Resource description framework merupakan sebuah bahasa untuk merepresentasikan informasi tentang resource yang ada di dunia (Yu, 2011). RDF juga merupakan model berbentuk graf untuk merepresentasikan resource dan relasinya. Resource yang direpresentasikan adalah seluruh pengetahuan manusia yang kemudian dimodelkan kedalam bentuk subyek, predikat, dan objek. Statement RDF berupa triple, yaitu subject, predicate, object (Manola et al, 2004). Contoh: Ari Rifki is the author of the resource http://tugas-akhir.com/semantic-search/. Kalimat tersebut memiliki beberapa bagian seperti:
Semanticsearch engine is a search technology that is being developed at this time were very influential and useful for the user to obtain a more detailed search results .In a semanticsearch many processes that must be passed, but the core of the search is the process of matching between data and keywords entered by the user . Applications semanticsearch must have a user friendly display, where the latter process user searches by entering keywords and semanticsearch applications will eventually provide a search result page ranking list of recommended web . In general a semanticsearch keeps the information content of a semantic web and the search is not only content that can be searched through the search process, in addition to the search results can be content in the form of images and the others .
Pengujian sistem menggunakan dua cara, yang pertama adalah pengujian metode pencarian semanticsearch dengan kalimat, bertujuan untuk mengetahui keberhasilan sistem yang dibangun. Kemudian cara kedua adalah dengan penghitungan informasi yang dikembalikan atau di retrieved oleh sistem. Pengujian ini dilakukan untuk mengukur kemampuan sistem dalam me-retrieve Informasi yang tersimpan dalam ontology. Untuk mengukur efektifitas sistem temu kembali informasi terdapat dua rasio umum yang biasa dipergunakan yaitu precision (ukuran kemampuan sebuah sistem untuk menampilkan hanya dokumen yang relevan) dan recall (ukuran kemampuan sistem untuk menampilkan seluruh dokumen yang relevan).
Therefor, searching facility with ontology and semanticsearch technology is applied in order to solve problems of relevance of search results and the words meaning between system and users. The ontology model formed is expressed by using OWL language which contains semantic entities such as three main class i.e: Student class, Lecturer class and Publication class and three helper class i.e: Kalimat class, Stopword class and Keyword class. The rules of semanticsearch builts are Language processing rules using Natural Language Processing so that the system is able to understand the meaning of keyword or search sentence which are input by the users and the query SWRL rule to search the information stored in the knowledge base.
Application of the Qur'an nowadays is already providing search feature using a text based search techniques, search based on the selection of chapters, sura, and verse numbers, and there was already applying text mining methods. Sometimes on the text based search techniques, keywords entered by the user do not produce any content. Therefore, we need a method that can recognize relationships between words. Semanticsearch methods can find the words that relate to each other. Semanticsearch method could find in related words which backing an RDF data model. Resource Description Framework provides a simple semantic relations to objects and their relationships. RDF data modeling can be expressed in XML syntax. Then, by utilizing the rule of SPARQL queries to process keywords from the user, the search results can be obtained in the form of content that contains and related with the keyword and also displays a list of related keywordsin the form of links.
The method offered in the search process to overcome these weaknesses is the semanticsearch. In this research, the model was constructed by calculating the similarity search using the combination of LSA method and Weighted Tree similarity algorithm. In the theory, LSA is able to find keyword semantic relation that is language independent. The research was implemented in the digital library collection of books in Indonesian language. The system will respond by providing relevant search results to documents in Indonesian language
This OGC Testbed 11 Engineering Report provides a comprehensive review and comparison in terms of architecture, functionality, and usability of the OGC catalogue service standards CSW 2.0.2 and CSW 3.0. We are especially interested in how well the two standards provide support for open searches and federated distributed searches in current distributed computing paradigms. We also evaluated the support of semantic searches using different strategies, including (1) semantic mediation, a.k.a. ontology- based query expansion (Li et al. 2008; Li et al. 2011), (2) semantic association, which enables current catalogue information models to support semanticsearch (Li et al. 2014; Li et al. 2015), and (3) complete renovation of the CSW information model to be a triple store and utilize Semantic Web technology (Berner-Lee 2001) to support semantic query and data retrieval. Scenarios to search for hydrological data are developed to evaluate the performance of catalogue searching using the above strategies. Recommendations for adoption of CSW standards as well as tasks in advancing catalogue search and data discovery in future testbeds is also discussed.
The information is extracted from the online store site through keyword search and other means of textual analysis. This process makes use of as- sumptions about the proximity of certain pieces of information (for example, the price is indicated by the word price followed by the symbol $ followed by a positive number). This heuristic approach is error-prone; it is not always guaranteed to work. Because of these difficulties only limited information is extracted. For example, shipping expenses, delivery times, restrictions on the destination country, level of security, and privacy policies are typically not extracted. But all these factors may be significant for the user’s deci- sion making. In addition, programming wrappers is time-consuming, and changes in the online store outfit require costly reprogramming.
“Langkah yang pertama adalah meletakkan data pada Web dalam suatu bentuk sehingga mesin dapat secara alami memahami, atau mengubahnya menjadi format tertentu. Pembuatan ini yang kita sebut suatu Semantic Web—Suatu data web yang dapat diproses secara langsung atau secara tidak langsung oleh mesin.” (Tim Berners-Lee, Weaving the Web, Harper San Francisco, 1999)
Two important technologies for developing semantic web are XML and RDF . XML allows everyone to put tags on the web pages, while RDF is functioned to understand the meaning of the sentence, in which it consists of subject, verb, and object (always called as triple). The triple could be written using XML tags . For instance, the XML-based RDF syntax below shows that Edi is a Lecturer at STMIK TIME.
Starting from research institutions need to manage knowledge with the main source of human capital, BPPT requires a system that can accommodate it. Development system that is better known as knowledge management system aimed at sharing data, information, and knowledge as well as the communication that occurs in these institutions. The model that is a prototype of webbased system uses semantic web technology with the application of ontologies and uses web 2.0 as a medium for collaboration among knowledge workers. Model of knowledge management system resulting from this development is the knowledge sharing system that consists of semantic web portals and blogs that are still independent.
c) Semantic portal e-government yang dikembangkan tidak hanya pada bidang government-to-employee (G2E) dan government-to-government (G2G), tetapi juga pada bidang government-to-citizen (G2C) dan government-to-business (G2B).
Ontologi diartikan sebagai berikut: “ An explicit formal specification of how to represent the objects, concepts and other domain entities and relationships among them” (Chen, 2004). Dengan kata lain, ontologi menyediakan representasi pengetahuan sehingga memungkinkan adanya pemahaman yang sama mengenai suatu domain. Dalam bidang ilmu komputer, ontologi digunakan dalam teknologi baru, yaitu semantic web yang menyajikan informasi dalam web secara semantik. Penjelasan lebih lanjut mengenai ontologi akan dibahas pada subbab 2.2.
When analysing published IE approaches the whole range can be found, from utilizing statistical word lists (often augmented with frequencies that are learned on document corpora), through hierarchies of concepts that are related to each other by specific relation types (concept–superconcept relations, part– whole relations, etc.), towards even richer semantic representations like the theory of conceptual dependencies (Shank, 1975). Generally speaking, such representational schemes are said to represent a world state, certain beliefs or even factual knowledge that is implicitly present in the analysed documents. There are very diverse opinions on how to represent semantics. Some research groups see semantics as the semantics of some logic representation. Others propose that semantics and context are contained in a more ‘fuzzy’ way by statistical properties over text. Other representation formalisms are also found, but the division between symbolic approaches (representation through logic) and sub-symbolic (connectionist, fuzzy or statistical approaches) is important. In OntoExtract the initial analysed and annotated text is transformed into an internal representation that makes use of a variety of linguistic analysis steps to come to an initial interpretation of what is written. After an additional disambiguation phase, performing co-reference chaining, resolution of, for example, abbreviations, pronoun resolution, etc., an internal representation of the text is built up. This representation contains the original text, its annota- tions, but also the resolutions performed on it. Now it is possible to tell what is going on, who is involved, and what type of relations are discussed in the discourse that has been analysed.
The purposes of this study are to investigate the mastery of Kanji of university students after being taught by using Semantic Mapping and conventional techniques, to find out the significant differences of mastery of Kanji between university students who are taught by using semantic mapping and those are who taught by using conventional techniques, and to investigate the responses of university students who are taught by using semantic mapping in learning Kanji. This study uses True Experimental method which belongs to Quantitative approach. To be specified, this study use control group pretest posttest design. The instruments used are written test and questionnaires. The data is analyzed by using t-test and simple descriptive statistic. The findings point out 80% students in experimental group reach the score above 75 in posttest. Meanwhile, 55% students in control group get the score above 75 in posttest. The gain of pretest and posttest result of experimental group is 18.125 and the control group is 11.125. The results above indicate that there is a significant difference of mastery of Kanji between students in experimental group and control group. To sum up, the use of semantic mapping is effective in teaching Kanji than conventional techniques. Furthermore, based on the result of questionnaire, most of students give positive responses toward the use of semantic mapping in learning Kanji because it is attractive and interesting. Besides, the use of semantic mapping can facilitate students in memorizing and understanding Kanji.