Automated Knowledge Discovery Research Articles

Content Automation in Marketing Research: A Bibliometric Analysis using VOSviewer

Objectives: This study conducted a comprehensive assessment of content automation in marketing research using bibliometric analysis spanning 20 years from 2004 to 2024. The primary influential factors, topics, and areas of research were analyzed to determine future research paths. Methods/Analysis: Sample selection and data collection were conducted using the Scopus database. The initial dataset was adjusted by applying certain inclusion and exclusion criteria. The final dataset consisting of 149 articles in the RIS format was loaded into the VOSviewer program to perform a bibliometric analysis. Findings: The results revealed key findings, including the highest citation-counting papers, the most common research format, the research field, the year of publication, collaboration countries, the journal, prominent authors, popular themes, areas for further investigation, and the intellectual framework of current research on the topic. Novelty/ Improvement:Four areas were identified for future research: marketing automation as digital commerce content marketing, artificial intelligence for digital marketing transformation, automation and optimization for personalized advertising content, and automatic knowledge discovery. The temporal development of these issues was also examined to offer valuable insights into the changing focus of academic interest over time and establish a foundation for future investigations. Doi: 10.28991/HIJ-2024-05-03-019 Full Text: PDF

Toward Automated Knowledge Discovery in Case-Based Reasoning

Automated Case Elicitation (ACE) enables case-based reasoning (CBR) systems to automatically acquire knowledge through real-time exploration and interaction with environments. CBR is an explainable AI methodology, where decisions are based on previous encounters. ACE combined with CBR continues learning as it is being deployed, and produces specific cases that can be reviewed by humans, unlike pretrained large language models (LLMs) that learn by training offline on prior data. ACE and CBR may be useful methods to gather training data for use with generative AI, or to help them to adapt on the fly. This research explores ACE's potential by applying it to chess and conducting extensive experiments against Stockfish, the world's highest rated chess engine. An ACE agent was developed that combines random exploration with shallow alpha-beta search for novel game states. Results over 1000+ games showed the ACE player defeated Stockfish in nearly 10% of games—a notable achievement given Stockfish's extreme strength. Notably, the ACE agent required only 0.1 seconds per game compared an average of 8 minutes for Stockfish, while still gradually improving its win rate through accrued experience. Detailed analyses revealed how the relaxation of ACE's case matching criteria along with selective retention of useful cases enabled accumulation of strategic chess knowledge. The research provides valuable insights into ACE's proficiency for knowledge discovery in complex, adversarial domains. It lays groundwork for integrating ACE, an unsupervised CBR learner, with modern deep learning techniques like neural networks and large language models to combine the strengths of symbolic and subsymbolic AI. By demonstrating ACE's ability to extract strategic knowledge against world-class opponents, this work highlights its potential for impact across gaming, autonomous systems, and other complex problem-solving domains.

The automated discovery of kinetic rate models – methodological frameworks

Two automated knowledge discovery methodologies (ADoK-S & ADoK-W) are created whereby symbolic regression, parameter estimation, information criteria and model-based design of experiments synergize for the optimized discovery of kinetic rate models.

Digital humanities on the Semantic Web: Sampo model and portal series

Cultural heritage (CH) contents are typically strongly interlinked, but published in heterogeneous, distributed local data silos, making it difficult to utilize the data on a global level. Furthermore, the content is usually available only for humans to read, and not as data for Digital Humanities (DH) analyses and application development. This application report addresses these problems by presenting a collaborative publication model for CH Linked Data and six design principles for creating shared data services and semantic portals for DH research and applications. This Sampo model has evolved gradually in 2002–2021 through lessons learned when developing the Sampo series of linked data services and semantic portals in use, including MuseumFinland (2004), CultureSampo (2009), BookSampo (2011), WarSampo (2015), Norssit Alumni (2017), U.S. Congress Prosopographer (2018), NameSampo (2019), BiographySampo (2019), WarVictimSampo 1914–1922 (2019), MMM (2020), AcademySampo (2021), FindSampo (2021), WarMemoirSampo (2021), and LetterSampo (2022). These Semantic Web applications surveyed in this paper cover a wide range of application domains in CH and have attracted up to millions of users on the Semantic Web, suggesting feasibility of the proposed Sampo model. This work shows a shift of focus in research on CH semantic portals from data aggregation and exploration systems (1. generation systems) to systems supporting DH research (2. generation systems) with data analytic tools, and finally to automatic knowledge discovery and Artificial Intelligence (3. generation systems).

Abstract 5366: Constructing the largest-scale biomedical knowledge graph using all PubMed articles and its application in automated knowledge discovery

Abstract The number of biomedical publications is growing at an accelerated speed. This ever-increasing amount of scientific literature has made reading all the published articles regularly impossible even for a very specific research area. A solid grasp of existing literature is essential for coming out with novel and plausible scientific ideas. To bridge the gap between the published scientific findings and our incapability of manually processing them, we need to convert the unstructured text into structured form to enable automated methods to use the structured, machine-readable information to generate novel hypotheses, which can then be manually validated. A plausible approach for converting unstructured text into structured form is to use named entity recognition (NER) and relation extraction (RE) methods to identify the biological entities and extract their relations to construct knowledge graphs (KGs). KGs can link concepts within existing research to allow researchers to find connections that may have been difficult to discover without them. The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical, publication-free text to create KGs. Our team participated in the challenge and ranked first place. Using the pipelines developed for the LitCoin NLP challenge, we have constructed the largest-scale biomedical KG using all PubMed articles. We further develop advanced deep-learning methods to predict new links from the constructed KG. We demonstrate the power of this new framework using several examples important for drug discovery. Citation Format: Yuan Zhang, Feng Pan, Xin Sui, Donghu Sun, Menghan Chung, Jinfeng Zhang. Constructing the largest-scale biomedical knowledge graph using all PubMed articles and its application in automated knowledge discovery. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5366.

Abstract 5365: Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries

Abstract Millions of new papers are published in biomedical sciences every year. In many disciplines, it has become impossible to read all the published new papers to learn what is happening in the frontier of a particular area. This gap is widening as the publishing speed has been accelerating in recent years. To address this challenge, one can convert unstructured text data into a structured form, which can then support highly accurate information retrieval, information integration, and automated knowledge discovery. A plausible approach for such a task is to use named entity recognition (NER) and relation extraction (RE) methods to identify important biological entities and extract their relations to construct knowledge graphs (KGs). The LitCoin Natural Language Processing (NLP) Challenge was recently organized by NCATS of NIH and NASA to spur innovation by rewarding the most creative and high-impact uses of biomedical text to create KGs. In addition to entities and relations, the manually annotated LitCoin dataset also contains the annotations of relations being new discoveries or background knowledge. Our team participated in the challenge and ranked first place. The novelty prediction model of our pipeline has achieved an F1 score of 0.90. We have applied our model to all the PubMed abstracts published previously and the newly published ones to extract the novel discoveries in each article. A web portal has been created to allow scientists to view the latest discoveries in cancer research. The web portal is updated daily with versatile visualization tools for cancer researchers to quickly grasp the latest discoveries in a particular area. It also offers powerful functions to explore the existing literature and make sophisticated inferences about causal and indirect relationships. Citation Format: Feng Pan, Yuan Zhang, Xin Sui, Donghu Sun, Menghan Chung, Jinfeng Zhang. Extracting novel knowledge from scientific literature to build a web portal for cancer researchers to keep up with the latest scientific discoveries. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5365.

Recent text-based research and applications in railways: A critical review and future trends

In the railway industry, a significant amount of data is stored in the textual format. The advanced development of natural language processing and text mining techniques enable automatic knowledge extraction and discovery from such documents. This paper presents a systematic review with quantitative and qualitative analyses to understand the current state of text-based research in the context of railway transport. The paper collects 107 relevant publications in the past decade and identifies different channels for researchers to obtain text data in railways and the corresponding text analysis application use-cases. Moreover, a comprehensive analysis is performed on the state-of-the-art machine learning and natural language processing methods. Four key research directions, namely multilingual NLP, digital maintenance, external data integration, and railway-centred solution pipeline, are identified from Siemens Mobility’s perspective to highlight the most prominent challenges faced in the railway industry.

Discovering Precursors to Tropical Cyclone Rapid Intensification in the Atlantic Basin Using Spatiotemporal Data Mining

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification.

KnowMore: an automated knowledge discovery tool for the FAIR SPARC datasets

Background: This manuscript provides the methods and outcomes of KnowMore, the Grand Prize winning automated knowledge discovery tool developed by our team during the 2021 NIH SPARC FAIR Data Codeathon. The National Institutes of Health Stimulating Peripheral Activity to Relieve Conditions (NIH SPARC) program generates rich datasets from neuromodulation researches, curated according to the Findable, Accessible, Interoperable, and Reusable (FAIR) SPARC data standards. Currently, the process of simultaneously comparing and analyzing multiple SPARC datasets is tedious because it requires investigating each dataset of interest individually and downloading all of them to conduct cross-analyses. It is crucial to enhance this process to enable rapid discoveries across SPARC datasets. Methods: To fill this need, we created KnowMore, a tool integrated into the SPARC Portal that only requires the user to select their datasets of interest to launch an automated discovery process. KnowMore uses several SPARC resources (Pennsieve, o²S²PARC, SciCrunch, protocols.io, Biolucida), data science methods, and machine learning algorithms in the back end to generate various visualizations in the front end intended to help the user identify potential similarities, differences, and relations across the datasets. These visualizations can lead to a new discovery, new hypothesis, or simply guide the user to the next logical step in their discovery process. Results: The outcome of this project is a SPARC portal-ready code architecture that helps researchers to use SPARC datasets more efficiently and fully leverages their FAIR characteristics. The tool has been built and documented such that more data analysis methods and visualization items could be easily added. Conclusions: The potential for automated discoveries from SPARC datasets is huge given the unique SPARC data ecosystem promoting FAIR data practices, and KnowMore has only demonstrated a small highlight of what could be achieved to speed up discoveries from SPARC datasets.

Digging in Documents: Using Text Mining to Reveal Hidden Knowledge in the Lithium-Ion Battery Literature

Scientific publications constitute the main information source of battery-related data for academic research. There is an impressive number of papers published on Lithium Ion Batteries (LIBs) for instance. Such impressive amount of data in principle could be used by machine learning (ML) methods in order to optimize efforts and even make possible the discovery of new optimal experimental conditions. Yet, the majority is published as unstructured data, making ML analysis difficult. Here, in order to unravel researchers’ habits when reporting their results, we present a text mining study using primarily publications containing original experimental data from the LIB field. We analyze the reported data structure and we discuss the remaining challenges for applying automated knowledge discovery techniques. We then present a Named Entity Recognition approach to automatically extract quantified parameters from the battery literature and how to build an in house generated database containing a wide diversity of materials, experimental parameters and conditions. Our in house approach achieves a data extraction F-scores ≥ 90% and can thereby pave the way towards an automatized way of generating large databases for use in battery ML models. References El-Bouysidy, H., Lombardo, T., Primo, E. N., Duquesnoy, M., Morcrette, M., Johansson, P., Simon, P., Grimaud, A., & Franco, A. A. submitted (2020).Torayev, Amangeldi, Pieter CMM Magusin, Clare P. Grey, Céline Merlet, and Alejandro A. Franco. Journal of Physics: Materials 2, no. 4 (2019): 044004.

Decoding and interpreting cortical signals with a compact convolutional neural network

Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.

Orbit Computation for Atomically Generated Subgroups of Isometries of $\mathbb{Z}^n$

Partitions of a set play an important role in automatic knowledge discovery algorithms that learn human-like concepts from human-like innate priors. One such type of prior is isometries, the recogn...

A computational ecosystem to support eHealth Knowledge Discovery technologies in Spanish.

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents.

Automatic identification of knowledge related to dengue cases in the state of Piauí in public databases using Filtered-Association Rules Networks

Dengue is an endemic disease in Brazil since the 1980s and since 1996 in Piau ́ı. The number of cases increases each year, with the incidence of more severe symptoms. This research aimed to evaluate the use of an automatic knowledge identification technique in factors related to the number of dengue occurrences. We built a dataset formed by data available in the Information System for Notifiable Diseases (SINAN) and meteorological data of the municipalities of the coastal plain of Piau ́ı. The technique used was that of Filtered Association Rules Networks, which allows visual analysis of knowledge through the use of network structures and rules filtering. As a main result, we confirmed the understanding that the most significant number of cases occurs in May, as it is the moment when the rainfall indexes are decreasing, besides that socio-cultural and race factors do not interfere in the identification of the population of higher risk. This research presents the innovation of the use of a computational technique of automatic knowledge discovery that can assist in the elaboration of prevention actions by epidemiological surveillance.

A Knowledge Graph of Combined Drug Therapies Using Semantic Predications From Biomedical Literature: Algorithm Development.

BackgroundCombination therapy plays an important role in the effective treatment of malignant neoplasms and precision medicine. Numerous clinical studies have been carried out to investigate combination drug therapies. Automated knowledge discovery of these combinations and their graphic representation in knowledge graphs will enable pattern recognition and identification of drug combinations used to treat a specific type of cancer, improve drug efficacy and treatment of human disorders.ObjectiveThis paper aims to develop an automated, visual approach to discover knowledge about combination therapies from biomedical literature, especially from those studies with high-level evidence such as clinical trial reports and clinical practice guidelines.MethodsBased on semantic predications, which consist of a triple structure of subject-predicate-object (SPO), we proposed an automated algorithm to discover knowledge of combination drug therapies using the following rules: 1) two or more semantic predications (S1-P-O and Si-P-O, i = 2, 3…) can be extracted from one conclusive claim (sentence) in the abstract of a given publication, and 2) these predications have an identical predicate (that closely relates to human disease treatment, eg, “treat”) and object (eg, disease name) but different subjects (eg, drug names). A customized knowledge graph organizes and visualizes these combinations, improving the traditional semantic triples. After automatic filtering of broad concepts such as “pharmacologic actions” and generic disease names, a set of combination drug therapies were identified and characterized through manual interpretation.ResultsWe retrieved 22,263 clinical trial reports and 31 clinical practice guidelines from PubMed abstracts by searching “antineoplastic agents” for drug restriction (published between Jan 2009 and Oct 2019). There were 15,603 conclusive claims locally parsed using the search terms “conclusion*” and “conclude*” ready for semantic predications extraction by SemRep, and 325 candidate groups of semantic predications about combined medications were automatically discovered within 316 conclusive claims. Based on manual analysis, we determined that 255/316 claims (78.46%) were accurately identified as describing combination therapies and adopted these to construct the customized knowledge graph. We also identified two categories (and 4 subcategories) to characterize the inaccurate results: limitations of SemRep and limitations of proposal. We further learned the predominant patterns of drug combinations based on mechanism of action for new combined medication studies and discovered 4 obvious markers (“combin*,” “coadministration,” “co-administered,” and “regimen”) to identify potential combination therapies to enable development of a machine learning algorithm.ConclusionsSemantic predications from conclusive claims in the biomedical literature can be used to support automated knowledge discovery and knowledge graph construction for combination therapies. A machine learning approach is warranted to take full advantage of the identified markers and other contextual features.

A systematic review on literature-based discovery workflow.

As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to the input component, we discuss the data types and data sources used in the literature. The process component presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, the output component focuses on the visualisation techniques used in LBD discipline. As for the evaluation component, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions.

Data Pre-Processing and Customized Onto-Graph Construction for Knowledge Extraction in Healthcare Domain of Semantic Web

Present electronic world produces enormous amount of data every second in various formats, especially in healthcare units. To efficiently utilize the available data by representing it in the machine readable form, the concept of Semantic web stepped in progressing towards automated knowledge discovery process. In this paper, comprehensive pre-processing techniques have been proposed for preparing the raw data to be presentable in structured format so as to construct the onto-graph for selected features in a health care domain. Cluster based Missing Value Imputation Algorithm (CMVI) has been proposed to enhance the quality of the imputed data which is the most important step during data pre-processing. Missing values were randomly induced into the Pima Indian Diabetic dataset with the missing ratio of 1%, 3% and 5% for each attribute up to 50% of the attributes in the original diabetic dataset. The experimental observations reveal that the quality of the pre-processed data is better compared to raw, unprocessed data in terms of imputation accuracy measured against coefficient of determination (R2), Index of agreement (d2) and Root Mean Square Error (RMSE).Documented results proved that the proposed techniques are comparatively superior than the traditional approaches with increased R2 & d2 and decreased RMSE scores. Further, importance of knowledge graph and various ontological representation types are discussed in short as construction of .owl file is the first step towards automation in semantic web.

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal ("may cause") relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.

Natural language processing for music knowledge discovery

ABSTRACTToday, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago. In this work, we present different Natural Language Processing (NLP) approaches to harness the potential of these text collections for automatic music knowledge discovery, covering different phases in a prototypical NLP pipeline, namely corpus compilation, text-mining, information extraction, knowledge graph generation, and sentiment analysis. Each of these approaches is presented alongside different use cases (i.e. flamenco, Renaissance and popular music) where large collections of documents are processed, and conclusions stemming from data-driven analyses are presented and discussed.

A SURVEY ON NEW APPROACHES OF INTERNET OF THINGS DATA MINING

The huge information created by the Internet of Things (IoT) is taken for high business appraisals, and data mining calculations can be related to IoT to separate hidden data from data. IoT data mining in learning view , method view and application view along with grouping, bunching, time management and exception analysis. Many number of devices are associated with IoT, massive amount of volume data producing every day.Data Mining is a process of mining required data from databases in efficient way and obtaining optimized results. In subsequently Internet of Things (IoT) Middleware applications allow data user to retrieve and read the data without even knowing about devices ,signaling ,communication and technical issues in IoT resources data processing . Configuring an IoT middleware layer and retrieving data is a big challenge for data consumers .In this paper, we survey on how IoT data can be produce and in such away automated knowledge discovery from data storage .