Semantic Information Retrieval on Medical Texts

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.

Fake News Detection using Data Mining Techniques

Nowadays, internet has been well known as an information source where the information might be real or fake. Fake news over the web exist since several years. The main challenge is to detect the truthfulness of the news. The motive behind writing and publishing the fake news is to mislead the people. It causes damage to an agency, entity or person. This paper aims to detect fake news using semantic search.

IMPLEMENTATION OF THE APPROACH TO ONTOLOGICAL MODELING OF THE MUSICAL SUPERGENRE “METAL”

Background. The relevance of the article is due to the development of modern ontological methods of structuring information and the need to systematize data in many new specific subject areas. Such subject areas include the musical art of the "metal" variety, which is quite common today, but insufficiently studied within the terminology. The subject of the article are ontological models and tools for creating ontological knowledge bases. Objective. The purpose of the paper is to increase the correctness of the semantic search in the knowledge base of the musical supergenre "metal". The scientific problem is the need to improve the terminology in this subject area and build an ontological knowledge model that increases the accuracy of information retrieval for the target audience, compared to the existing relational model implemented on one of the known web resources. Methods. Classification method, generalization method, software optimization methods, analytical method. The way to solve the problem: selection based on the comparative characteristics of the best web resource of the subject area and identifying the shortcomings of its model of knowledge representation, designing an ontological knowledge model and testing its effectiveness. Results. The average SUM for all users is 83.85%, which is a good indicator for ontological knowledge bases. At the same time, a similar method of checking the database of the supergenre "metal" on the basis of the site "Encyclopedia Metallum", which used the classical relational model of database organization, showed much lower results. Thus, the average SUM for 10 users was 75.32%, respectively. Conclusions. The scientific novelty of the obtained results is as follows: For the first time an ontological model (ontology) of the subject area was created: musical supergenre "metal", which showed much higher efficiency of semantic search than the best relational model of this subject area, implemented as a web resource. The developed structure can be used to create ontologies of related musical supergenres with similar terminology. Future research also plans to integrate this ontological knowledge model with applied web-based and desktop applications.

Semantic Reasoning of Product Biologically Inspired Design Based on BERT

Bionic reasoning is a significant process in product biologically inspired design (BID), in which designers search for creatures and products that are matched for design. Several studies have tried to assist designers in bionic reasoning, but there are still limits. Designers’ bionic reasoning thinking in product BID is vague, and there is a lack of fuzzy semantic search methods at the sentence level. This study tries to assist designers’ bionic semantic reasoning in product BID. First, experiments were conducted to determine the designer’s bionic reasoning thinking in top-down and bottom-up processes. Bionic mapping relationships, including affective perception, form, function, material, and environment, were obtained. Second, the bidirectional encoder representations from transformers (BERT) pretraining model was used to calculate the semantic similarity of product description sentences and biological sentences so that designers could choose the high-ranked results to finish bionic reasoning. Finally, we used a product BID example to show the bionic semantic reasoning process and verify the feasibility of the method.

Semantic Search and Summarization of Judgments Using Topic Modeling

Online legal document libraries, such as WorldLII, are indispensable tools for legal professionals to conduct legal research. We study how topic modeling techniques can be applied to such platforms to facilitate searching of court judgments. Specifically, we improve search effectiveness by matching judgments to queries at semantics level rather than at keyword level. Also, we design a system that summarizes a retrieved judgment by highlighting a small number of paragraphs that are semantically most relevant to the user query. This summary serves two purposes: (1) It explains to the user why the machine finds the retrieved judgment relevant to the user’s query, and (2) it helps the user quickly grasp the most salient points of the judgment, which significantly reduces the amount of time needed by the user to go through the returned search results. We further enhance our system by integrating domain knowledge provided by legal experts. The knowledge includes the features and aspects that are most important for a given category of judgments. Users can then view a judgement’s summary focusing on particular aspects only. We illustrate the effectiveness of our techniques with a user evaluation experiment on the HKLII platform. The results show that our methods are highly effective.

Semantic Search to Foster Scientific Findability: A Systematic Literature Review

One of the main goals of the Open Science movement is to leverage scientific collaboration through, among others, promoting the sharing and reuse of research outputs, such as publications, data and software. Sharing is enabled by public and accessible scientific repositories where these outputs are managed throughout their lifecycle. In this context, finding these digital artifacts has become a key problem. Semantic search mechanisms have risen as a means to solve this issue. However, implementing and integrating them into scientific repositories presents many challenges. This article presents a systematic literature review of research efforts on mechanisms for supporting search for scientific papers, data and processes. Our investigation is based on extracting and analyzing the entire contents of nine digital libraries using the associated search engines – in alphabetical order: ACM Digital Library, arXiV, Engineering Village, IEEE Xplore, SBC OpenLib, Springer Link, Scopus, Wiley Online Library and Web of Science. After retrieving a combined amount of 5012 documents, we identified 2054 unique papers that were used as a basis for our analysis. Our findings provide, among others, a new categorization of literature on search and discuss unexplored gaps, thereby contributing to advancing research on semantic search mechanisms to support Open Science.