## 4 Conclusion
The focus of this study is filling the gap between formal representation and design of the system. This study is based on the formal representations provided in [62; 63]. However, in [23], the authors focus on the problems in the use of these formal representations in design time. [PERSON] et al. also designed the E-barter system using WS-BPEL [67] web services [68]. The studies of [23; 68] uses web services as the base element for the development of the E-barter system, however, our study benefits from semantic web services and provides semantically matching capability for bartering. [PERSON] et al. [69] focused on E-barter systems with a new knowledge-based approach in their study. The goal is to ensure that multiple barter situations are performed with optimal matching. In this study, a logical language was introduced that provides more complex specifications of agents' requests. It is also intended to simulate the semantic similarity between proposals that will be presented in a logic-based utility function [69]. On the other hand, in [70], a game concept was defined to describe the interactions in the barter system. According to this concept, there are several agents, and these agents have vectors with parameters specifying their requests and bids. Bartering is performed according to the matching of these vectors. The studies presented in [69; 70] focuses on improving the matching mechanism for bartering using more specific parameters in the definition of requirements and logic-based utility functions. However, our study tackles semantic matching in two levels: one in finding the closest semantic web service and another in the level of items to be bartered using ontologies. [PERSON] et al. [25] have designed an agent-based application called Bartecell. In this application, software agents can work on wireless networks and reach mutually beneficial barter agreements. New negotiation algorithms have also been introduced for transactions between agents. An E-barter architecture compatible with mobile devices has been introduced that provides location-based services [25]. This study focusses on the use of agents and benefiting from wireless networks in the negotiation of those agents for E-bartering propose. However, unlike our study, Bartecell does not utilize semantic web services and neither considers the semantic discovery of these services nor semantic matching of the bartering items. [PERSON] et al. [26] have designed and developed a MAS for supply chain automation. The system automatically recommends suitable suppliers for handicraft women (HDWs). The recommendation procedure is based on two supplier selection levels and then a negotiation phase. The first level is the process of selecting vendors that sell the necessary products. On the second level, it only specifies vendor profiles that can successfully match HDW. During the negotiation phase, the relevant actors will conduct discussions on the required quantity, quality, cost and delivery processes. Ontologies have also been utilized in the operation of these processes. Although this study addresses a different domain than ours, the general approach is close when considering the two selection levels. However, they use the ontologies only in the second level where they specify vendor profiles which match HDWs. In our study, selection of the categories in the first level is also done with the help of semantic matching of services. Moreover, the selection mechanism in the first level is automatized by using semantic web services. In [24], a MAS was developed for the E-barter systems. Unlike other studies, an architecture has been designed and implemented that uses ontology-based comparisons in bid mapping. This architecture introduces a type of agent named Barter Manager Agent in addition to the E-barter agents. This agent determines the barter partners according to semantic proximity. With this approach, it is aimed to find the best match, not just based on the price and quantity of goods but also considering the relation between supply and demand.