Ontology population from web product information

With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a frame- work that is capable of populating a product ontology us- ing tabular product information from Web shops. By for- malizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The per- formed evaluation shows that instantiating consumer elec- Tronics from Best Buy and Newegg.com results in an F1 score of approximately 77%

Development of a Framework for Ontology Population Using Web Scraping in Mechatronics

One of the major challenges in engineering contexts is the efficient collection, management, and sharing of data. To address this problem, semantic technologies and ontologies are potent assets, although some tasks, such as ontology population, usually demand high maintenance effort. This thesis proposes a framework to automate data collection from sparse web resources and insert it into an ontology. In the first place, a product ontology is created based on the combination of several reference vocabularies, namely GoodRelations, the Basic Formal Ontology, ECLASS stan- dard, and an information model. Then, this study introduces a general procedure for developing a web scraping agent to collect data from the web. Subsequently, an algorithm based on lexical similarity measures is presented to map the collected data to the concepts of the ontology. Lastly, the collected data is inserted into the ontology. To validate the proposed solution, this thesis implements the previous steps to collect information about microcontrollers from three differ- ent websites. Finally, the thesis evaluates the use case results, draws conclusions, and suggests promising directions for future research

Ontology Population in Conversational Recommender System for Smartphone Domain

The Conversational recommender system (CRS) is a knowledge-based recommendation system that uses ontology as its knowledge representation.The knowledge of a CRS is based on a real world knowledge base service where information on the topic such as product details and descriptions must always be up-to-date.However, the process of gathering the information is still conducted manually.The process is very time consuming and prone to error.Therefore, automatic or semi-automatic processes that can adapt to update, find and insert information into the knowledge base that matches a given ontology are needed. Hence, this study aims to design a framework for ontology population on Conversational Recommender Systems based on the Functional Requirements as in [4] from tabular web documents so its instantiation as ontology result can substitute manual ontology update on CRS. The framework includes a clustering process that employs the Bi-Layer K-Means Clustering Algorithm as a part of knowledge acquisition. To reach the objective, it is necessary to analyze and check the individual consistency of the resulting ontology. Another aim of this study is to analyze the resulting ontology still suitable according to CRS ontology requirements by checking the CRS Ontology Requirements.The experiment is conducted using data from www.gsmarena.com through a crawler engine. There are four steps in an ontology population process: Document Crawling, Identification of the page (individuals, attributes and values), KnowledgeAcquisition, and OWL Ontology Export. Using input from the tabular web document and developing OWL ontology export that mapping the instances and relations, the result shows that the specifications included in the Weak Clustering, Reasonable Clustering and strong clustering categories can be recommended for the Conversational Recommender System ontology. Analysis of consistency checking shows that the ontology remains consistent and suitable for the CRS ontology requirement

Intelligent Information Systems for Web Product Search

Over the last few years, we have experienced an increase in online shopping. Consequently, there is a need for efficient and effective product search engines. The rapid growth of e-commerce, however, has also introduced some challenges. Studies show that users can get overwhelmed by the information and offerings presented online while searching for products. In an attempt to lighten this information overload burden on consumers, there are several product search engines that aggregate product descriptions and price information from the Web and allow the user to easily query this information. Most of these search engines expect to receive the data from the participating Web shops in a specific format, which means Web shops need to transform their data more than once, as each product search engine requires a different format. Because currently most product information aggregation services rely on Web shops to send them their data, there is a big opportunity for solutions that aim to tackle this problem using a more automated approach. This dissertation addresses key aspects of implementing such a system, including hierarchical product classification, entity resolution, ontology population and schema mapping, and lastly, the optimization of faceted user interfaces. The findings of this work show us how one can design Web product search engines that automatically aggregate product information while allowing users to perform effective and efficient queries

An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology

HandyBroker - An intelligent product-brokering agent for M-commerce applications with user preference tracking

One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been designed for m-commerce applications. It uses an evaluation function to represent a user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tracks the user’s preferences for a particular product by tuning some parameters inside its evaluation function. A prototype called “Handy Broker” has been implemented in Java and the results obtained from our experiments looks promising for m-commerce use

Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer’s generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers’ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising

A Process Framework for Semantics-aware Tourism Information Systems

The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives

Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration