A Review on the Knowledge Representation Models and its Implications

V, Hepsiba Mabel.; Selwyn, Justus

doi:10.5815/ijitcs.2016.10.09

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Internet of Things is another wide area, where we have greater and real-time challenges to deal with data from electronic devices, those which could be unstructured or semistructured [22]. This work on the review of knowledge representation models will lead knowledge workers and researchers to choose from these models or to propose newer models to manipulate and process data and information to acquire, extract, represent and codify knowledge.…”

Section: Data Modeling On Approved and Ban Drugsmentioning

confidence: 99%

Drugs Relationship Discovery using Hypergraph

Ramanathan¹,

Mohan²

2018

IJITCS

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Abstract-Globally, the governments are concerned for saving human life but in recent days, the cost of medicine to save the human life grows rapidly, due to the different combination of the drugs sold by the drug store and based on the drugs availability in the drug store. As a regular routine, the Clinical physicians prescribe the drug name or druggist company name for the patient, instead of prescribing the brand name. For a particular drug name, more number of brand name both generic drugs and combination drugs were available. Almost all the physicians or druggist knows these generic drugs and combination drugs along with their dosage and recommends it for the patient. Since the patients are unaware about the brand name of the medicine, they are buying the costly drug without knowledge. At the same time the patients whoever provide the review on the drug consumed, mostly gives a negative review. On the other side, Government gives approval for every drug produced year by year and those drugs getting rejected for approval where sent for new alternative extension of chemical formula, as well government bans several combinations of drugs. There should be a database for awareness; it should help physicians, druggist and end user to look for banned and unbanned drug. We have identified that the drug details and their approvals were not maintained in the database. So, the proposed work is (i) to identify and classify the exact drug, which can be used as generic or combination and (ii) to create a banned drug model representation, which was unavailable.A hypergraph-based model named Drugs Relationship Discovery using Hypergraph (DRDH) was constructed as a preliminary design representation for drugs. Here, the drugs combination was considered as a hypervertex and the combination approved for manufacturing in the particular company was considered as an edge, having weight as the price of single dose usage based on Indian government listed drug. With this proposed model as a reference, the banned drugs are listed separately, manufacturing stopped drugs are highlighted with the weight of -1 and the physician or druggist can access the model to ensure the correct drug, their production, and usage. The Time complexity has been analyzed for the hypergraph, which was constructed based on the details of relationship identified among the drugs. Using the parameters of the list of drugs used in India on fever and the common cold, comparison over time taken to identify the relationship has also been done.

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Section: Data Modeling On Approved and Ban Drugsmentioning

confidence: 99%

Drugs Relationship Discovery using Hypergraph

Ramanathan¹,

Mohan²

2018

IJITCS

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“…Semantic information discovery approaches [2,3] are now challenging traditional keyword-based information retrieval methods. This retrieval problem is further burdened by the poor quality of the metadata content in many digital collections.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Brisebois¹,

Abran²,

Nadembéga³

2017

IJITCS

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Abstract-Information systems need to be more flexible and to allow users to find content related to their context and interests. Metadata harvesting and metadata enrichments could represent a way to help users to find content and events according to their interests. However, metadata are underused and represents an interoperability challenge. This paper presents a new framework, called SMESE, and the implementation of its prototypes that consists of its semantic metadata model, a mapping ontology model and a user interest affinity model. This proposed framework makes these models interoperable with existing metadata models.SMESE also proposes a decision support process supporting the activation and deactivation of software features related to metadata. To consider context variability into account in modeling context-aware properties, SMESE makes use of an autonomous process that exploits context information to adapt software behavior using an enhanced metadata framework. When the user chooses preferences in terms of system behavior, the semantic weight of each feature is computed. This weight quantifies the importance of the feature for the user according to their interests. This paper also proposed a semantic metadata analysis ecosystem to support data harvesting according to a metadata model and a mapping ontology model. Data harvesting is coupled with internal and external enrichments. The initial SMESE prototype represents more than 400 millions of relationships (triplets). To conclude, this paper also presents the design and implementation of different prototypes of SMESE applied to digital ecosystems.Index Terms-Metadata, metadata enrichment, metadata model, ontology, semantic metadata enrichment, software ecosystem.

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Intelligent Information Systems Based on Notional Models Without Relationships

Vykhovanets

2021

Advances in Intelligent Systems, Computer Science and Digital Economics II

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A Review on the Knowledge Representation Models and its Implications

Cited by 3 publications

References 11 publications

Drugs Relationship Discovery using Hypergraph

Drugs Relationship Discovery using Hypergraph

A Semantic Metadata Enrichment Software Ecosystem based on Metadata and Affinity Models

Intelligent Information Systems Based on Notional Models Without Relationships

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