Discovery ) Next, these ambiguities are interpreted the refinement process will support a user in fine- regarding the users information need, in order to tuning of his/her initial query. Thereafter, it ranks the estimate the effects of an ambiguity on the fulfilment received resources according to their relevance of the of the user's goals(Ambiguity-Interpretatic user's query, and finally, the system relaxes the users B. Query Refinement such that its best approximation can be found The approach of [15] for query refin the query refinement based on the domain ontology simulate reflect the refinement model which a human and user annotation on data. The recommendation librarian uses in her daily work. It means that we us system of Ontogator utilises the domain ontology three sources of information in query refinement: together with annotated data and recommendation (1)the structure of the underlying ontology,(2) the rules to recommend the user to view other related content of the knowledge repository and ( )the users information which maybe missed by his initial query behaviour(how users refine their queries on their This process is known s the semantic browsing own). Since the first two sources are used for function. Through this kind of system, the user can measuring the ambiguity of a query, the query refine his/her queries by selecting related information refinements based on them are treated cooperatively that suits his needs as the ambiguity-driven query refinement. In this Query refinement in Knowledge Sifter[20] is an query refinement approach, the ambiguity parameters aggregation of query expansion(Query Formulation presented in the previous section are combined and Agent), which is also used in [11, [2]and [3], and presented to the user in case she wants to make a recommendation system (Integration Agent) refinement of the initial query. Each of ambiguity techniques. The Query Formulation Agent consults parameters has its role in quantifying ambiguity. For the Ontology Agent to refine or generalise the query each of the parameters, query term(s)that affect the based on the semantic median provided by the ambiguity most importantly are determined available ontology services. Besides, the Integration [16 presents another query refinement approach Agent is responsible for compiling the sub-query called information-need driven query refinement. This results from vari definition of an order between queries, in order to user preferences ous sources, ranking them according approach is a formalised one; it bases on 1)the create the map of the query neighbourhood- query map or so-called query space, and 2)the V. METHODOLOGIES IN COMMON characterisation of the query ambiguity, in order to While surveying the field of ontology-based control the navigation in the query space- compass. querying research, some common methodologies can The query refinement process is then realized as the be determined. Some are intrinsic to the RDF movement through the query's neighbourhood in formalism and are present in almost semantic web order to change the ambiguity of that query applications. The knowledge and understanding of Similarly, [7] presents a comprehensive approach these common methods as well as how they are used for the refinement of ontology-based queries, which is in the various actual approaches are of great founded on the incrementally and interactively importance for future methodologies of ontology tailoring of a query to the current information needs based search/query systems of a user. These needs are implicitly and on-line elicited by analyzing the user's behaviour during the A. Role of Ontology searching process. The gap between a user's need and In the regarded systems, ontologies are very crucial his query is quantified by measuring several types of and play a key-role. Ontologies appear from the query ambiguities. Consequently, in the refinement starting (query formulation) until the end(query process a user is provided with a ranked list of answering)of querying processes. We can conclude refinements. which should lead to a significant the roles of ontology as following:(1)providing a decrease of these ambiguities. Moreover, by pre-defined set of terms for exchanging information exploiting the ontology background, the approach between users and systems; (2)providing knowledge supports the detection of"similar"results that should for systems to infer information which is relevant to help a user to satisfy his information need users requests; (3) filtering and classifying The third source for making the query's refinement information; and (4) indexing information gathered recommendations in[ 15] mentioned above requires an and classified for preser analysis of the users' activities in an ontology-base B. Keyword- Concept Mapping application. That is also the approach of many query Mapping between keywords and formal concepts is refinement mechanisms and OntoLoger [14 is a one of them. Ontologer bases on the log-ontology(usage- a common pattern data)and analyses the user's behaviour in order to search/query modules. There are a number of reasons guide the user in refinement process. By doing this, for its prevalence. The first is that an assumption of4 Discovery). Next, these ambiguities are interpreted regarding the user’s information need, in order to estimate the effects of an ambiguity on the fulfilment of the user’s goals (Ambiguity-Interpretation). B. Query Refinement The approach of [15] for query refinement tries to simulate reflect the refinement model which a human librarian uses in her daily work. It means that we use three sources of information in query refinement: (1) the structure of the underlying ontology, (2) the content of the knowledge repository and (3) the users’ behaviour (how users refine their queries on their own). Since the first two sources are used for measuring the ambiguity of a query, the query refinements based on them are treated cooperatively as the ambiguity-driven query refinement. In this query refinement approach, the ambiguity parameters presented in the previous section are combined and presented to the user in case she wants to make a refinement of the initial query. Each of ambiguity parameters has its role in quantifying ambiguity. For each of the parameters, query term(s) that affect the ambiguity most importantly are determined. [16] presents another query refinement approach called information-need driven query refinement. This approach is a formalised one; it bases on 1) the definition of an order between queries, in order to create the map of the query neighbourhood – query map or so-called query space, and 2) the characterisation of the query ambiguity, in order to control the navigation in the query space – compass. The query refinement process is then realized as the movement through the query’s neighbourhood in order to change the ambiguity of that query. Similarly, [17] presents a comprehensive approach for the refinement of ontology-based queries, which is founded on the incrementally and interactively tailoring of a query to the current information needs of a user. These needs are implicitly and on-line elicited by analyzing the user’s behaviour during the searching process. The gap between a user’s need and his query is quantified by measuring several types of query ambiguities. Consequently, in the refinement process a user is provided with a ranked list of refinements, which should lead to a significant decrease of these ambiguities. Moreover, by exploiting the ontology background, the approach supports the detection of “similar” results that should help a user to satisfy his information need. The third source for making the query’s refinement recommendations in [15] mentioned above requires an analysis of the users’ activities in an ontology-based application. That is also the approach of many query refinement mechanisms and OntoLoger [14] is a one of them. OntoLoger bases on the log-ontology (usage￾data) and analyses the user’s behaviour in order to guide the user in refinement process. By doing this, the refinement process will support a user in fine￾tuning of his/her initial query. Thereafter, it ranks the received resources according to their relevance of the user’s query, and finally, the system relaxes the user’s query such that its best approximation can be found. In a similar manner to OntoLoger, [12] deals with the query refinement based on the domain ontology and user annotation on data. The Recommendation system of Ontogator utilises the domain ontology together with annotated data and recommendation rules to recommend the user to view other related information which maybe missed by his initial query. This process is known s the semantic browsing function. Through this kind of system, the user can refine his/her queries by selecting related information that suits his needs. Query refinement in Knowledge Sifter [20] is an aggregation of query expansion (Query Formulation Agent), which is also used in [1], [2] and [3], and recommendation system (Integration Agent) techniques. The Query Formulation Agent consults the Ontology Agent to refine or generalise the query based on the semantic median provided by the available ontology services. Besides, the Integration Agent is responsible for compiling the sub-query results from various sources, ranking them according user preferences. V. METHODOLOGIES IN COMMON While surveying the field of ontology-based querying research, some common methodologies can be determined. Some are intrinsic to the RDF formalism and are present in almost semantic web applications. The knowledge and understanding of these common methods as well as how they are used in the various actual approaches are of great importance for future methodologies of ontology￾based search/query systems. A. Role of Ontology In the regarded systems, ontologies are very crucial and play a key-role. Ontologies appear from the starting (query formulation) until the end (query answering) of querying processes. We can conclude the roles of ontology as following: (1) providing a pre-defined set of terms for exchanging information between users and systems; (2) providing knowledge for systems to infer information which is relevant to user’s requests; (3) filtering and classifying information; and (4) indexing information gathered and classified for presentation. B. Keyword - Concept Mapping Mapping between keywords and formal concepts is a common pattern appearing in ontology-based search/query modules. There are a number of reasons for its prevalence. The first is that an assumption of