# A Data Driven Approach for Raw Material Terminology
## 4 Terminology Aggregation and Presentation  
### Data Integration Procedure  
_The Pipeline_  
The main goal of our approach is to merge and link all available terms in the raw material domain into one lexicon structure, within the terminological database Termi and as linguistic linked data available via SPARQL endpoint, in the first place by aligning as much as possible term entries from dictionaries and other resources covering raw material domain terminology. Besides the aim of aggregating terms from different resources, one of the reasons for alignment of terms from multiple dictionaries (paper and electronic) was to assess term usage, which determines its importance for raw material terminology. On the other hand, alignment of terms with SprMD was necessary, since these dictionaries are a base resource for lemmatization and multiword term extraction. Since SprMD are already in the lexical database Leximirka [32], developed and managed by the same research team, this type of alignment was possible. Figure 3 presents an outline of the pipeline for termbase population, which starts with collecting and preparing research papers, project documentation, and textbooks in Serbian for the monolingual corpus and aligning English-Serbian texts for the bilingual parallel corpus. Also, paper dictionaries, both monolingual and bilingual are digitized, parsed and stored in an auxiliary database as structured data in XML format. Compiled resources also comprise monolingual lists derived from all available resources, interlinked with their source entries, for example Serbian list from Serbian monolingual dictionaries and Serbian part of bilingual dictionaries. Translation equivalents are retrieved from bilingual dictionaries and within the word alignment phase (more in Section 4.2), keeping again information about the original dictionary source. Extracted terms were also subject to a labeling procedure, which we will illustrate here on the example of MD. Out of 16,491 entries obtained from MD, 12,018 (73%) were  
Figure 3: The pipeline for terminology compilation (termbase population). manually classified and markers for domain and subdomain, as well as semantic labels, were assigned to them. The remaining 4473 (27%) unclassified entries included words from general lexica and some rarely used terms. The classified entries are mostly from the mining domain, more precisely, there are 4793 (40%) entries common for different areas of mining. The basic vocabulary from related domains is also included, for example, 2398 (20%) entries related to geology, hydrogeology and geography, 860 (7%) entries related to transport, rock mechanics, surveying, environment protection, safety, construction, transport and electrical engineering, while 3082 (26%) entries belong to the general technical terminology. There are also entries from basic science, for example, 885 (7%) terms related to biology, chemistry, mathematics, informatics and physics. Among entries from the mining domain, those related to a specific subdiscipline of mining were identified by mining experts, and marked by a subdomain marker, as for example, entries related to mineral processing (251), transport (243), or underground mining (469). Additional semantic labels were also assigned, for example, material (699), device (536), machine (384), mineral (313), facility (288), instrument (279), etc. The part-of-speech was semi-automatically assigned, where only 40 entries were marked as adjectives, 250 as verbs, and all other as nouns. Lexical entry alignment with DMMRT is performed using terms on the English side of the MD. Since one English term can have several senses, such alignments are marked for manual filtering. An indicator is used for status: automatic relation or manually evaluated. A terminological dictionary must accompany each entry with a scientifically and lexicographically correct definition [45].