# Joint Extraction of Multiple Relations and Entities from Building Code Clauses
###### Abstract
[21] designed a pipeline combining named entity recognition (NER) with sentence classification for the extraction of typical temporal relationships between two construction activities (named entities). Undoubtedly, these machine-learning-based methods offer a promising approach for the automatic extraction of regulatory information without the need for handcrafted rules. However, most of these studies have treated regulation information extraction as a process of NER and have inadequately addressed the complexity of extracting the semantic relations between entities. For instance, [PERSON] et al. [21] attempted to infer relations through sentence type classification. However, this paradigm is confined to the extraction of one relation from one simple clause involving two entities. By contrast, in practice, many Chinese code clauses describing engineering constraints are written in natural language and express two or more relations among a set of entities. In particular, due to the concise nature of code language, the entities in code clauses tend to appear at a high density. Moreover, overlapping relations often exist, meaning that multiple relations involve a common entity (herein called the \"overlapping relation\" problem). Recent research has explored the joint extraction of entities and relations, which has shed some light on the automatic extraction of multiple relations from code clauses with complex structures. In contrast to the separate NER and relation classification tasks, the aim of joint extraction is to simultaneously detect multiple entities along with their relations using a single model [22]. Unfortunately, the current joint extraction models are not suitable for analyzing code documents since the entities and relations used for labeling documents and training the models are often domain-specific and purpose-dependent. Furthermore, overlapping relations, which frequently exist in code documents, exacerbate the difficulty of information extraction. Therefore, the main purpose of this paper is to extract complex semantic relationships residing in code documents. For this purpose, a joint extraction model is developed based on the application of a decomposition strategy. Meanwhile, a collection of domain-specific entity and relation types is proposed from the viewpoint of the ontology of building design. Compared with the existing machine-learning-based regulation information extraction, this research contributes to the body of knowledge by providing a joint learning approach to enhance the acquisition of complex semantic relations in terms of multiple relations and overlapping relations. Remarkably, this paper attempts to better utilize NLP techniques with the assistance of the ontological description of semantic relations and named entities in the context of regulation information extraction. The remaining of this paper is organized as follows: Section 2 presents the semantic types of named entities and relations while the framework of the joint extraction model is proposed with algorithm description of each module. In Section 3, the experiment process and the hardware platform are introduced, and later the resultant data are evaluated to validate the performance of the proposed model. Finally, the conclusions and limitations are summarized in Section 4.