MODEL PENCARIAN INFORMASI BATIK DENGAN METODE SEMANTIK BERBASIS ONTOLOGI

Penelitian ini bersifat eksprimental dengan membangun aplikasi sistem pencarian informasi batik dengan metode semantik berbasis ontologi. Penelitian ini dilakukan karena banyaknya data artikel mengenai batik yang tersebar di internet. Dan telah banyak teknik pencarian yang dikembangkan untuk menemukan kembali informasi yang diinginkan pengguna. Salah satunya adalah dengan memanfaatkan teknologi semantik. Pencarian berdasarkan makna katakunci dapat lebih ditingkatkan dengan memanfaatkan ontologi untuk menjadikan suatu domain menjadi terstruktur.Penelitian ini memanfaatkan ontologi untuk memperluas makna katakunci agar hasil pencarian dapat lebih presisi dan relevan dengan keinginan pengguna. Ontologi dengan domain batik dibangun menggunakan Protégé. Kemudian untuk mengukur kemiripan antara dokumen dengan kueri digunakan model ruang vektor (vector space model).Artikel dibobot menggunakan pembobotan Tf/Idf, kemudian dihitung nilai kosinus nya. Dan setelah itu artikel dirangking. Hasil penelitian ini dievaluasi dengan membandingkan nilai presisi dan nilai recall pada sistem pencarian berbasis ontologi ini dengan sistem pencarian konvensional yang tanpa menggunakan ontologi.Hasilnyadiperoleh nilaipresisi80% dan recallsebesar 76% untuk pencarian menggunakan ontologi. Dan nilai presisi 46% dan recallsebesar 90% untuk pencarian yang tidak menggunakan ontologi.Ini menunjukkan bahwa sistem yang dibangun mampumenemukan artikel yang lebih presisi daripada sistem pencarian konvensional. Kata kunci: Semantik, Ontologi, Vector Space Model, Batik This experimental research is built with developing information search system application on batik with semantic method based on ontology. This research is done because of many batik article is spread on the internet. Numbers of searching technique has been developed to retrieve information that user need. One of these techniques is using semantic technology. Semantic search the information based on the meaning. Searching technique based on the keyword meaning could be higher by using ontology that makes domain structured. This experiment using ontology to expand keyword query so the result be more precise and more relevant with the user want. Ontology with batik as the domain, is built using Protégé. Vector space model is used to measure the similarity between document and keyword query. Article is weighted using Tf/Idf, then counting the cosine value. after that the article is ranked. The result is evaluated by comparing the precision and recall value between this system with other conventional search system. The result shows 80%precision value and76%recall valuefor semantic search based on ontology. And precision 46%, recall 90% for conventional searching technique without ontology. This results show that this system is able to find relevant article with user input than conventional search system. Keywords: Semantic, Ontology, Vector Space Model, Bati

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with arXiv:1303.244

Automatic Annotation and Semantic Search from Protégé

Semantic search has been one of the major envisioned benefits of the Semantic Web since its emergence in the late 90’s [1]. Our demo shows a proposal towards this goal. One way to view a semantic search engine is as a tool that gets formal queries (e.g. in RDQL, RQL, SPARQL, or the like) from a client, executes them against an ontology-based knowledge base, and returns tuples of ontology values (resources) that satisfy the query [2]. While this conception of semantic search brings enormous advantages already, our work aims at taking a step beyond this. In our view of Information Retrieval in the Semantic Web, a search engine returns documents, rather than (or in addition to) exact values, in response to user queries. The engine should rank the documents, according to concept-based relevance criteria. The overall retrieval process is illustrated in Figure 1 (see [3] for more details of our research)