# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 2 Related Work  
Ontology reuse is one of the best practices when developing a new ontology, as it promotes using content that has been modeled before and reusing it to represent a new domain. These contents could be whole ontologies (hard ontology reuse) or a selected subset (softontology reuse) [10]. MIREOT [11] specifies the minimal information needed to specify the term for reuse, i.e., source ontology URI, source term URI, and target direct superclass URI. Ontofox [12] follows the full MIREOT guidelines with the advantage of allowing the user to extract and reuse specific terms from an ontology, which promotes modularization. There have also been attempts that made use of natural language processing techniques to extract terms and relations from text documents representing a target domain, and to reuse them to develop a new ontology modeling the target domain [13]. The problem with these approaches is that they require the user to have good knowledge of all of the existing ontologies and their concepts, which is very difficult, especially for a person who is not an expert in the target domain. Ontology development tools (e.g., Protege [14] and NeOn toolkit [15]) support hard ontology reuse (owl: imports), allowing the user to import and reuse whole ontologies [10], which is not preferrable for ontology experts as they import concepts that may be irrelevant and incompatible with the requirements of the target ontology [10]. However, there are some plugins that support soft ontology reuse. MIREOT Protege plugin [16] facilitates MIREOT in practice and implements crucial variables of specification. ProtegeLOV [17] allows the user to access Linked Open Vocabularies (LOV) and reuse terms from it during the development of an ontology. Watson [18] is a NeOn plugin and is mainly a semantic web search engine which searches and locates the content of ontologies on the Web. XOD [19] is an \"eXtensible Ontology Development\" strategy which recommends principles to support the active development and usage of extensible ontologies and to suggest different tools, \"Ontoanimal\", that fulfill the requirements of the principles. It emphasizes that there is currently no tool to support the whole extensible ontology development process. Based on these studies, there were several attempts to support ontology reuse by adding concepts from existing ontologies to the target ontology. However, determining and selecting concepts for reuse was done manually by an expert or using a semantic search engine. Selecting such concepts in more complex domains that have hundreds of ontologies representing different interdisciplinary domains (e.g., the BioPortal repository contains more than one thousand ontologies [20]) is a challenging task which requires searching, evaluating, and ranking those concepts according to their relevance to the context of the target domain. Thus, ontology recommendation, i.e., the process that includes searching, evaluating, and ranking ontologies, becomes an essential part of the ontology reuse process. Next, we present benchmark ontology recommendation systems, summarize and compare their features, see Table 1, and discuss their limitations which motivated our proposed system. WebCORE [21] is a semi-automatic Collaborative Ontology Reuse and Evaluation system which provides an NLP model that lexically evaluates ontologies. CORE is one of the first attempts to apply recommender system techniques for ontology recommendations, as it uses previous ontology ratings and evaluations to apply a collaborative filtering technique to recommend the appropriate ontologies which represent a given domain. The drawback of this approach is that it is hard to collect ratings in real situations as the number of ontology development users (for one ontology) is not large enough to satisfy the collaborative filtering technique, in contrast to other product-based recommender systems. BioSS [6] is an ontology selection system that accepts user input as a set of keywords which describe a domain of interest.