Knowledge Of Entities Research Articles

Construction and Application of the User Behavior Knowledge Graph in Software Platforms

The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.

Intelligent pig‐raising knowledge question‐answering system based on neural network schemes

AbstractThe most important parts of Taiwan's agriculture are animal husbandry and the pig industry, of which the output value reached NT$75.6 billion in 2017. Taiwan has a high technical level of pig raising. However, practical pig‐raising skills rely mainly on the inheritance of mentoring experience. The livestock and pig breeding industry in Taiwan has no relevant pig breeding knowledge management information system and no intelligent knowledge question‐answering system. Therefore, this study designs and implements an intelligent knowledge question‐and‐answer system for pig farming. To identify intelligent questions and answers for raising livestock pigs, this study addresses the following issues: (a) to determine the semantic meaning of a sentence, the system needs to accurately interpret the meaning of a question and to identify the expression of a knowledge entity. Therefore, this study applies the lattice long‐short‐term memory (LSTM) and structured perceptron methods to parse sentences accurately and correctly perform word segmentation. A set of stop words for pig raising in Taiwan is initially established to ensure accurate sentence parsing; (b) to understand the intent of each sentence, the bidirectional gated recurrent init (bi‐GRU) method is adopted to realize the knowledge extraction of the livestock in the question and complete the intent detection and slot filling. The bi‐GRU extracts the correct livestock knowledge and classifies it into suitable topics; (c) the wide range of knowledge sources in questions often leads to unrelated vocabulary, repeated words, and structural loss in potential answers. To establish an effective knowledge search graph, the method infers implicit knowledge from the existing knowledge and conducts related knowledge retrieval based on the inference results. The knowledge data model is defined in the open standard resource description framework Knowledge filtering and knowledge reasoning strategies are presented to produce candidate answers to the livestock problem; (d) to obtain the final answer, most related works search established knowledge graphs, often producing multiple ambiguous candidate answers. The Siamese neural network (SNN) is adopted to obtain accurate answers. An SNN compares candidate answers to a livestock problem as two input values, previously trained by the bidirectional LSTM neural network. The final answer is determined from the cosine similarity value, which represents the highest relevance to the question. Finally, this study implements the intelligent pig‐raising knowledge question‐answering system based on the proposed methodology. The evaluation results reveal that the proposed system is accurate and practicable.

WAx: An integrated conceptual framework for the analysis of cyber-socio-technical systems

Modern work domains are constituted by an intertwined set of social and technical actors with different, often conflicting, functional purposes. These agents act jointly to ensure system’s functioning under both expected and unexpected working conditions. Considering the increasing digitalization and automation of work processes, socio-technical systems are progressively including interconnected cyber technical artefacts, thus becoming cyber-socio-technical systems (CSTSs). Adopting a natural science perspective, this paper aims to explore knowledge creation and knowledge conversion within CSTSs, as rooted in an in-depth analysis of work practices and work contexts. The paper proposes a conceptual framework which unveils the relationships between different work representations, i.e. relying on Work-As-Imagined, Work-As-Done, Work-As-Disclosed, Work-As-Observed, intended as knowledge entities generated by different agents, i.e. sharp-end operators, blunt-end operators, and analysts. The recursive and fractal nature of the proposed WAx (Work-As-x) framework ensures its adaptability for different granularity levels of analysis, fostering the understanding, modeling, and analysis of work practices, while abandoning reductionist and over-simplistic approaches.

Automatic recommendation of medical departments to outpatients based on text analyses and medical knowledge graph

In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients.

Constructing Knowledge Graphs for Online Collaborative Programming

This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class achieved 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.

A Continual Learning Survey: Defying Forgetting in Classification Tasks.

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern: (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods; and (4) baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage.

Mobile shopping apps adoption: a systematic literature review

The study aims to contribute by evaluating and synthesising the findings across various strata of populations in developed and developing countries and by unifying the diverse streams of research into a more coherent and cohesive knowledge entity. The theoretical models and determinants of M-shopping apps adoption intentions and behaviours from selected papers in literature are reviewed to synthesise the findings and identify the gaps and contradictions. Recommendations for future research are made as regards to theoretical approaches, research methodology, anchor constructs, consumer profiles to be researched, and technological and marketing perspectives are to be explored. The research findings in literature are limited by exclusive focus on smartphones, selective geographical regions, theoretical models, consume profiles and methodological approaches and hence not generalisable.

Research on Medical Question Answering System Based on Knowledge Graph

To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system.

Mobile shopping apps adoption: a systematic literature review

The study aims to contribute by evaluating and synthesising the findings across various strata of populations in developed and developing countries and by unifying the diverse streams of research into a more coherent and cohesive knowledge entity. The theoretical models and determinants of M-shopping apps adoption intentions and behaviours from selected papers in literature are reviewed to synthesise the findings and identify the gaps and contradictions. Recommendations for future research are made as regards to theoretical approaches, research methodology, anchor constructs, consumer profiles to be researched, and technological and marketing perspectives are to be explored. The research findings in literature are limited by exclusive focus on smartphones, selective geographical regions, theoretical models, consume profiles and methodological approaches and hence not generalisable.

Locating Personal Pedagogical Content Knowledge of Science Teachers within Stories of Teaching Force and Motion

A narrative inquiry approach was adapted to study teacher’s experiences of planning and teaching force and motion topics. Oral narrative data were collected through interview conversations between the researcher and the teachers about their experiences of planning and teaching force and motion concepts. Narrative analysis technique suggested by Polkinghorne, was employed to develop stories of teaching force and motion—that acceded access to their pPCK. and comprised of small entities of knowledge—pedagogical constructions, which are narrative fragments. Each pedagogical construction was placed on a four-level PCK continuum to assess the breadth and depth of each teacher’s pPCK. A mapping technique was devised to illustrate pPCK of each participant teacher, and a comparative analysis of these illustrations reveals fascinating similarities and differences apparently grounded in individual teacher’s subject area background and their specific teaching experiences. Implications for pre-service science teacher education are discussed.

Community answer generation based on knowledge graph

Community Question Answering (CQA) has become an indispensable way for modern people to share and acquire knowledge. It allows users to ask questions, which will be answered by experienced users enthusiastically. By recording user operation logs, CQA has accumulated a large amount of valuable and complex data. However, askers must wait (usually for a long time) until other expert users answer their questions on social platforms. This will seriously affect the user experience. In this paper, we propose a Community Answer Generation method based on the Knowledge Graph, called CAGKG, to generate natural language answers automatically. Firstly, we extract the core phrases of posts to represent their semantics relations. Then, we model the user's knowledge background based on their action records. Finally, we query knowledge entities in a knowledge graph based on user background and question semantics, then convert them into natural language answers. Besides, we proposed a Phrase-based Answers Semantic Similarity Evaluation indicator, called PASSE, which focuses on the semantic similarity between texts instead of literal matching. To the best of our knowledge, it is the first work that utilizes the user knowledge and text semantics to improve the performance of CQA. Experiments on four real datasets (Stack Overflow, Super User, Mathematics, and Quora) show that CAGKG is superior to the state-of-the-art question answering frameworks. Compared with other answer evaluation indicators, PASSE is a promising indicator for evaluating semantic similarity.

Research and Application of Knowledge Graph in Teaching: Take the database course as an example

With the rapid development of technologies such as artificial intelligence and deep learning, education informatization has entered the 2.0 era with artificial intelligence as the main feature. As an important part of artificial intelligence technology, knowledge graph provides possibilities for smart education and promotes the innovation and development of smart education. At this stage, the core concepts and knowledge systems of some computer science disciplines need further clarification and improvement. Using a large amount of course-related information to construct an educational knowledge graph, processing and analyzing the knowledge points in the course, extracting the knowledge entities and effectively integrating them, can greatly help to clarify the knowledge system of the subject. The improvement of the teaching quality of this subject is of great significance. Therefore, this paper selects the construction of educational knowledge graph as research content, and uses database courses as examples of graph construction for research. The BIO tagging method is used to construct the database subject dataset, at the same time, it builds knowledge card based on educational knowledge graph to achieve database teaching wisdom, systematic teaching content, integration of knowledge fragments and improve the quality of learning.

Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis

This paper focuses on the problem of student knowledge diagnosis that is a basic task of realizing personalized education. Most traditional methods rely on the question-concept matrix empirically designed by experts. However, the expert concepts are expensive and inter-overlapping in their constructions, leading to ambiguous explanations. With the intuition that each student can master a part of the knowledge involved in all questions, in this paper, we propose a novel learning-based model for student knowledge diagnosis, dubbed Meta-knowledge Dictionary Learning (metaDL). MetaDL aims to learn a meta-knowledge dictionary from student responses, where any knowledge entity (e.g., student, question or expert concept) is a linear combination of a few atoms in the meta-knowledge dictionary. The resultant problem could be effectively solved by developing the alternating direction method of multipliers. This study has three innovations: learning independent meta-knowledges instead of traditional complex concepts, sparely representing knowledge entity instead of densely weighted representation, and interpreting expert concepts with the resulting meta-knowledges. For evaluation, the diagnosis results from metaDL are used to group students and predict responses on two public datasets and a private dataset from our institution. The experiment results show that metaDL delivers an effective student knowledge diagnosis and then results in good performances on the two applications in comparison with other methods. This technique could provide significant insights into student’s knowledge state and facilitate the progress on personalized education.

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.

Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction

• Preferential attachments are prevalent in knowledge network growth. This article analyzes temporal co-occurrences of author selected keywords in scientific literature to support emerging Literature Based Discovery (LBD) by viewing the process as supervised learning problem. • By mining temporal evolution of keyword co-occurrences networks (KCN) and prominence of keywords over time with regards to their edge formation, network neighborhood, this article defined genealogical communities of keywords. • To predict the future co-evolution of author selected keywords in scientific literature, the feature construction process analyzed both bipartite network (keywords-authors and keywords- articles) and normalized unipartite network (keywords-keywords) including relative importance of the citation counts accrued by the author selected keywords over time. • The prediction performances were compared against features extracted from homogeneous and heterogeneous networks (heterogeneous bibliographic information network) to demonstrate the competency of the constructed features in predicting future scientific hypotheses in two research domains. Literature-based discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval (IR) and Natural Language Processing (NLP) attempt to identify the hidden or unpublished connections between information concepts within published literature, however, these techniques overlooked the concept of predicting the future and emerging relations among scientific knowledge components such as author selected keywords encapsulated within the literature. Keyword Co-occurrence Network (KCN), built upon author selected keywords, is considered as a knowledge graph that focuses both on these knowledge components and knowledge structure of a scientific domain by examining the relationships between knowledge entities. Using data from two multidisciplinary research domains other than the bio-medical domain, and capitalizing on bibliometrics, the dynamicity of temporal KCNs, and a recurrent neural network, this study develops some novel features supportive for the prediction of the future literature-based discoveries - the emerging connections (co-appearances in the same article) among keywords. Temporal importance extracted from both bipartite and unipartite networks, communities defined by genealogical relations, and the relative importance of temporal citation counts were used in the feature construction process. Both node and edge-level features were input into a recurrent neural network to forecast the feature values and predict the future relations between different scientific concepts/topics represented by the author selected keywords. High performance rates, compared both against contemporary heterogeneous network-based method and preferential attachment process, suggest that these features complement both the prediction of future literature-based discoveries and emerging trend analysis.

Study on Construction of Emergent Public Health Event Data Platform Based on Knowledge Entity

Study on Construction of Emergent Public Health Event Data Platform Based on Knowledge Entity

An algorithmic approach to rank the disambiguous entities in Twitter streams for effective semantic search operations

The most challenging task in any modern reasoning system is that it has been completely relied on automatic knowledge acquisition from the unstructured text and filtering out the structured information from it has turned out to be the most crucial task of Information Retrieval systems. In this paper, we have proposed a system that can recognize the potential named entities from the Twitter streams and link them to the appropriate real world knowledge entities. Besides, it has performed many semantic functions such as entity disambiguation, contextual similarity, type induction, and semantic labeling, to augment the semantic score of the entity and provide the rich entity feature space to quantitatively enhance entity retrieval accuracy. Nevertheless, we have leveraged a model to alleviate the entity imbalance present over the collected Twitter Streams and effectively utilized the contextual relatedness between the candidate entity sets. Eventually, we have proposed a probabilistic approach to deal with topic modeling and effectively disambiguate the entities by clustering the entities into its appropriate entity domain. The proposed Latent Dirichlet Allocation (LDA) model has been categorically distinguished the topics for clustering between the candidate entities and fix the exact true mentions occurred in the Knowledge Base such as DBpedia. We have also demonstrated the performance and accuracy rate of the proposed system and evaluated the results with the collected Twitter Streams for the month of August, 2016. The empirical results have shown that it has outperformed the existing state-of-the-art systems and proved that the proposed system given here has gradual accuracy rate against the conventional systems.

Impact of Accounting Software among SMEs Accountants in Oman

Financial losses, bankruptcy and closure of the company may be the result of incorrect choice of accounting software, inefficient modernization of such software depending on the specifics of the economic entity and ignorance of technical knowledge of staffs to work with the software product. The paper notes that for companies from member countries of the Gulf Cooperation Council, the technique of implementation and application of tax legislation and International Financial Reporting Standards (IFRS) differs significantly from other countries. The article emphasizes that in Oman, companies need to prepare financial statements in accordance with current applicable IFRS, as well as the Law on Commercial Companies 2019 and the guidelines and requirements for disclosure of capital market information. The purpose of this paper is to study and study the impact of the implementation of accounting software among small and medium enterprises (SMEs) in Oman. The study systematizes the features and issues of assessing the relationship between generalized accounting software (GAS) and its use by accountants working for SMEs. Twenty small and medium business accountants were selected as the target audience, taking into account their experience and basic knowledge of accounting in the context of ownership and use of GAS. The study confirms and theoretically proves that the use of GAS in the financial and economic activities of SMEs has a significant impact on the practice of accountants working in such enterprises, ie, there is a positive and significant relationship between GAS choice and use of GAS by SME accountants. The results of this study can be useful for the government, representatives of tax authorities, higher education institutions in the context of establishing adequate policies regarding the use of software for accounting by economic entities. Keywords: Generalized accounting software, accounting, accountant, small and medium enterprises, international financial reporting standards (IFRS), Oman.

MEIM: A Multi-source Software Knowledge Entity Extraction Integration Model

Entity recognition and extraction are the foundations of knowledge graph construction. Entity data in the field of software engineering come from different platforms and communities, and have different formats. This paper divides multi-source software knowledge entities into unstructured data, semi-structured data and code data. For these different types of data, Bi-directional Long ShortTerm Memory (Bi-LSTM) with Conditional Random Field (CRF), template matching, and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model (MEIM) to extract software entities. The model can be updated continuously based on user’s feedbacks to improve the accuracy. To deal with the shortage of entity annotation datasets, keyword extraction methods based on Term Frequency–Inverse Document Frequency (TF-IDF), TextRank, and K-Means are applied to annotate tasks. The proposed MEIM model is applied to the Spring Boot framework, which demonstrates good adaptability. The extracted entities are used to construct a knowledge graph, which is applied to association retrieval and association visualization.

Academic Approach for Religious Radicalism

Blaming or demonizing other could appear from misunderstanding paradigm toward knowledgeable entities: individually or collectively. Inclusive approach to know and understand other religious groups is a basic principle to build interreligious relation. In his Dialogue Decalogue, Leonard Swidler suggests that to build interfaith dialogue is to learn and understand (other) realities. In this regard, religious radicalism could be perceived as an outcome of misunderstanding that allows people to exclude or ignore others. This paper aims to explore several books of Muslim scholars, both in classical and contemporary period, relating to comparative study of religion. It is the finding of this work that understanding other religions (or religious others) as articulated in the book will create a level of acceptance within religious believers.