Research on Ontology-Based Multi-source Engineering Information Retrieval in Integrated Environment of Enterprise

Information retrieval (IR) is the field of computer science that deals with the processing of documents containing free text, so that they can be rapidly retrieved based on keywords specified in a user's query. IR was born in the 1950s out of necessity to find useful information from large collections. Over the last sixty years, the field has matured considerably. IR technology is the basis of Web-based search engines, and plays a vital role in research, because it is the foundation of software that supports literature search. Several IR systems are used on an everyday basis by a wide variety of users. This article is a brief overview of Information Retrieval. Keywords: information retrieval, Web-based search engine, universal repositories, filtering and precision

Due to the increasing number of digital document repositories there is a heavy demand for information retrieval systems and therefore, information retrieval is still appearing as an emerging area of research. The information retrieval technology these days focuses on achieving better performance under different context by extracting documents most appropriate to the user’s query. Majority of the classical keyword based retrieval techniques does not focus on semantic meanings and therefore, are found to be less effective in reconstructing the actual information conveyed in the context. Also, retrieval of the relevant documents depends on appropriate analysis of the query terms. As words are polysemic, their actual meanings are influenced by their relationships with other words and their syntactic roles in the sentence. This work presents a fuzzy-cluster based semantic information retrieval model that considers these relationships to determine the exact meaning of the user query and extracts relevant documents as per their relevance scores.

In information retrieval, vocabulary mismatch between the search vocabulary and documents vocabulary has been a common challenge for web users, hindering information access and retrieval. This issue is often attributed to the ambiguous representation of user information needs as queries, leading to the retrieval of many irrelevant documents, particularly for non-skilled web users. This paper aims to improve user query representation for effective retrieval of relevant documents from the web. To achieve this, a query expansion strategy was employed to identify terms with similar meanings to the user’s initial query term. The similarities between the expanded terms and the initial user query term were determined by calculating the cosine angle between the vector resenting document vocabulary and the vector representing query term. Thereafter, Formal Concept Analysis (FCA) was employed to analyze and present the results. Findings from the analysis revealed that concatenating similar terms with the initial user query terms resulted in a 0.16% improvement, as evident in the retrieval precision of 0.64% with the initial query and 0.80% with the expanded query terms.

The rapid development of information technology offers people a broad information-sharing platform. We can get relevant information through information retrieval tools. In order to get higher search efficiency, the information retrieval technologies based on semantic grid are discussed. Based on analysis of the technologies of grid, semantic grid and information retrieval, the architecture of grid information retrieval system is brought out and a common model of grid information retrieval is established. The grid platform based on Globus toolkit is built which is made as the basic development platform of the system, the semantic grid information retrieval system under that platform is realized, and the designed model and algorithm are implemented and the specific experimental tests is realized. The result shows that the system has better retrieval recall factor and pertinency factor than the conventional retrieval methods, and the feasibility of system model is verified.

Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers. Familiarizing ontology as information retrieval (IR) aids in augmenting the searching effects of user-required relevant information. The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet, mapping the connection between keywords and information, and categorizing the retrieval outcomes. The prevailing procedures for IR consume considerable time, and they could not recover information proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), the IR time is mitigated, and the retrieval accuracy is enhanced for trouncing the above-stated downsides. The proposed method encompasses three phases: i) development of a crop ontology, ii) implementation of the IR system, and iii) processing of user query. In the initial phase, a crop ontology is developed and evaluated by gathering crop information. In the next phase, a hash tree is constructed using closed frequent patterns (CFPs), and MNFA is used to train the database. In the last phase, for a specified user query, CFP is calculated, and similarity assessment results are retrieved using the database. The performance of the proposed system is measured and compared with that of existing techniques. Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.

This Article discusses the impacts of the introduction of a data ownership scheme on access to information. It does so with reference to the distinction between data, defined as some representation of information by a specific medium (the syntactic level), and the information itself (the semantic level). With respect to data, it then looks at the difference between standard property rights and intellectual property (IP) rights, similarly assessing impacts on access to information. Private control at the semantic level is bad policy as it effects particularly serious limitations, relative to private control at the syntactic level, on the availability of information in society. IP-type rights similarly effect more serious limitations on the availability of information in society than do standard property rights over data. Focusing on the syntactic-semantic divide to discuss the data ownership case clarifies a few things. First, although there are reasons to be more solicitous of the case for data ownership for data subjects than the case for data ownership for others, there must be an acknowledgment of the fact that the former case often requires data ownership over the information itself. The two premier rationales for granting data ownership to data subjects are that such rights would enable data subjects to protect their privacy, and that it would enable them to claim a share in the benefits of the data economy. This Article points out that any effective attainment of these rationales for data ownership for data subjects requires that data subjects be empowered to exclude others from information even before its collection—that is, even before its representation in syntactic form. This is exclusion power over semantic information. Second, the data ownership for data subjects case is less objectionable in the context of control at the syntactic level as such control is less likely to lead to indirect semantic-level control. By contrast, control at the syntactic level by data collectors creates the problem of indirect semantic control. There is, therefore, no meaningful case for data ownership for data collectors, even at the syntactic level. But the reduced likelihood of indirect semantic-level control in the context of data subjects means that there are sometimes prospects for propertarian rights for data subjects over data being structured to avoid semantic control. What emerges from this understanding is a pattern in which policy supports some syntactic bases for a propertarian remedy for data subjects, but otherwise counsels against data ownership. Finally, those bases for a propertarian remedy over data for data subjects are limited to discrete areas where data ownership for data subjects can be deployed with little, or no, control being exerted at the semantic level, and therefore at little to no cost in public access to information. This is likely to be especially true in the context of standard property as semantic-level control by data subjects is more likely to be a non-issue in this case.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

A general-purpose, interactive computerized information retrieval system specially suitable for the retrieval of bibliographic information from a small scale database. (DB) containing up to 15000 records is described. The user defines fields in terms of tags through the data definition processor which provides flexibility for the record structure. Another processor creates the DB from an off-line input file containing the tag(s) followed by the data for the field(s) supplied by the user. Retrieval of information is carried out by the query processor in an interactive manner. It provides options to (i) select a descriptor (the postings are displayed; (ii) combine 2 sets at a time; (iii) display the different sets chosen; and iv) print the chosen set(s) with full record details or specified tag fields only. The package is developed on an Usha-Alpha 1042 system using Alpha BASIC language under AMOS operating system.

With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval scenarios, implicit reasoning cues within clinical data often hinder current models from effectively capturing deep semantic associations between queries and cases. Query rewriting and expansion techniques based on reasoning offer a promising solution to this challenge by uncovering and completing the latent clinical intent behind user queries, thereby enhancing semantic coverage and reasoning sensitivity. In this paper, we propose CRAF, a clinically adaptive reasoning framework tailored for similar case retrieval. Our method generates clinical reasoning paths and incorporates a fine-grained semantic reward mechanism, enabling efficient query rewriting through reinforcement learning. Experimental results on the PMC-Patients benchmark demonstrate that CRAF consistently delivers robust improvements across multiple retrieval tasks, achieving reasoning performance comparable to that of commercial models.

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

To satisfy the user information needs, the use of his/her profile is adopted in the information retrieval process. Our goal is to build and exploit the user profile in search process. To achieve this goal, we adopt a Bayesian network as a strategy for personalized search since it provides a clear formalism to model qualitatively and quantitatively information types, namely, a user query, documents, index terms, and user's interests. In this paper, we present a Bayesian network model that captures the relationships between this information to evaluate the relevance as matching relationships between documents, user's interest and query concepts. An experimental evaluation was designed to appraise the exploitation impact of the user profile defined by his/her interests on the search results relevance.

We consider the task of entity search and examine to which extent state-of-art information retrieval (IR) and semantic web (SW) technologies are capable of answering information needs that focus on entities. We also explore the potential of combining IR with SW technologies to improve the end-to-end performance on a specific entity search task. We arrive at and motivate a proposal to combine text-based entity models with semantic information from the Linked Open Data cloud.

This paper focuses on the research of media knowledge text translation based on information retrieval, and discusses how information retrieval technology can affect and optimize the translation process and results of media texts. By combining the basic principles of information retrieval technology and its practical application scenarios, this paper analyzes the characteristics of media texts and their special requirements for translation. Through the design experiment, the effect of information retrieval technology on improving translation quality, efficiency and reaction cost is evaluated. The experimental results show that this technology can significantly improve the accuracy and fluency of translation, shorten the translation cycle and reduce the cost. The application prospect of information retrieval in the field of translation will be broader. This study can not only provide a new perspective and method for the translation of media texts, but also contribute to the improvement of cross-cultural communication and information dissemination efficiency.

In recent years, information retrieval technology is widely used in the medical industry. How to effectively retrieve electronic medical record has become a hot issue in the field of information retrieval. Medical terms occupy an important position in the electronic medical record (EMR) retrieval, and they are usually used to limit the retrieval conditions, so they suggest the user’s search intention. Aiming at the importance of medical terms, a method of query reformulation based on medical term reweighting is proposed to improve the performance of EMR retrieval. Firstly, the method filtrates medical terms from the origin query. Then, each medical term is weighted by its own self-information which can be estimated from the document set. Finally, the new query is constructed by combining the weighted medical terms and origin query proportionally. Experimental results on the TREC dataset show that our method performed above the baseline in three performance metrics: MAP (+ 14.2%), bpref (+ 8.0%), and P@10 (+ 9.6%).

Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically. However, it is a great challenge to apply these technologies to biomedicine domain directly because of some domain specific characteristics, such as the abundance of terminologies. To enhance the effectiveness of the biomedical information retrieval, we propose a novel framework based on the state-of-the-art information retrieval methods, called learning to rank, which has been proved effective to rank documents based on their relevance degree. In the framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate our proposed framework is effective in improving the performance of biomedical information retrieval.