Examples TI=(genetics", NOUN, ROOT, 0). Ti is one of the root 几 ↓ expanded from any other term so its lexical parent is the empty T2=( biology”,NOUN,“ genetIcs”, HYPERNYM,1).T2isa entry of its parent is"genetics", it has been expanded by the hypernym"relation, and the number of relations that separates it rom the root term TI is I Figure 2 shows the interface of the Golden Standard Definition hase. In the left side of the screen, the current list of root terms is Figure 1. WebCORE architecture shown. The user can manually insert new root terms to this list giving their lexical entries and selecting their parts of speech. The 3.1 Golden standard definition correctness of these new insertions is controlled by verifying that all The first phase of our ontology recommender system is the the considered lexical entries belong to the WordNet repository. Golden Standard definition. as done in the first version of cOre Adding new terms, the final Golden Standard definition is [8], the user describes a domain of interest specifying a set of immediately updated the final list of (root and expanded) terms that relevant terms that will be searched through the concepts(classe represent the domain of the problem is shown in the bottom of the or instances)of the ontologies stored in the system. figure. The user can also make term expansion using WordNet. He selects one of the terms from the golden standard definition and the an improvement, WebCORE include NLP system shows him all its meanings contained in WordNet( top of the omponent that automatically retrieves the most terms figure). After he has chosen one of them, the system presents hir from a given text. moreover. we have added a three different lists with the synonyms, hyponyms and hypernyms component that continuously offers to the user of the term. The user can then selects one or more elements of thes the terms that have been used in those previous problem lists and add them to the expanded term list For each expansion, the descriptions in which a given term appears. depth of the new term is increased by one unit. This will be used 3.1.1 Term-based Problem Description later to measure the importance of the term within the Golden In our system, the Golden Standard is described by a set of initial Standard: the greater the depth of the derived term with respect to its root term the less its relevance will be terms. These terms can automatically be obtained by the Natural Language Processing(NLP)module, which uses 3.1.2 Collaborative Problem Description sitory of documents related to the specific domain in whicl In the problem definition phase a collaborate er is interested in. This NLP module accesses to the been added to the system(right side of Figure 2). This component epository of documents, and returns a list of pairs(lexical entry eads the term currently selected by the user and searches for all part of speech that roughly represents the domain of the problem the stored problem definitions that contain it. For each of these On the other hand, the list of initial (root) terms can be manually problem definitions, the rest of their terms and the number of problems in which they appear are retrieved and shown in the web The module also allows the user to WordNet [12] and some of the rel provides: hypernym, With this simple strategy the user is suggested the most popular hyponym and synonym. The new dded to the Golden terms, fact that could help him to better describe the domain in Standard using these relations might also be extended again, and which he is interested in. It is very often the case that a person has new terms car n iteratively be added to the very specific goals or interests, but does not know how to The final representation of the Golden Standard is defined as a correctly explain/describe them, and how to effectively find set of terms T(L, POS, L, R, Z)where solutions for them. With the retrieved terms. the user might iscover new ways to describe the problem domain and obtain LG is the set of lexical entries defined for the Golden better solutions in the ontology recommendation phase This follows somehow the ideas of the well known folksonomies POS corresponds to the different Parts Of Speech considered The term“ folksonomy” is a combination of“folk"and by WordNet: noun, adjective, verb and adverb ""taxonomy", and was firstly used by Thomas Vander Wal [22]in . Lu is the set of lexical entries of the golden Standard that R is the set of relations between terms of the Golden 4 Mathes, a Standard: synonmym, hypernym, hyponym and root(if a term omies: Cooperative Classification has not been obtained by expansion, but is one of the initial nd commt Shared metadata http://www.adamma om/academic/computer-mediated-Figure 1. WebCORE architecture 3.1 Golden Standard Definition The first phase of our ontology recommender system is the Golden Standard definition. As done in the first version of CORE [8], the user describes a domain of interest specifying a set of relevant terms that will be searched through the concepts (classes or instances) of the ontologies stored in the system. As an improvement, WebCORE includes an internal NLP component that automatically retrieves the most informative terms from a given text. Moreover, we have added a new collaborative component that continuously offers to the user a ranked list with the terms that have been used in those previous problem descriptions in which a given term appears. 3.1.1 Term-based Problem Description In our system, the Golden Standard is described by a set of initial set of terms. These terms can automatically be obtained by the internal Natural Language Processing (NLP) module, which uses a repository of documents related to the specific domain in which the user is interested in. This NLP module accesses to the repository of documents, and returns a list of pairs (lexical entry, part of speech) that roughly represents the domain of the problem. On the other hand, the list of initial (root) terms can be manually specified. The module also allows the user to expand the root terms using WordNet [12] and some of the relations it provides: hypernym, hyponym and synonym. The new terms added to the Golden Standard using these relations might also be extended again, and new terms can iteratively be added to the problem definition. The final representation of the Golden Standard is defined as a set of terms T (LG, POS, LGP, R, Z) where: • LG is the set of lexical entries defined for the Golden Standard. • POS corresponds to the different Parts Of Speech considered by WordNet: noun, adjective, verb and adverb. • LGP is the set of lexical entries of the Golden Standard that have been extended. • R is the set of relations between terms of the Golden Standard: synonym, hypernym, hyponym and root (if a term has not been obtained by expansion, but is one of the initial terms). • Z is an integer number that represents the depth or distance of a term to the root term from which it has been derived. Examples: T1 = (“genetics”, NOUN, “”, ROOT, 0). T1 is one of the root terms of the Golden Standard. The lexical entry that it represents is “genetics”, its part of speech is “noun”, it has not been expanded from any other term so its lexical parent is the empty string, its relation is “root”, and its depth is 0. T2 = (“biology”, NOUN, “genetics”, HYPERNYM, 1). T2 is a term expanded from “genetics” (T1). The lexical entry it represents is “biology”, its part of speech is “noun”, the lexical entry of its parent is “genetics”, it has been expanded by the “hypernym“ relation, and the number of relations that separates it from the root term T1 is 1. Figure 2 shows the interface of the Golden Standard Definition phase. In the left side of the screen, the current list of root terms is shown. The user can manually insert new root terms to this list giving their lexical entries and selecting their parts of speech. The correctness of these new insertions is controlled by verifying that all the considered lexical entries belong to the WordNet repository. Adding new terms, the final Golden Standard definition is immediately updated: the final list of (root and expanded) terms that represent the domain of the problem is shown in the bottom of the figure. The user can also make term expansion using WordNet. He selects one of the terms from the Golden Standard definition and the system shows him all its meanings contained in WordNet (top of the figure). After he has chosen one of them, the system presents him three different lists with the synonyms, hyponyms and hypernyms of the term. The user can then selects one or more elements of these lists and add them to the expanded term list. For each expansion, the depth of the new term is increased by one unit. This will be used later to measure the importance of the term within the Golden Standard: the greater the depth of the derived term with respect to its root term, the less its relevance will be. 3.1.2 Collaborative Problem Description In the problem definition phase a collaborative component has been added to the system (right side of Figure 2). This component reads the term currently selected by the user, and searches for all the stored problem definitions that contain it. For each of these problem definitions, the rest of their terms and the number of problems in which they appear are retrieved and shown in the web browser. With this simple strategy the user is suggested the most popular terms, fact that could help him to better describe the domain in which he is interested in. It is very often the case that a person has very specific goals or interests, but does not know how to correctly explain/describe them, and how to effectively find solutions for them. With the retrieved terms, the user might discover new ways to describe the problem domain and obtain better solutions in the ontology recommendation phase. This follows somehow the ideas of the well known folksonomies4 . The term “folksonomy” is a combination of “folk” and “taxonomy”, and was firstly used by Thomas Vander Wal [22] in 4 Mathes, A. (2004). Folksonomies: Cooperative Classification and Communication through Shared Metadata. http://www.adammathes.com/academic/computer-mediated￾communication/folksonomies.html