GOWDA: Goal-oriented Web Documents Querying tool

Each day, a vast amount of data is published on the web. In addition, the rate at which content is being published is growing, which has the potential to overwhelm users, particularly those who are technically unskilled. Furthermore, users from various domains of expertise face challenges when trying to retrieve the data they require. They may rely on IT experts, but these experts have limited knowledge of individual domains, making data extraction a time-consuming and error-prone task. It would be beneficial if domain experts were able to retrieve needed data and create relatively complex queries on top of web documents. The existing query solutions either are limited to a specific domain or require beginning with a predefined knowledge base or sample ontologies. To address these limitations, we propose a goal-oriented platform that enables users to easily extract data from web documents. This platform enables users to express their goals in natural language, after which the platform elicits the corresponding result type using the algorithm proposed. The platform also applies the concept of ontology to semantically improve search results. To retrieve the most relevant results from web documents, the segments of a user's query are mapped to the entities of the ontology. Two types of ontologies are used: goal ontologies and domain-specific ones, which comprise domain concepts and the relationships among them. In addition, the platform helps domain experts to generate the domain ontologies that will be used to extract data from web documents. Placing ontologies at the center of the approach integrates a level of semantics into the platform, resulting in more-precise output. The main contributions of this research are that it provides a goal-oriented platform for extracting data from web documents and integrates ontology-based development into web-document searches.