# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
###### Abstract
These tools have limited functionality which hinders their use in the ontology reuse process. First, they are not helpful if the user has preferences/requirements about the ontologies he wants to reuse, other than the pre-defined evaluation criteria, such as the ontology domain or ontology type. For example, if the user wants to develop a new ontology representing a certain domain (i.e., Botany), then he wants the system to recommend ontologies related to the \"Botany\" domain, likewise if he wants to select a specific type of ontology (e.g., taxonomies). Second, they give one-shot recommendations and do not support the iterative nature of the ontology development process. Finally, they recommend whole ontologies but cannot guide the users to the ontology components (i.e., concepts) to be reused. Also, ontology matching tools (e.g., LYAM++ [8] and AML [9]) use lexical, structural, and semantic matching approaches to find correspondences between concepts in a source and a target ontology. They successfully determine alignments between concepts that are richly described (i.e., have annotations such as synonyms, definitions, comments, etc. ), but they have limited performance if one of the input ontologies is a basic one with few concepts and no rich annotations describing these concepts. This is the case for most ontologies (e.g., bio-ontologies), where scientists are interested in defining a hierarchy of concepts describing a domain without adding many annotations describing each concept in detail. This causes ontology matching tools, in some cases, to identify corresponding concepts that have identical labels, but this does not necessarily mean that those concepts are semantically equivalent (i.e., sharing the same word sense). In this case, we need to learn more about the users' interests as an additional source of knowledge to conduct proper ontology matching. In this paper, we propose a novel personalized ontology recommendation and reuse system which can effectively support an ontology engineer in the task of developing a new ontology by suggesting parts from existing ontologies to extend an input ontology. We developed a utility-based recommender system that explicitly collects user preferences and recommends appropriate ontologies, along with their matching concepts. Then, the user can select appropriate components to extend his input ontology. The proposed recommender system uses a technique based on reinforcement learning to learn the user's explicit preferences and implicit feedback to adaptively offer personalized relevant recommendations according to the user's preferences. We present three main contributions. First, we propose a utility-based ontology recommender system that (a) helps the user to iteratively develop a new ontology using an input ontology, (b) recommends ontologies along with their appropriate concepts to the user so that the user can reuse them and extend the input ontology, (c) creates a user profile for each user to recommend and rank ontologies and their appropriate classes based on the user's preferences, and (d) uses the user's feedback to update his user profile to cope with any change in his preferences throughout the interaction session. Second, we develop a prototype and use the biomedical domain as a case study, as it has hundreds of existing ontologies in different repositories. Third, we evaluate the system using real users to obtain a realistic evaluation of the system. The rest of the paper is structured as follows. Section 2 reviews and compares the features of existing ontology recommendation tools. Next, Section 3 illustrates the proposed interactive ontology recommender framework, and Section 4 describes the utility-based ontology recommendation system. Section 5 provides the implementation details of the proposed framework. Section 6 describes the user-based evaluation of the proposed framework and discusses the outcoming results. Finally, Section 7 concludes the paper with possible suggestions for future work.