Session Program

Abstract - In this paper, we present a summarization agent based on Fuzzy Markup Language (FML) and its application for meeting schedule data analysis. The knowledge base and rule base of FML are constructed by referring the meeting schedule ontology of Research and Development (RD) office in National University of Tainan (NUTN) from Jan. 2011 to Jul. 2015. We propose an intelligent agent to retrieve the meeting activities of RD Office from open meeting schedule database of NUTN. There are three categories belonging to the RD meeting schedule ontology, including an International Affairs division, an Academic Development division, and an Industry- Academia Collaboration division. In addition, we apply the Natural Language Processing (NLP) open API for Chinese text mining and document preprocessing. Finally, the proposed FML- based summarization agent is combined with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to construct a meeting summarization robot agent. Experimental results show that the proposed agent can work effectively.

Abstract - Inliers and Outliers are widely studied in the world of databases. In the early 80's, L. A. Zadeh proposed an interesting approach to the concept of 'prototype' which contrasts with the classical theories of prototypes from the field of cognitive psychology. This definition has not been used much in fuzzy scientific proposals. Metasearch engines are often designed and used for Information Retrieval tasks because they are cheap and easy to develop, as they get their results from multiple search engines. In this paper, a definition of Inliers and Outliers is presented and related to Zadeh's concept of fuzzy prototype: Inliers with the prototype of the borderline elements and Outliers with the prototype of the bad elements of a dataset. Then, this approach is used for dealing with the User Profiles in a real Scientific Metasearcher with the aim of proposing recommendations, sending warnings and in general enriching the management of these User Profiles. An example and experiments proposal in this real system are also presented.

Abstract - A search process becomes an essential component of everyday routine for many users. Users constantly look for new, and more or less relevant items that they require for work or for entertainment. On multiple occasions, they try to find other users who match their 'likes' and 'dislikes'. Many different methods and approaches have been proposed and developed to address such needs. The Pythagorean Fuzzy Sets have been proposed as a new class of non-standard fuzzy sets. They are related to the idea of Pythagorean membership grades (a,b) that satisfy the requirement a2 + b2 $<$ 1. The interesting aspect of those types of sets is their ability to express a positive support a - a positive membership grade, and a negative support b - a negative membership grade. In this paper, we propose a method based on the application of Pythagorean fuzzy relations for identifying a degree of matching between users based on their evaluations of items. We use triangular compositions to determine users that match positive evaluations, and users that agree on negative ones. The usage of Pythagorean fuzzy sets allows us to take into consideration both positive and negative aspects of evaluations and find users who like or dislike at least the same items as a given user likes or dislikes. The proposed approach is used to identify users that evaluate movies in a similar way.

Abstract - In the current data-rich / knowledge-poor world, humans require machine assistance to summarize, analyze and understand a situation and the trends in events. The idea of collaborative intelligence enables humans to focus on higher-level tasks involving insight and understanding, whilst machines deal with gathering, filtering and processing data into a convenient and understandable form. In this paper, we propose graded concept lattices as a representation for exchanging information between machine and human in a collaborative intelligent system. Graded concepts allow summarization at multiple levels of discernibility (granularity). We present a novel incremental algorithm to find a graded concept lattice. The lattice can be used to identify associations in data at multiple levels of discernibility.

Abstract - Fuzzy Markup Language (FML) presented by IEEE Computational Intelligence Society (CIS) has been an IEEE Standard since May 2016. It is an XML-based language for designer to easily construct the knowledge base and rule base of the developed fuzzy logic system. In this paper, we propose an FML-based linguistic classification agent and apply it to popular Chinese songs' classification in social media environment. In addition, the lyrics are retrieved from Youtube, Facebook or Google+, and then we adopt Natural Language Processing (NLP) mechanism to deal with the document preprocessing. First, the domain experts construct the classification ontology model and design related categories for the application domain. Moreover, the fuzzy concept sets are also adopted in the related categories. Then, the Chinese Knowledge Information Processing (CKIP) tool is utilized to deal with the Chinese documents of the songs. Finally, the FML-based knowledge base and rule base of the classification agent are constructed for inferring the related categories of the song. The Fujisoft robot PALRO receives the classified songs and plays the song for the desired users. Experimental results show the proposed classification agent can work correctly.

Abstract - In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decisionmaking problem, which in turn translates into two subproblems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings.