESCR)

2018 ◽  
Author(s):  
Vishnu Sharma ◽  
Vijay Singh Rathore ◽  
Chandikaditya Kumawat
Keyword(s):  
Software Quality ◽  
Software Reuse ◽  
Development Time ◽  
Software Component ◽  
Semantic Search ◽  
Easy Access ◽  
Software Components ◽  
Efficient Search ◽  
Systematic Reuse ◽  
New System

Software reuse can improve software quality with the reducing cost and development time. Systematic reuse plan enhances cohesion and reduces coupling for better testability and maintainability. Software reuse approach can be adopted at the highest extent if relevant software components can be easily searched, adapted and integrated into new system. Large software industries hold their own well managed component libraries containing well tested software component with the project category based classification .Access to these repositories are very limited. Software reuse is facing so many problems and still not so popular. This is due to issues of general access, efficient search and adoption of software component. This paper propose a framework which resolves all of the above issues with providing easy access to components, efficient incremental semantics based search, repository management, versioning of components.

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.

Method of semantic search and analysis of information

2020 ◽  
pp. 75-80
Author(s):  
A.L. Ogarok
Keyword(s):  
Computer Systems ◽  
Linguistic Analysis ◽  
Semantic Search ◽  
Textual Information ◽  
Formalized Description

The methodology of semantic search and analysis of information is considered. The results of the analysis of various approaches to solving the problem of a complete linguistic analysis of textual information in computer systems are presented. A formalized description of the method of semantic search and analysis of information is given.

Semantic Search on Unstructured Data

2010 ◽  
Vol 6 (2) ◽  
pp. 17-35 ◽  
Author(s):  
Alex Kohn ◽  
François Bry ◽  
Alexander Manta
Keyword(s):  
Retrieval System ◽  
Pharmaceutical Research ◽  
Information Retrieval System ◽  
Semantic Search ◽  
Unstructured Data ◽  
Search Performance ◽  
Adaptive Search ◽  
Retrieval Performance ◽  
Enterprise Search ◽  
Existing Data

Studies agree that searchers are often not satisfied with the performance of current enterprise search engines. As a consequence, more scientists worldwide are actively investigating new avenues for searching to improve retrieval performance. This paper contributes to YASA (Your Adaptive Search Agent), a fully implemented and thoroughly evaluated ontology-based information retrieval system for the enterprise. A salient particularity of YASA is that large parts of the ontology are automatically filled with facts by recycling and transforming existing data. YASA offers context-based personalization, faceted navigation, as well as semantic search capabilities. YASA has been deployed and evaluated in the pharmaceutical research department of Roche, Penzberg, and results show that already semantically simple ontologies suffice to considerably improve search performance.

scholarly journals Enhancing semantic search using case-based modular ontology

2010 ◽  
Author(s):  
Nesrine Ben Mustapha ◽  
Hajer Baazaoui Zghal ◽  
Marie-Aude Aufaure ◽  
Henda Ben Ghezala
Keyword(s):  
Semantic Search ◽  
Modular Ontology ◽  
Case Based

Semantic Information Retrieval on Medical Texts

2022 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Lynda Tamine ◽  
Lorraine Goeuriot
Keyword(s):  
Information Retrieval ◽  
Health Informatics ◽  
Medical Information ◽  
State Of The Art ◽  
Lessons Learned ◽  
Semantic Search ◽  
Future Research ◽  
Cross Model ◽  
Wide Range ◽  
Search Systems

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.

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