Intelligent Information Retrieval in Industrial Information Storage

Large amounts of information are stored in industrial information repositories, and access to this information is complex. Therefore, the techniques used in metadata and the materials chosen by the user cannot be scaled efficiently. A healthy web allows information to be shared and reused efficiently.

Intelligent Information Retrieval from Industrial Information Repositories

Today, industrial information includes capacities of hosted equipment, performances, start or stop dates, turbine and generator models, etc. Provides effective information about available resources through databases and repositories that provide details about the equipment hosted, including information. All these features and information are stored in digital repositories, digital files, and business websites. Online databases called digital industry repositories (DIR) are used to collect, contribute and share information about resources installed in the industrial field. Therefore, the way information and information stored in digital repositories are retrieved is vital. DIRs provide centralized hosting and access to content, permissions and controls for accessing content, and the ability to share digital objects or files.
Existing search engines get the information by comparing the content of the database with the searched models. The result generated is a list of data containing this pattern. Although search engines have become more efficient, information overload prevents searching and accessing correct information. As a result, it is necessary to develop new semantic and intelligent models that contribute to new possibilities. The presented study offers a new approach to information retrieval based on semantics and smart models. For this, case-based reasoning (CBR) technique is applied, which contributes to the goal of improving knowledge acquisition in the industrial field.
A significant number of researchers have already researched the application of intelligence and semantic techniques, but only a few of them are an industrial environment in terms of the full integration of both technologies. For example, researchers and related field studies involving ontology finding methods conduct research to analyze the usefulness of ontologies to perform document searches effectively. For this, it provides a system that uses an ontology query model and proposes an algorithm to refine ontologies for preliminary information retrieval tasks. Intelligent Information Retrieval in Industrial Information Storage
In a test study, real-time image capture was performed using digital camera technology and image processing technology. The closure and quality of the glue curve can be determined by subtracting the glue line curve from the image, thinning the glue curve by morphological method, and extracting the frame information. Test results show that the effect is satisfactory and the method is effective. Its main contribution is a new semantic inquiry extending technique that combines association rules with natural language processing techniques that make use of explicit semantics as well as ontologies and other linguistic features of unstructured text corpus.
It takes advantage of the contextual properties of important terms discovered by association rules, and ontology entries are added to the query, eliminating the ambiguity of the word sense. The Semantic Web uses concepts, taxonomic relationships, and non-taxonomic relationships in a given domain ontology to efficiently capture information. For example, it describes a component of an information management platform with multiple agent search module (MASH) that uses domain ontology to search Web pages containing information about each concept in the respective domain. The search is then restricted to a specific area to avoid the analysis of irrelevant information as much as possible.
Each existing layer describes its function, analyzes the implementation of this system from the organization of information, expression and access to information, and proposes an information management system framework based on ontology. This management system establishes a shareable ontology that can be understood by both human and computer. People can find more associations of different concepts through a better state of the information access interface. It proposes an ontology-based user model called user ontology to provide personalized information service in the Semantic Web that uses concepts, taxonomic relationships, and non-taxonomic relationships in a particular domain ontology to capture users’ interests.
His research presents a semantics-based digital project that provides directional search and represents a new approach to digital libraries, integrating social web and multimedia elements in a semantically annotated repository. In another study, it was designed to automatically and intelligently index large repositories of special effects video clips based on their semantic content, using a network of scalable ontologies to enable smart access. This design describes the architecture of dynamic retrieval analysis and semantic metadata management system (DREAM). When looked semantically; It provides an information search and retrieval framework based on an annotated versatile product family ontology. It is an innovative, comprehensive semantic search model that extends the classical information retrieval model and addresses the challenges of the massive and heterogeneous web environment. Intelligent Information Retrieval in Industrial Information StorageThere is a lot of research on applying these new technologies to existing information retrieval systems. However, no research addresses artificial intelligence (AI) and semantic issues from the entire life cycle and architectural perspective. The main bidding goal is smart search management in decentralized industrial pools where there is no global information scheme. The most important innovation brought by this proposal is that contextual user profiles are based on ontologies and metadata that facilitate ontological search using expert system technologies. The goal is focused on creating technologically complex environments industrial domains. And it includes semantic web and AI technologies to ensure precise positioning of industrial resources. They are smart systems with the general purpose of replacing human operators with smart agents. CBR methodology is used to develop a prototype to support efficient information retrieval from DIR.

Future Studies

There is a system based on ontology and artificial intelligence architecture for information management in industrial pools. An effort to design and develop a prototype to manage resources in a repository, such as the OntoEnter project, is exploited to assist users in selecting resources. In the study, the main aspects of a semantic web information retrieval system architecture that tries to respond to the needs of new generation semantic web users should be discussed. An important goal is to study appropriate industrial cases, compile arguments, launch industrial projects, as well as develop prototypes for industrial companies that also benefit from the semantic web.
Semantic models play an important role in emerging solution architectures that support the business goal of getting a complete perspective of what is happening within operations and then deriving business insights from that perspective. Semantic models based on industry standards take this a step further, especially as application vendors adopt these standards. DIRs can be part of a larger framework of interactive global information networks, including scientific repositories, commercial providers. It can be based on standards and existing building blocks as well as web standards as possible. The combination of effective information retrieval techniques and IAs continues to show promising results for users in improving the performance of information extracted from online repositories.
Findings show that IA is the central manager in the information transfer process. Mediation is essential to help adapt the knowledge produced by academics and facilitates its adoption and use by the educational community. The efficiency of information access is increased by improving the representation by including more metadata among the information and intelligent techniques that indicate an important aspect of the achieved integration into the access process. The model has good features in providing users with preferences, with a new approach to finding the meaning of a nearby query, and the user can also suggest results pages based on their views.Intelligent Information Retrieval in Industrial Information Storage
Future work will address information abuse from other corporate repositories and digital services and improve proposed queries. It will also extend the system to provide further support, improve and evaluate the system through user testing. It should focus on the design of distributed and self-managed services based on the web and some services, and these are as follows:
• Examine and filter information based on semantic similarity and affinity,
• Can process heterogeneous data, information and intelligence sources,
• Automatically discover, create and integrate heterogeneous components,
• Create, distribute and exploit linked data,
• Automatic and user-oriented application, service orchestration and choreography etc. can realize,


Author: Ozlem Guvenc Agaoglu

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