ISTRAŽIVANJE O POVEZIVANJU ENTITETA ZA SPECIFIČNE DOMENE S HETEROGENIM INFORMACIJSKIM MREŽAMA

Entity linking is a task of extracting information that links the mentioned entity in a collection of text with their similar knowledge base as well as it is the task of allocating unique identity to various entities such as locations, individuals and companies. Knowledgebase (KB) is used to optimize the information collection, organization and for retrieval of information. Heterogeneous information networks (HIN) comprises multiple-type interlinked objects with various types of relationship which are becoming increasingly most popular named bibliographic networks, social media networks as well including the typical relational database data. In HIN, there are various data objects are interconnected through various relations. The entity linkage determines the corresponding entities from unstructured web text, in the existing HIN. This work is the most important and it is the most challenge because of ambiguity and existing limited knowledge. Some HIN could be considered as a domain-specific KB. The current Entity Linking (EL) systems aimed towards corpora which contain heterogeneous as web information and it performs sub-optimally on the domain-specific corpora. The EL systems used one or more general or specific domains of linking such as DBpedia, Wikipedia, Freebase, IMDB, YAGO, Wordnet and MKB. This paper presents a survey on domain-specific entity linking with HIN. This survey describes with a deep understanding of HIN, which includes datasets,types and examples with related concepts.Povezivanje entiteta je zadatak izvlačenja podataka koji povezuju spomenuti entitet u zbirci teksta sa njihovom sličnom bazom znanja, kao i zadatak dodjeljivanja jedinstvenog identiteta različitim entitetima, kao što su lokacije, pojedinci i tvrtke. Baza znanja (BZ) koristi se za optimizaciju prikupljanja, organizacije i pronalaženja informacija. Heterogene mreže informacija (HMI) obuhvaćaju višestruke međusobno povezane objekte različitih vrsta odnosa koji postaju sve popularniji i nazivaju se bibliografskim mrežama, mrežama društvenih medija, uključujući tipične podatke relacijske baze podataka. U HMI-u postoje razni podaci koji su međusobno povezani kroz različite odnose. Povezanost entiteta određuje odgovarajuće entitete iz nestrukturiranog teksta na webu u postojećem HMI-u. Ovaj je rad najvažniji i najveći izazov zbog nejasnoće i postojećeg ograničenog znanja. Neki se HMI mogu smatrati BZ-om specifičnim za domenu. Trenutni sustav povezivanja entiteta (PE) usmjeren je prema korpusima koji sadrže heterogene informacije kao web informacije i oni djeluju suptimalno na korpusima specifičnim za domenu. PE sustavi koristili su jednu ili više općih ili specifičnih domena povezivanja, kao što su DBpedia, Wikipedia, Freebase, IMDB, YAGO, Wordnet i MKB. U ovom radu predstavljeno je istraživanje o povezivanju entiteta specifičnog za domenu sa HMI-om. Ovo istraživanje opisuje s dubokim razumijevanjem HMI-a, što uključuje skupove podataka, vrste i primjere s povezanim konceptima

Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table

Using a literature based discovery approach to study moderating effects and preventive mechanisms of online game addiction problems among gamers

This research makes use of a hypothesis generation technique known as Literature-Based Discovery (LBD) to study certain under-researched topics related to online game addiction problems in our society. This research work tries to primarily address two problems related to game addiction which have been under-studied according to the literature available on this topic. In the first problem, we try to identify certain moderating factors of massively multiplayer games which weakens the relationship between the psychological flow state and game addiction. In this data-driven approach, we studied 2829 abstracts to generate a list of keywords that suggest potential moderating factors for MMO games. Then interview data from 3 domain experts are used to support our findings from the LBD method. The results suggest that the keywords help us to identify alternative pathways (e.g., escapism, cognitive mechanisms and identification with avatars) to game addiction which have received less research attention. In the second problem, we tried to identify potentially effective but under-studied measures that help prevent the negative effects of online game addiction among children and adolescents. We searched the abstracts of 876 articles using Literature-Based discovery and applied association rule mining to identify negative effects and preventive mechanisms of game addiction among children and adolescents in the age group of 8-19. We then tried to rank the relationship between these negative effects and preventive mechanisms by using the measure of ‘w-support’. This helped us identify some preventive mechanisms which have been under-studied in the game addiction literature along with the corresponding negative effects they address. Finally, we have computed the effective size of one such under-studied preventive mechanism to show that it is equally effective as other popular preventive mechanisms. This approach gives us an important future direction in the study and design of preventive programs to address online game addiction issues. In both the problems, we use LBD methodology to either identify alternative pathways to supplement a more popular theory, as in the MMO game study or we used LBD to suggest some under-studied relationships, that can be further explored to design effective preventive mechanisms of online game addiction in children and adolescents. Thus, with the help of LBD as a research methodology, we try to overcome the challenges of knowledge overspecialization in certain areas and identify important future directions of research on the causes and prevention of online game addiction

Prevention and Reversal of Peripheral Neuropathy/Peripheral Arterial Disease

This monograph presents a five-step treatment protocol to prevent and reverse Peripheral Neuropathy (PN)/Peripheral Arterial Disease (PAD), based on the following systemic medical principle: at the present time, removal of cause is a necessary, but not necessarily sufficient, condition for restorative treatment to be effective. Implementation of the five-step PN/PAD treatment protocol is as follows: Step 1: Obtain a detailed medical and habit/exposure history from the patient. Step 2: Administer written and clinical performance and behavioral tests to assess the severity of the higher-level symptoms and degradation of executive functions Step 3: Administer laboratory tests (blood, urine, imaging, etc) Step 4: Eliminate ongoing PN/PAD contributing factors Step 5: Implement PN/PAD treatments This individually-tailored PN/PAD treatment protocol can be implemented with the data currently available in the biomedical literature. Additionally, while the methodology developed for this study was applied to comprehensive identification of diagnostics, contributing factors, and treatments for PN/PAD, it is general and applicable to any chronic disease/condition that, like PN/PAD, has an associated substantial research literature. Thus, the protocol and methodology developed to prevent or reverse PN/PAD can be used to prevent or reverse any chronic disease (with the possible exceptions of individuals with strong genetic predispositions to the disease in question or who have suffered irreversible damage from the disease)