A Framework for Modelling Real-World Knowledge Capable of Obtaining Answers to Fuzzy and Flexible Searches

2015 ◽  
pp. 281-297
Author(s):  
Víctor Pablos-Ceruelo ◽  
Susana Munoz-Hernandez
Keyword(s):  
Real World ◽  
World Knowledge

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.

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

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.

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.

An Ontology Based Framework for Intelligent Web Based e-Learning

2015 ◽  
Vol 11 (2) ◽  
pp. 23-39 ◽  
Author(s):  
B. Senthilnayaki ◽  
K. Venkatalakshmi ◽  
A. Kannan
Keyword(s):  
Learning Style ◽  
Real World ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Learning Performance ◽  
World Knowledge ◽  
E Learning ◽  
Domain Ontologies ◽  
Discovery Method

E-Learning is a fast, just-in-time, and non-linear learning process, which is now widely applied in distributed and dynamic environments such as the World Wide Web. Ontology plays an important role in capturing and disseminating the real world knowledge for effective human computer interactions. However, engineering of domain ontologies is very labor intensive and time consuming. Some machine learning methods have been explored for automatic or semi-automatic discovery of domain ontologies. Nevertheless, both the accuracy and the computational efficiency of these methods need to be improved. While constructing large scale ontology for real-world applications such as e-learning, the ability to monitor the progress of students' learning performance is a critical issue. In this paper, a system is proposed for analyzing students' knowledge level obtained using Kolb's classification based on the students level of understanding and their learning style using cluster analysis. This system uses fuzzy logic and clustering algorithms to arrange their documents according to the level of their performance. Moreover, a new domain ontology discovery method is proposed uses contextual information of the knowledge sources from the e-Learning domain. This proposed system constructs ontology to provide an effective assistance in e-Learning. The proposed ontology discovery method has been empirically tested in an e-Learning environment for teaching the subject Database Management Systems. The salient contributions of this paper are the use of Jaccard Similarity measure and K-Means clustering algorithm for clustering of learners and the use of ontology for concept understanding and learning style identification. This helps in adaptive e-learning by providing suitable suggestions for decision making and it uses decision rules for providing intelligent e-Learning.

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