Semantic Knowledge Extraction Research Articles

A photovoltaic cell defect detection model capable of topological knowledge extraction

As the global transition towards clean energy accelerates, the demand for the widespread adoption of solar energy continues to rise. However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction. Our approach begins with the introduction of a multi-scale dynamic context-based feature extraction method, capable of generating static context by thoroughly capturing the local texture and structural information of multi-scale defects. This static context is then combined with dynamic context to produce fine-grained local features. Subsequently, we developed a centralized feature pyramid structure, enhanced by spatial semantics, which models the explicit visual center. This structure effectively elucidates the relationship between local and global features in defect images, thereby improving the representation of defect characteristics. Finally, we implemented a feature enhancement strategy grounded in spatial semantic knowledge extraction. This strategy uncovers potential correlations among defect targets by constructing a spatial semantic topology of features, mapping these features to a higher-order representation, and ultimately delivering precise defect detection results.

Deep Generation of Synthetic Training Data for the Automated Extraction of Semantic Knowledge from Historical Maps

Deep Generation of Synthetic Training Data for the Automated Extraction of Semantic Knowledge from Historical Maps

Extraction of Event-Related Information from Text for the Representation of Cultural Heritage

In knowledge representation systems for Cultural Heritage (CH) there is a vast amount of curated textual information for CH objects and sites. However, the large-scale study of the accumulated knowledge is difficult as long as it is provided in the form of free text. By extracting the most significant pieces of information from textual descriptions of CH objects and sites and compiling them in a single comprehensive knowledge graph, conforming to a standard would facilitate its exploitation from multiple perspectives including study, presentation and narratives. The method proposed by this research work was to employ Natural Language Processing, and reinforcement learning for semantic knowledge extraction, and a knowledge representation standard of the CH domain for the knowledge graph thus making the extracted knowledge directly compatible with linked open data platforms and CH representation systems.

Automatic semantic knowledge extraction from electronic forms

Electronic tabular forms are an intuitive way for organisations to collect, present and store structured information for human readers. Forms use features such as fonts, colours and cell positioning to help readers navigate and find information. Millions of forms, typically in Portable Document Format (PDF), are generated by businesses as part of routine operations. Unlike human readers, machines are not able to directly ‘understand’ the implicit cues contained in the fonts, colours and use of boxes without explicit processing. In this paper, a supervised computer vision model is proposed to decompose the PDF form document into nested microtables. The cells within these microtables are then processed using a customisable rule bank for meaningful table content and semantic relationship extraction. The process is demonstrated on an industry dataset of 37 maintenance procedure documents containing 373 pages and 1016 unique microtables. A web application EMU (Extracting Machine Understandable Semantics from Forms) demonstrates how data captured in tables with different dimensions in procedural forms can be automatically extracted and stored in JavaScript Object Notation (JSON). Identifying and extracting nested tables is a critical fundamental step for future applications to support machine-automated search and extraction of data at scale for both maintenance and other procedural documentation.

Spatially Adjacent Regions in Posterior Cingulate Cortex Represent Familiar Faces at Different Levels of Complexity.

Extensive research has shown that perceptual information of faces is processed in a network of hierarchically-organized areas within ventral temporal cortex. For familiar and famous faces, perceptual processing of faces is normally accompanied by extraction of semantic knowledge about the social status of persons. Semantic processing of familiar faces could entail progressive stages of information abstraction. However, the cortical mechanisms supporting multistage processing of familiar faces have not been characterized. Here, using an event-related fMRI experiment, familiar faces from four celebrity groups (actors, singers, politicians, and football players) and unfamiliar faces were presented to the human subjects (both males and females) while they were engaged in a face categorization task. We systematically explored the cortical representations for faces, familiar faces, subcategories of familiar faces, and familiar face identities using whole-brain univariate analysis, searchlight-based multivariate pattern analysis (MVPA), and functional connectivity analysis. Convergent evidence from all these analyses revealed a set of overlapping regions within posterior cingulate cortex (PCC) that contained decodable fMRI responses for representing different levels of semantic knowledge about familiar faces. Our results suggest a multistage pathway in PCC for processing semantic information of faces, analogous to the multistage pathway in ventral temporal cortex for processing perceptual information of faces.SIGNIFICANCE STATEMENT Recognizing familiar faces is an important component of social communications. Previous research has shown that a distributed network of brain areas is involved in processing the semantic information of familiar faces. However, it is not clear how different levels of semantic information are represented in the brain. Here, we evaluated the multivariate response patterns across the entire cortex to discover the areas that contain information for familiar faces, subcategories of familiar faces, and identities of familiar faces. The searchlight maps revealed that different levels of semantic information are represented in topographically adjacent areas within posterior cingulate cortex (PCC). The results suggest that semantic processing of faces is mediated through progressive stages of information abstraction in PCC.

Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

Augmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

Construction Methods of Knowledge Mapping for Full Service Power Data Semantic Search System

The power sector continues to accumulate a large amount of data resources, including relevant standard specifications, technical documents, management documents, fault resolution records. How to quickly query and intelligently search these documents is of great value for grid dispatching and fault recovery. The domain search system of traditional power grid is based on keywords, and has the problems of low precision and recall rate. It cannot understand the business language and cannot support semantic reasoning. This paper designs and implements a method based on knowledge mapping to construct the power domain semantic search system. Semantic knowledge extraction of unstructured data is carried out by intelligent domain segmentation technology, organized and stored as knowledge mapping, and semantic search for support reasoning is realized based on knowledge mapping. The process of constructing domain semantic search system is introduced. Experiments show that the accuracy rate and recall rate of the method have been greatly improved.

Verifying and Exploring Settlement Selection Rules and Variables for Small‐Scale Maps Using Decision Tree‐Based Models

In the presented research, the main aim is the assessment of machine learning (ML) techniques usage in the process of acquiring and formalizing generalization rules and variables, understood as settlement features, in settlement generalization. The research specifically addresses the problem of automated settlement selection for 1:1,000,000 scale. We focus on two processes of cartographic knowledge formalization: first, the extraction of semantic and structural knowledge through data enrichment, and second, automated acquisition and application of procedural cartographic knowledge with the use of ML models. This work contributes to extending the toolbox for small‐scale mapping and we concentrate specifically on decision tree‐based models. The main achievements of our research are as follows. First, we verify the existing settlement’s selection rules and variables proposed for 1:1,000,000 detail level by comparing the selection results with the well‐elaborated reference map. Second, with the use of DT‐based models, we make the part of the cartographic knowledge hidden in maps explicit, and show how these models can be used to explore and formulate additional generalization rules and variables important in settlement selection. The research is carried out for the area of Poland but we believe it can be validated and extended to other National Mapping Agencies.

Math-word embedding in math search and semantic extraction

Word embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning.

Disequilibrium multi-dividing ontology learning algorithm

ABSTRACTAs an effective tool for data storage, processing, and computing, ontology has been used in many fields of computer science and information technology. By means of its powerful performance on semantic query and knowledge extraction, domain ontology has been built on various disciplines such as biology, pharmaceutics, geography, chemistry, etc. and been smoothly employed for their engineering applications. In these ontology applications, we aim to get an optimal ontology function which maps each ontology to a real number and then determine the similarity between concepts by the distance of their corresponding real numbers. In former ontology learning approaches, all the instances in the training sample have equal status in the learning process. In this article, we present the disequilibrium multi-dividing ontology algorithm in which the important ontology data will be highlighted during the learning, and the relevant ontology data tend to be eliminated. Four experiments are designed to test the serviceability of our disequilibrium multi-dividing algorithm from angles of ontology similarity measuring and ontology mapping construction.

A survey on Flickr multimedia research challenges

Multimedia content sharing within social networks has become one of the most interesting and trending research fields over the last few years. This undoubted emerge of related research works is rather twofold, namely it includes both the analysis and management techniques of the content itself, as well as new ways for its accompanied meaningful interpretation and exploitation. In this paper, we review the recent advances in the above fields in the humanistic framework of the popular Flickr social network. In addition, the major research challenges in the area are demonstrated and discussed, which include current state-of-the-art approaches with respect to interesting humanistic data collection and interpretation research fields, such as multimedia information retrieval, (semi-) automatic tag manipulation, travel applications, semantic knowledge extraction, human activity tracking, as well as related benchmarking efforts. At the end of this survey, we also discuss the main challenges and propose a number of future research directions for interested fellow researchers to continue investigation in the field.

Knowledge Based Summarization and Document Generation using Bayesian Network

In this paper an approach of Semantic Knowledge Extraction (SKE), from a set of research papers, is proposed to develop a system Summarized Research Article Generator (SRAG) which would generate a summarized research article based on the query given by a user. The SRAG stores the semantic knowledge extracted from the query relevant papers in the form of a semantic tree. Semantic Tree stores all the textual units with their score in nodes organized at different levels depending on their type such as at the bottom leaf nodes keep the words with its probability, the upper level of it represent sentences with its score, next to it paragraphs, segments and so on. Scores of all the entities are calculated in bottom to up manner, first score of words are calculated, based on words sentences are ranked and similarly all the higher levels of the knowledge tree would be scored. A method of Bayesian network is used to generate a probabilistic model which would extract the relevant information from the knowledge tree to generate a summarized article. To maintain coherency, the summarized document is generated segment-wise by combining the most relevant paragraphs. Abstract of a generated summary is shown as a sample result. To show the effectiveness of the algorithm, an intrinsic evaluation strategy, degree of representativeness (DOG) is used. DOG gives on average 50% of relevance of the summary with the source. It's been observed that the proposed approach generates a comprehensive and precise papers.

Natural Language to SQL Generation for Semantic Knowledge Extraction in Social Web Sources

Enormous evolution of web data creates a peculiar myth in the field of computer and information technology for extracting the meaningful content from the web. Many organizations and social networks use databases for storing information and the data will be fetched from the specified data store. Data can be retrieved or accessed by SQL queries whereas the query is in the form of natural lingual statement which has to be processed. So, the primary objective of this research article is to find the suitable way to convert natural language query to SQL and make the data apt for semantic extraction. This Research paper also aims to derive an automatic query translator for Natural Language based questions into their associated SQL queries and provides an user friendly interface between end user and the database for easy access of social web data from different web sources such as facebook, twitter and linkedIn etc.,. This paper is implemented using java as the front end, SQL server as the back end and R-tool is used to collect the data from social web sources. This research article provides an optimized SQL query generation for the Natural Language question provided by the end user.

Semantic Normalization and Query Abstraction Based on SNOMED-CT and HL7: Supporting Multicentric Clinical Trials.

Advances in the use of omic data and other biomarkers are increasing the number of variables in clinical research. Additional data have stratified the population of patients and require that current studies be performed among multiple institutions. Semantic interoperability and standardized data representation are a crucial task in the management of modern clinical trials. In the past few years, different efforts have focused on integrating biomedical information. Due to the complexity of this domain and the specific requirements of clinical research, the majority of data integration tasks are still performed manually. This paper presents a semantic normalization process and a query abstraction mechanism to facilitate data integration and retrieval. A process based on well-established standards from the biomedical domain and the latest semantic web technologies has been developed. Methods proposed in this paper have been tested within the EURECA EU research project, where clinical scenarios require the extraction of semantic knowledge from biomedical vocabularies. The aim of this paper is to provide a novel method to abstract from the data model and query syntax. The proposed approach has been compared with other initiatives in the field by storing the same dataset with each of those solutions. Results show an extended functionality and query capabilities at the cost of slightly worse performance in query execution. Implementations in real settings have shown that following this approach, usable interfaces can be developed to exploit clinical trial data outcomes.

Learning from syntax generalizations for automatic semantic annotation

Nowadays, there is a huge amount of textual data coming from on-line social communities like Twitter or encyclopedic data provided by Wikipedia and similar platforms. This Big Data Era created novel challenges to be faced in order to make sense of large data storages as well as to efficiently find specific information within them. In a more domain-specific scenario like the management of legal documents, the extraction of semantic knowledge can support domain engineers to find relevant information in more rapid ways, and to provide assistance within the process of constructing application-based legal ontologies. In this work, we face the problem of automatically extracting structured knowledge to improve semantic search and ontology creation on textual databases. To achieve this goal, we propose an approach that first relies on well-known Natural Language Processing techniques like Part-Of-Speech tagging and Syntactic Parsing. Then, we transform these information into generalized features that aim at capturing the surrounding linguistic variability of the target semantic units. These new featured data are finally fed into a Support Vector Machine classifier that computes a model to automate the semantic annotation. We first tested our technique on the problem of automatically extracting semantic entities and involved objects within legal texts. Then, we focus on the identification of hypernym relations and definitional sentences, demonstrating the validity of the approach on different tasks and domains.

On the development of a robust, fast and lightweight keypoint descriptor

In this paper we introduce BRAND—Binary Robust Appearance and Normal Descriptor, a novel descriptor which efficiently combines appearance and geometric information from RGB-D images, that is largely invariant to rotation and scale transformations. Based on relevant characteristics of successful image only descriptors, we define a set of eight fundamental requirements to guide the design and evaluation of descriptors that also use depth information. We then describe the design of BRAND, followed by the evaluation of its performance according to those requirements. We also show how BRAND can be simplified in order to obtain a higher performance version, that we named BASE, for applications that require speed performance, but do not demand rigorous scale and rotation invariance.We compare the performance of BRAND against three standard descriptors on real world data. Results of several experiments demonstrate that as far as precision and robustness is concerned, BRAND compares favorably to SIFT and SURF for textured images, and to Spin-Image, for geometrical shape information. Furthermore, BRAND attains improved results when compared to state of the art descriptors that are based either on texture or geometry alone, or on their combination.Finally, we report on the use of BRAND in two applications for which we show that it provides reliable results for the registration of indoor textured depth maps and for object recognition in tasks that require the extraction of semantic knowledge.

Http://www.omicsonline.com/open-access/utilizing-dbpedia-via-cbr-approach-of-recommender-system-2277-1891.1000148.php?aid=19103

Using Linked Open Data Cloud for knowledge extraction is a challenging as well as budding research field. Knowledge extraction is essential work of developing a Recommendation System (RS). Traditional context analyzers of Content Based Recommendation (CBR) are no more sufficient in current web era of semantics. This problem can be removed through generation of features from the logic of semantics presents implicitly in structured format in the web. This promising area merge various domains and technologies namely, semantic web, machine learning, personalization and information retrieval for achieving a good recommendation. This paper discussed about the research questions and challenges that originate from the extraction of semantic knowledge. This semantics can be accessed from a huge open source data cloud that are linked in meaningful way and named as Linked Open Data Cloud (LOD). The implementation includes key methods for how to gain semantically enriched data related to particular items. Paper explains the book domain residing inside the DBpedia datasets, which consist of cross domain information.

Algorithm to Match Ontologies on the Semantic Web

It has been recognized that semantic data and knowledge extraction will significantly improve the capability of natural language interfaces to the semantic search engine. Semantic Web technology offers a vast scale of sharing and integration of distributed data sources by combining information easily. This will enable the user to find the information easily and efficiently. In this paper, we will explore some issues of developing algorithms for the Semantic Web. The first one to build the semantic contextual meaning by scanning the text, extract knowledge and automatically infer the meaning of the information from text that contains the search words in any sentence and correlate with hierarchical classes defined in the Ontology as a result of input resources. The second to discover the hierarchical relationships among terms (i.e. discover the semantic relations across hierarchical classifications). The proposed algorithm will be relying on a number of resources including Ontology and WordNet.

Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

국방 디지털 아카이브의 효율적 연관정보 검색을 위한 자동화된 비즈니스 서비스 식별

정보기술혁명 및 네트워크 기반 정보공유 대중화는 국방 분야 디지털콘텐츠 수를 증가시켰다. 이에 따라 급격하게 그 수가 증가한 장기 보존된 디지털화된 공공정보 가운데 사용자 본인에게 적합한 공공정보를 찾는 것은 매우 중요한 문제로 대두되고 있다. 디지털콘텐츠는 원 자료의 출처 및 생산 시기에 따라 그 형태가 매우 다양하고, 디지털콘텐츠 간에는 많은 상호 연관 관계가 존재한다. 비즈니스 서비스 온톨로지는 공공분야 디지털 아카이브 정보 생산자와 정보 이용자 간의 상호 지식을 명시화하고 지식을 공유할 수 있는 방안을 제공함으로써 디지털 공공정보 검색능력을 증진시키는데 많은 도움을 줄 수 있다. 비즈니스 서비스 온톨로지는 정보 생산자와 정보 이용자 간의 교량과 같은 인터페이스 역할을 수행하는 것이다. 그러나 업무 처리절차에 대한 의미 지식 추출의 어려움으로 인하여, 비정형의 업무 활동들로부터 정형화된 비즈니스 서비스로의 매핑 정보를 제공하는 비즈니스 서비스 온톨로지 자동화는 실용화하기가 매우 힘든 실정이다. 이러한 문제점을 해결하고자 본 연구에서는 비즈니스 서비스 온톨로지 구축을 위한 첫 단계로써 전사적 아키텍처(ITA/EA)로부터 단위 비즈니스 서비스 식별 자동화 방안을 제안한다.