Kazuhiro Kuwabara

Procedural knowledge is essential in specialized domains, and natural language tools for retrieving procedural knowledge are necessary for non-expert users to facilitate their understanding and learning. In this study, we focus on function decomposition trees, a framework for representing procedural knowledge, and propose a natural language interface leveraging Retrieval-Augmented Generation (RAG). The natural language interface converts the user’s inputs into SPARQL queries, retrieving relevant data and subsequently presenting them in an accessible and chat-based format. Such a flexible and purpose-driven search facilitates users’ understanding of functions of artifacts or human actions and their performance of these actions. We demonstrate that the tool effectively retrieves actions, goals, and dependencies using an illustrative real-world example of a function decomposition tree. In addition, we evaluated the system by comparing it with ChatGPT 4o and Microsoft GraphRAG. The results suggest that the system can deliver responses that are both necessary and sufficient for users’ needs, while the outputs of other systems lack the key elements and return redundant information.

Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful networks is the abundant amount of unstructured text data available through the web. The efforts to turn unstructured text data into a network have spawned different research endeavors, including the simplification of the process. This study presents the design and implementation of TraCER, a pipeline that turns unstructured text data into a graph, targeting the business networking domain. It describes the application of natural language processing techniques used to process the text, as well as the heuristics and learning algorithms that categorize the nodes and the links. The study also presents some simple yet efficient methods for the entity-linking and relation classification steps of the pipeline.

This paper presents the use of gamified crowdsourcing for knowledge content validation. Constructing a high-quality knowledge base is crucial for building an intelligent system. We develop a refinement process for the knowledge base of our word retrieval assistant system, where each piece of knowledge is represented as a triple. To validate triples acquired from various sources, we introduce yes/no quizzes and present them to many casual users for their inputs. Only the triples voted “yes” by a sufficient number of users are incorporated into the main knowledge base. Users are incentivized by rewards based on their contribution to the validation process. To ensure transparency of the reward-giving process, blockchain is utilized to store logs of the users’ inputs from which the rewards are calculated. Different strategies are also proposed for selecting the next quiz. The simulation results indicate that the proposed approach has the potential to validate knowledge contents. This paper is a revised version of our conference paper presented at the 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020).

In recent years, companies have been seeking communication skills from their employees. Increasingly more companies have adopted group discussions during their recruitment process to evaluate the applicants’ communication skills. However, the opportunity to improve communication skills in group discussions is limited because of the lack of partners. To solve this issue as a long-term goal, the aim of this study is to build an autonomous robot that can participate in group discussions, so that its users can repeatedly practice with it. This robot, therefore, has to perform humanlike behaviors with which the users can interact. In this study, the focus was on the generation of two of these behaviors regarding the head of the robot. One is directing its attention to either of the following targets: the other participants or the materials placed on the table. The second is to determine the timings of the robot’s nods. These generation models are considered in three situations: when the robot is speaking, when the robot is listening, and when no participant including the robot is speaking. The research question is: whether these behaviors can be generated end-to-end from and only from the features of peer participants. This work is based on a data corpus containing 2.5 h of the discussion sessions of 10 four-person groups. Multimodal features, including the attention of other participants, voice prosody, head movements, and speech turns extracted from the corpus, were used to train support vector machine models for the generation of the two behaviors. The performances of the generation models of attentional focus were in an F-measure range between 0.4 and 0.6. The nodding model had an accuracy of approximately 0.65. Both experiments were conducted in the setting of leave-one-subject-out cross validation. To measure the perceived naturalness of the generated behaviors, a subject experiment was conducted. In the experiment, the proposed models were compared. They were based on a data-driven method with two baselines: (1) a simple statistical model based on behavior frequency and (2) raw experimental data. The evaluation was based on the observation of video clips, in which one of the subjects was replaced by a robot performing head movements in the above-mentioned three conditions. The experimental results showed that there was no significant difference from original human behaviors in the data corpus and proved the effectiveness of the proposed models.

This paper presents a conversation strategy for interactive recommendations using a chatbot. Chatbots have recently been attracting attention for their use as a flexible user interface. To develop an effective chatbot, it is important to determine what kind of questions to ask, what information should be provided, and how to process a user's responses for a given task. In this paper, we target a chatbot that uses a graphical user interface (GUI) and focus on the task of recommending an item that suits a user's preference. We propose a conversation strategy where a chatbot combines questions about a user's preferences and recommendations while soliciting user's feedback to them. The balance between the questions and recommendations is controlled by changing the parameter values. In addition, we propose a simulation model to evaluate the performance of interactive recommendation under different parameter values. The simulation results with a prototype dataset are presented and discussed.

This paper discusses a gamification design for knowledge base refinement. We present a simulation model for game playing and examine the possibility of estimating the effects of game rule design. The maintenance of a knowledge base involves human intervention and is one of the application domains of human computation. Using the concept of crowdsourcing, refinement tasks can be delegated to many casual users over a network. In addition, gamification, such as games with a purpose (GWAP), is a useful idea to motivate workers in crowdsourcing by making tasks enjoyable. For effective gamification, designing the game rules is critical. In this paper, we present a model for simulating the gamified knowledge base refinement process to estimate the effects of different game rule designs beforehand. We use a rental apartment FAQ system as an example knowledge base and show that by invoking game playing when a problem is found in a knowledge base, it can be properly updated. In addition, we discuss the effectiveness of using a calibration quiz to estimate the reliability of users.