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Domain ontologies are a good starting point to model in a formal way the basic vocabulary of a given domain. They provide a broad coverage of concepts and their relationships within a particular domain. However, in-depth coverage of concepts is often not available, thereby limiting their use in specialized subdomain applications. It is also the business dynamics and changes in the operating environment which requires modification to an ontology (McGuinness, 2000). Therefore, the techniques for modifying ontologies, i.e. ontology enrichment, have emerged as an essential prerequisite for ontology-based applications. An ontology can be enriched with lexical data either by populating the ontology with lexical entries or by adding terms to ontology concepts. The former means updating the existing ontology with new concepts along with their ontological relations and types. This increases the size of the existing ontology which requires more computational resources and more time to compute. Thus making it less cost effective. The latter means adding new concepts without taking into account the ontological relations and types between concepts. As a result of this, the ontology structure will remain the same but its concepts will be enriched with their synonyms and homonyms.
Enrichment of ontology concepts aims to improve a given ontology by updating it with similar concepts. It is part of an iterative ontology engineering process (Faatz & Steinmetz, 2005) and it involves subtasks from only lower part of ontology learning layer cake model (Cimiano, 2006). Acquisition of the relevant terminology, identification of synonym terms or linguistic variants and the formation of concepts are subtasks involved. To perform these subtasks, the enrichment process requires an initial ontology which has to be enriched. It then explores available documents and texts from related domain of the given ontology in order to find synonyms or linguistic variants. Finally, by employing the learning approach, which is the core of an ontology concepts’ enrichment process, the concepts are ready for updating the initial given ontology.
There is a variety of learning approaches that are available to enrich concepts of an ontology. These approaches rely on either linguistic, pattern matching, machine learning or statistical techniques (Drumond & Girardi, 2008; Hazman, El-Beltagy, & Rafea, 2011). Even though these approaches have proved useful for enriching ontologies of many domains, they however have some limitations. These approaches use only contextual information without taking into account the semantic information of terms. The contextual information is derived by distributional property of terms such as term frequency or tf*idf, and co-occurrence of terms. Therefore, to address this limitation, this paper proposes a new objective metric namely SEMCON to enriching the domain ontology with new concepts by combining contextual as well as semantics of a term.
The new proposed objective metric uses unstructured data as input for ontology learning process and is composed of two parts - contextual and semantic. Context is defined as the part of a text or statement – passage that surrounds a given term and it determines term meaning. In our work, it is the cosine distance between the feature vectors of any two terms. The feature vectors are composed of values computed by both the frequency of occurrence of terms in corresponding passages, and the statistical features such as font type and font size. The semantics on the other hand is defined by computing a semantic similarity score using lexical database WordNet.
In addition, we also have investigated into how much each of contextual and semantic components contributes to the overall task of enriching the domain ontology concepts and compared our results with the results obtained by other approaches such as tf*idf, 
and LSA. We present our results for several domains, namely, Computer, Software Engineering, C++ Programming, Database and Internet.