# Product Improvement Using Knowledge Mining and Effect Analogy
## 1 Introduction  
With the increasing market competition, the rapid evolution through improvement iterations of the product is a common practice for enterprises to maintain their product's competitive advantages [1; 2; 3]. Unlike new product development, which needs to open a whole new market, product improvement requires a well-defined prototype product to carry out the subsequent functional analysis and structural modeling for the design problems to meet user requirements. However, existing methods of product improvement have two problems. One is their poor pertinence. Most of the product improvements use methods of new product development, such as quality function deployment (QFD) [4], axiomatic design (AD) [5], and design for six sigma (DFSS) [6]. The other methods focus on specific applications, which cannot be used for different applications. For example, the improvement method used in telecommunications equipment [7] cannot be applied to general machinery. In particular, in complex big data environments, the cost of making design decisions for enterprises is increasing [8]. Effect is a specific description of the conversion process between product input and output based on scientific principles and system attributes including physics, chemistry, biology, and geometry [9]. Effect can be used as a design tool to fully apply multidisciplinary knowledge to maximize product improvement [10]. With the support of a network database, effect can help reduce the demand for design knowledge. An effect-based method can effectively support product improvement in product development. The effect database includes knowledge of different disciplines in a wide range of products. In order to obtain the effect knowledge for high-level product innovation, [PERSON] proposed a method of effectknowledge collection considering both characteristics of function and technical fields [11]. This method combines syntactic analysis, WordNet software, and word vector technology to extract the effect knowledge from the international patent classification text. [PERSON] and [PERSON] proposed a method to identify the TRIZ solution model of design problems based on the similarity measure [12]. They summarized 70 effects and 210 known cases from science websites. [PERSON] proposed a method to represent physical effects [13] for compatibility with the context of a particular problem to instantiate the solution model. However, the existing methods have limitations in forming the effect knowledge base, retrieving the appropriate effect, and using the effect for product improvement. This research proposes a method of product improvement with wide application potential. The novelty of the proposed method for design improvement is as follows:  
1. Ontology is introduced to represent the effect knowledge [14] and clearly define the knowledge vocabulary, relationships, and attributes to build a knowledge base for the effect expression and retrieval of the knowledge. It forms an effective tool for the effect knowledge sharing and reuse. 2. The concept of the length-time (LT) dimension is introduced to build an LT table similar to the conflict matrix. The LT dimension can effectively retrieve required design knowledge from different effects [15]. 3. The analogy design method is developed to solve similar product problems by using the existing design knowledge [16]. Engineering cases of application effects are used as the knowledge resource to guide the effect analogy from two levels of function modeling and specific structure forming. The following parts of this paper are organized as follows: Section 2 introduces basic concepts and the research status of the effect, ontology model, LT dimension, and knowledge mining and analogy design to clarify their relationships. In Section 3, a product improvement method using knowledge mining and effect analogy is proposed, including the effect knowledge base construction, LT dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping.