# A Knowledge and Semantic Fusion Method for Automatic Geometry Problem Understanding
## 7 Conclusions and Future Work  
GPU is a fundamental task in the intelligent processing of geometry. To achieve high-quality problem understanding, this paper first identifies the current deficiencies in the GPU field, analyzes the technical approaches and their limitations, and points out the issues present in existing work. In response, a knowledge and semantic fusion GPU method is proposed to address these problems. In this method, a knowledge ontology model, SGKO, is first constructed to build a knowledge base for guiding the information extraction process. For text-level information, a state machine model based on template matching is employed to extract entities and relationships directly mentioned in the problem text, while knowledge-level information is extracted using rule-based methods. During information extraction, knowledge is dynamically queried from SGKO as needed. This paper improves both the matching templates and the knowledge graph by modularizing the templates and treating knowledge as the matching object, as well as decoupling the ontology layer from the data layer in the knowledge graph. These optimizations render the methods and tools more efficient and flexible. The main innovation of the approach lies in the modular and component-based design of knowledge and templates, which, managed and maintained through a tree structure, realizes on-demand knowledge base queries and dynamic template generation and matching for the extraction of entities and relationships from problem texts. This not only guarantees high accuracy but also, on the one hand, significantly reduces the number of templates and improves method efficiency, reducing the time and space complexity of traditional rule/template-based methods from exponential to linear (text-level) or linearithmic (knowledge-level); on the other hand, it provides excellent scalability, supporting a plug-and-play expansion without the need to reconstruct existing templates or computational frameworks. Finally, the experimental results validate the effectiveness and excellent performance of the method, successfully extracting all two-level entities and relationships completely, correctly, and accurately from 91.87% of the 230 experimental problems containing complex relationship descriptions. Figure 10: Problem semantic KG (of the problem in Figure 9). The failures in entity and relationship extraction are mainly due to unhandled entity/relationship types and missing knowledge attributes for processed types. In the process of entity and relationship extraction, correctly identifying the entities associated with relationships and parsing the sentence structures that express these relationships are two indispensable factors for successful extraction; missing either can result in cases where relationships are recognized from complex text structures but subsequent failures occur due to unprocessed shared undefined entities. In other words, although the proposed method overcomes challenges arising from diverse expressions and complex sentence structures, its performance largely depends on the completeness of the SGKO knowledge base. Even though its excellent scalability alleviates the development burden to a certain extent, developers still need to invest significant effort in corpus research. Several aspects of the methods presented in this paper can be further researched and optimized: (1) further improving SGKO on the current approach by expanding support for more types of entities and relationships and enriching their corresponding knowledge, thus building a more robust knowledge base; (2) considering that the information provided by the text in geometry problems is limited and some relationships are difficult to fully capture solely through text, integrating image processing with this work is a promising direction for future research; (3) exploring the integration of this work with downstream tasks (such as automated reasoning, problem solving, and diagram drawing) to further enhance the intelligent processing level of geometry problems.