Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks

Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions

Pembangkitan interpretasi tekstual berbahasa Indonesia berdasarkan data pemeriksaan kimia darah menggunakan pendekatan berbasis r-template

The result of the blood chemistry tests is usually presented in the form of a table written in abbreviations, numbers, and units. Unfortunately, the young doctors often require the time and experience for interpreting the blood chemistry tests into a textual representation, which is easy to read and understand. Therefore, this research aimed at developing a system (BTISys) that can generate the textual representation in the Indonesian language automatically based on the blood chemistry test. BTISys generates the representation using Natural Language Generation (NLG) approach based on the r-template method. The reliability of BTISys is measured by considering the naturalness of generated textual representation. The naturalness can be observed by three criteria, such as readability, clarity, and general appropriateness. The reliability of BTISys is quite good to generate the textual representation automatically. It can be seen from the readability, clarity, and general appropriateness, which reach 73 %, 70 %, and 60 % respectively, that implies the naturalness of generated textual representation.Hasil pemeriksaan laboratorium kimia darah umumnya direpresentasikan dalam bentuk tabel yang ditulis dalam singkatan dan angka yang dilengkapi dengan satuannya. Dokter muda biasanya membutuhkan waktu dan pengalaman dalam menginterpretasikan hasil pemeriksaan tersebut ke dalam representasi tekstual yang mudah dibaca dan dipahami. Penelitian ini bertujuan untuk membangun suatu sistem (BTISys) yang mampu membangkitkan representasi tekstual dalam bahasa Indonesia secara otomatis berdasarkan hasil pemeriksaan laboratorium kimia darah. BTISys membangkitkan representasi tersebut dengan menggunakan pendekatan Natural Language Generation (NLG) berbasis metode r-template. Kehandalan BTISys pada penelitian ini diukur berdasarkan tingkat kealamian representasi tekstual yang dibangkitkan. Tingkat kealamian tersebut dapat dilihat melalui tiga kriteria, yaitu keterbacaan, kejelasan dan kesesuaian umum. Berdasarkan hasil pengujian dapat disimpulkan bahwa BTISys memiliki kehandalan yang cukup baik dalam menghasilkan suatu interpretasi ke dalam representasi tekstual secara otomatis menggunakan bahasa Indonesia. Hal ini terlihat dari tingkat keterbacaan yang mencapai 73 %, kejelasan 70 %, dan kesesuaian umum 60 % yang menyiratkan kealamian dari representasi tekstual yang dibangkitkan oleh BTISys