Search results

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

The purpose of this paper is to describe TraceALL which is a Semantic Web (SW), ontology‐based, service‐oriented framework which aims to provide the necessary infrastructure enabling food industry (particularly SMEs) to implement traceability applications using an innovative generic framework.

The framework builds upon the idea of the Semantic Web and provides an open and extensible underlying platform that allows different traceability interconnected applications to be designed and developed. More specifically, the framework provides a formal, ontology‐based, general‐purpose methodology to support knowledge representation and information modelling in traceability systems. Additionally, it suggests an open application framework based on widely used Semantic Web standards. Finally it provides a set of core services for storing, processing and retrieving traceability information in a scalable way. These components, taken together, facilitate the efficient development of next generation traceability applications.

Based on a case study which the authors executed as a proof of concept and studying the relevant literature it was found that TraceALL facilitates the development of next generation traceability applications because, from a food safety perspective, it enables all stakeholders in the food supply chain to have an information trail that follows the product's physical trail, but at the same time is cost effective, easy to manage and applicable within a globalised, networked, interoperable economic environment.

To the best of the authors' knowledge, this is the first food traceability system based completely on solid existing standards of the Semantic Web initiative. The authors consider that the inspiring analogy between resources such as those described in the Semantic Web initiative and the core traceability concept of a Traceable Resource Unit (TRU) is an extremely useful concept for developing cost‐effective traceability applications that possess many key requirements, which are described in the paper.