scholarly journals Backward chaining inference as a database stored procedure — the experiments on real-world knowledge bases

2017 ◽  
Author(s):  
Roman Siminski ◽  
Tomasz Xieski
Keyword(s):  
Real World ◽  
Knowledge Bases ◽  
World Knowledge ◽  
Backward Chaining ◽  
Stored Procedure

DATAtourist

2021 ◽  
Vol 13 (2) ◽  
pp. 62-84
Author(s):  
Boudjemaa Boudaa ◽  
Djamila Figuir ◽  
Slimane Hammoudi ◽  
Sidi mohamed Benslimane
Keyword(s):  
Software Engineering ◽  
Knowledge Acquisition ◽  
Software Architecture ◽  
Recommender Systems ◽  
Real World ◽  
Mobile Application ◽  
Knowledge Bases ◽  
User Requirements ◽  
World Knowledge ◽  
Points Of Interest

Collaborative and content-based recommender systems are widely employed in several activity domains helping users in finding relevant products and services (i.e., items). However, with the increasing features of items, the users are getting more demanding in their requirements, and these recommender systems are becoming not able to be efficient for this purpose. Built on knowledge bases about users and items, constraint-based recommender systems (CBRSs) come to meet the complex user requirements. Nevertheless, this kind of recommender systems witnesses a rarity in research and remains underutilised, essentially due to difficulties in knowledge acquisition and/or in their software engineering. This paper details a generic software architecture for the CBRSs development. Accordingly, a prototype mobile application called DATAtourist has been realized using DATAtourisme ontology as a recent real-world knowledge source in tourism. The DATAtourist evaluation under varied usage scenarios has demonstrated its usability and reliability to recommend personalized touristic points of interest.

scholarly journals Entity Alignment between Knowledge Graphs Using Attribute Embeddings

2019 ◽  
Vol 33 ◽  
pp. 297-304 ◽  
Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Large Numbers ◽  
Proposed Model ◽  
Alignment Task ◽  
Transitivity Rule ◽  
Knowledge Graphs

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

scholarly journals A Method of Ontology Integration for Designing Intelligent Problem Solvers

2019 ◽  
Vol 9 (18) ◽  
pp. 3793 ◽  
Author(s):  
Do ◽  
Nguyen ◽  
Mai
Keyword(s):  
Real World ◽  
Knowledge Integration ◽  
Knowledge Bases ◽  
World Knowledge ◽  
Knowledge Domain ◽  
Ontology Integration ◽  
Knowledge Based ◽  
Ontology Model ◽  
Problem Solvers ◽  
Integrated Knowledge

Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform better in those college courses.

scholarly journals Analisa Quarterlife crisis Mahasiswa Dan Mahasiswi Tingkat Akhir STMIK Royal Kisaran Dengan Backward Chaining

2021 ◽  
Vol 2 (2) ◽  
pp. 158-165
Author(s):  
Rivia Gulda Nasution ◽  
Hanina Hanina
Keyword(s):  
Real World ◽  
Backward Chaining

Sistem pakar membentuk kecerdasan buatan yang mempelajari suatu aspek atau banyak aspek ilmu dari seorang pakar maupun banyak pakar agar dapat mengadopsi pemahaman manusia ke komputer dan komputer dapat menyelesaikan masalah seperti yang biasa dilakukan oleh para pakar. Hambatan yang paling sering dialami oleh mahasiswa dan mahasiswi tingkat akhir yatitu munculnya perasaan panik, ketidakstabilan serta kebimbangan karena banyaknya pilihan bahkan frustasi yang sering disebut sebagai Quarterlife crisis. Quarterlife crisis ialah suatu respon emosional yang ditandai dengan munculnya perasaan panik, tidak berdaya, ketidakstabilan, kebimbangan karena banyaknya pilihan, cemas, tertekan, bahkan frustrasi, yang dialami oleh individu pada rentang usia 18-29 tahun, terprimer ketika akan atau baru menyelesaikan pendidikan di bangku kuliah dan menangkili real world yang penuh tantangan dan tuntutan yakni fresh graduate dan juga mahasiswa tingkat akhir. kerelevanan komputerisasi dan teknologi dalam sistem pakar analisa quarterlife crisis pada mahasiswa tingkat akhir ini dibutuhkan guna membantu mahasiswa menangkili quarterlife crisis dan mendapatkan solusi mengenai masalahnya. Kemajuan teknologi inilah yang dapat membantu mengalihkan pemahaman manusia ke dalam bentuk sistem sehingga dapat digunakan oleh banyak orang dan tidak terbatas oleh waktu tanpa menggantikan peran manusia.

Symbols Without Context

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Data Base ◽  
Machine Translation ◽  
Real World ◽  
Language Translation ◽  
Digital Data ◽  
Meaningful Difference ◽  
World Knowledge ◽  
Similarities And Differences ◽  
Words And Images ◽  
The Way

Humans have invaluable real-world knowledge because we have accumulated a lifetime of experiences that help us recognize, understand, and anticipate. Computers do not have real-world experiences to guide them, so they must rely on statistical patterns in their digital data base—which may be helpful, but is certainly fallible. We use emotions as well as logic to construct concepts that help us understand what we see and hear. When we see a dog, we may visualize other dogs, think about the similarities and differences between dogs and cats, or expect the dog to chase after a cat we see nearby. We may remember a childhood pet or recall past encounters with dogs. Remembering that dogs are friendly and loyal, we might smile and want to pet the dog or throw a stick for the dog to fetch. Remembering once being scared by an aggressive dog, we might pull back to a safe distance. A computer does none of this. For a computer, there is no meaningful difference between dog, tiger, and XyB3c, other than the fact that they use different symbols. A computer can count the number of times the word dog is used in a story and retrieve facts about dogs (such as how many legs they have), but computers do not understand words the way humans do, and will not respond to the word dog the way humans do. The lack of real world knowledge is often revealed in software that attempts to interpret words and images. Language translation software programs are designed to convert sentences written or spoken in one language into equivalent sentences in another language. In the 1950s, a Georgetown–IBM team demonstrated the machine translation of 60 sentences from Russian to English using a 250-word vocabulary and six grammatical rules. The lead scientist predicted that, with a larger vocabulary and more rules, translation programs would be perfected in three to five years. Little did he know! He had far too much faith in computers. It has now been more than 60 years and, while translation software is impressive, it is far from perfect. The stumbling blocks are instructive. Humans translate passages by thinking about the content—what the author means—and then expressing that content in another language.

Incremental Knowledge Construction for Real-World Event Understanding

2010 ◽  
Vol 4 (1) ◽  
pp. 65-79 ◽  
Author(s):  
Koji Kamei ◽  
Yutaka Yanagisawa ◽  
Takuya Maekawa ◽  
Yasue Kishino ◽  
Yasushi Sakurai ◽  
...  
Keyword(s):  
Real World ◽  
Knowledge Construction ◽  
A Priori ◽  
Sensor System ◽  
World Knowledge ◽  
Incremental Model ◽  
Proposed Model

The construction of real-world knowledge is required if we are to understand real-world events that occur in a networked sensor environment. Since it is difficult to select suitable ‘events’ for recognition in a sensor environment a priori, we propose an incremental model for constructing real-world knowledge. Labeling is the central plank of the proposed model because the model simultaneously improves both the ontology of real-world events and the implementation of a sensor system based on a manually labeled event corpus. A labeling tool is developed in accordance with the model and is evaluated in a practical labeling experiment.

Real World Knowledge for Databases

1992 ◽  
Vol 3 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Veda C. Storey
Keyword(s):  
Real World ◽  
World Knowledge

Reasoning on the Semantic Web

2011 ◽  
pp. 110-133
Author(s):  
R. Brussee
Keyword(s):  
Real World ◽  
Description Logic ◽  
World Knowledge ◽  
Closed World Assumption ◽  
Sharp Distinction ◽  
Ontology Language ◽  
Closed World ◽  
Present Information ◽  
Logical Modeling ◽  
Case Reasoning

We describe reasoning as the process needed for using logic. Efficiently performing this process is a prerequisite for using logic to present information in a declarative way and to construct models of reality. In particular we describe description logic and the owl ontology language and explain that in this case reasoning amounts to graph completion operations that can be performed by a computer program. We give an extended example, modeling a building with wireless routers and explain how such a model can help in determining the location of resources. We emphasize how different assumptions on the way routers and buildings work are formalized and made explicit in our logical modeling, and explain the sharp distinction between knowing some facts and knowing all facts (open vs. closed world assumption). This should be helpful when using ontologies in applications needing incomplete real world knowledge.

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