Web Intelligence

Author(s):  
Jafreezal Jaafar ◽  
Kamaluddeen Usman Danyaro ◽  
M. S. Liew

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

Big Data ◽  
2016 ◽  
pp. 711-733 ◽  
Author(s):  
Jafreezal Jaafar ◽  
Kamaluddeen Usman Danyaro ◽  
M. S. Liew

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xuhui Li ◽  
Liuyan Liu ◽  
Xiaoguang Wang ◽  
Yiwen Li ◽  
Qingfeng Wu ◽  
...  

Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.


Author(s):  
Souad Bouaicha ◽  
Zizette Boufaida

Although OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web, they do have expressive limitations. For some reasoning problems, it is necessary to modify existing knowledge in an ontology. This kind of problem cannot be fully resolved by OWL and SWRL, as they only support monotonic inference. In this paper, the authors propose SWRLx (Extended Semantic Web Rule Language) as an extension to the SWRL rules. The set of rules obtained with SWRLx are posted to the Jess engine using rewrite meta-rules. The reason for this combination is that it allows the inference of new knowledge and storing it in the knowledge base. The authors propose a formalism for SWRLx along with its implementation through an adaptation of different object-oriented techniques. The Jess rule engine is used to transform these techniques to the Jess model. The authors include a demonstration that demonstrates the importance of this kind of reasoning. In order to verify their proposal, they use a case study inherent to interpretation of a preventive medical check-up.


2020 ◽  
Vol 389 ◽  
pp. 218-228
Author(s):  
Neha Bharill ◽  
Aruna Tiwari ◽  
Aayushi Malviya ◽  
Om Prakash Patel ◽  
Akahansh Gupta ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Taimoor Khan ◽  
Mehr Durrani ◽  
Shehzad Khalid ◽  
Furqan Aziz

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.


Author(s):  
Javed Khan ◽  
Manas Hardas

The recent advances in knowledge engineering entail us to represent knowledge associated with a course in an expressive yet computable format as a hierarchical prerequisite relation-based weighted ontology. A schema called the course concept dependency schema written in Web ontology language (OWL) is designed to represent the prerequisite concept dependency. The knowledge associated with educational resources, like the knowledge required for answering a particular test question correctly, can be mapped to subgraphs in the course ontology. A novel approach for selectively extracting these subgraphs is given and some interesting inferences are made by observing the clustering of knowledge associated with test questions. We argue that the difficulty of a question is not only dependent on the knowledge it tests but also the structure of the knowledge it tests. Some assessment parameters are defined to quantify these properties of the knowledge associated with a test question. It is observed that the parameters are very good indicators of question difficulty.


2013 ◽  
Vol 14 (1) ◽  
pp. 80-87
Author(s):  
Olegs Verhodubs ◽  
Janis Grundspenkis

Abstract The main purpose of this paper is to present an algorithm of OWL (Web Ontology Language) ontology transformation to concept map for subsequent generation of rules and also to evaluate the efficiency of this algorithm. These generated rules are necessary to supplement and even to develop SWES (Semantic Web Expert System) knowledge base. This paper is a continuation of the earlier research of OWL ontology transformation to rules.


2019 ◽  
Vol 8 (3) ◽  
pp. 7664-7673

Fuzzy knowledge-based systems are successfully applied in several areas to classify and modelling the knowledge base using fuzzy If then rules. In recent era, taking the loan from banking system is highly practiced and the finding the eligible person to grant the credit is challenging task. In this context, this article designed a fuzzy knowledge base system and defined eight rules for credit allocation system and implemented on two different dataset German credit allocation system and Australian credit allocation system. These data are downloaded from well-known machine learning repository UCI. To classify the credit allocation data, fuzzy decision tree and Wang and Mendel model has been used. To estimate the performance of the proposed method for credit allocation system the accuracy and the interpretability is used. The experimental analysis highlight that the Wand and Mendel model gives higher accuracy i.e. 99.9% and the interpretability of the proposed model is very less or negligible


Author(s):  
Nowshade Kabir ◽  
Elias Carayannis

In the process of conducting everyday business, organizations generate and gather a large number of information about their customers, suppliers, competitors, processes, operations, routines and procedures. They also capture communication data from mobile devices, instruments, tools, machines and transmissions. Much of this data possesses an enormous amount of valuable knowledge, exploitation of which could yield economic benefit. Many organizations are taking advantage of business analytics and intelligence solutions to help them find new insights in their business processes and performance. For companies, however, it is still a nascent area, and many of them understand that there are more knowledge and insights that can be extracted from available big data using creativity, recombination and innovative methods, apply it to new knowledge creation and produce substantial value. This has created a need for finding a suitable approach in the firm’s big data related strategy. In this paper, the authors concur that big data is indeed a source of firm’s competitive advantage and consider that it is essential to have the right combination of people, tool and data along with management support and data‐oriented culture to gain competitiveness from big data. However, the authors also argue that organizations should consider the knowledge hidden in the big data as tacit knowledge and they should take advantage of the cumulative experience garnered by the companies and studies done so far by the scholars in this sphere from knowledge management perspective. Based on this idea, a big data oriented framework of organizational knowledge‐based strategy is proposed here.


2013 ◽  
Vol 373-375 ◽  
pp. 1027-1030
Author(s):  
Wan Li ◽  
Bi Hua Zhou ◽  
Qi Zhang ◽  
Ya Peng Fu ◽  
Tao Wang

According to knowledge sharing&reusing problem and system extensibility&portability problem in meteorological and hydrological support, the knowledge concepts, attributes, instances, hierarchy and relationships were clarified; the ontological knowledge base was established by OWL ontology language and SWRL rule language. With the help of Racer, Protégé, Jess, and class positioning algorithm, class testing algorithm, rule testing algorithm, the meteorological and hydrological support prototype system was implemented by Eclipse, ProtegeInEclipse plug-in and Jena. The system includes the server, the client and the processing controller. It provides artificial intelligence techniques means for knowledge maintenance and usage in meteorological and hydrological field.


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