Myung-Duk Hong

Social filtering using social relationship for movie recommendation

Traditional recommendation systems provide appropriate information to a target user after analyzing user preferences based on user profiles and rating histories. However, most of people also consider the friends opinions when they purchase some products or watch the movies. As social network services have been recently popularized, many users obtain and exchange their opinions on social networks. This information is reliable because they have close relationships and trust each other. Most of the users are satisfied with the information. In this paper, we propose a recommendation system based on advanced user modeling using social relationship of users. For the user modeling, both direct and indirect relations are considered and the relation weight between users is calculated by using six degrees of Kevin Bacon. From the experimental results, our proposed social filtering method can achieve better performance than a traditional user-based collaborative filtering method.

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.

Automatic indexing of cooking video by using caption-recipe alignment

In recent years, the internet use has led to rapid increases in multimedia resources. In this regard, handling multimedia data has become an important issue in information retrieval fields, and multimedia indexing plays an essential role in handling multimedia data. Textual metadata plays an important role in describing and analyzing multimedia resources. However, because only few multimedia resources provide textual metadata, there is a need for methods to generate the metadata. This paper proposes an automatic method for indexing cooking videos using textual data. The proposed method consists of web crawling for cooking videos and food recipes, the extraction of time-stamped text captions, and caption-recipe alignment using a dynamic programming technique. With information on caption-recipe alignment, automatic indexing is performed for cooking videos. The experimental results show that the proposed approach achieves highly accurate indexing. The user interface allows users to easily move to specific pinpoints with indexed videos.

An Unsupervised Approach for Identifying the Infobox Template of Wikipedia Article

Wikipedia infoboxes serve as important structured information source in the web. To author infobox for a particular article, volunteers required a considerable amount of manual effort to identify the respective infobox template. Thus, an automatic process to mark infobox template might be useful and beneficial for the Wikipedia contributors. In this paper, we present a Natural Language Processing (NLP)-based automated approach to identify the infobox template in an unsupervised fashion. The proposed approach has been developed by using semantic relations (hyponym and holonym) and word features of Wikipedia articles. Our approach works in three steps: first it processes the raw text of the article to generate sets of words, next it apply the proposed algorithm to identify the infobox type and finally point out the infobox template from the large pool of template list. The effectiveness of the proposed approach has been proved in terms of autonomous and accuracy, by a data-driven experiment.

Building a Semantic Social Network Based on Interpersonal Relationships

With the emergence of the Smart phone, people can use Online Social Network Services ubiquitously, leading to a significant increase of the number of participants in online social networks. Under these circumstances, online users will require an intelligent and intuitive social relationship management system such as the ontology-driven browsing method. In this paper, to build a user-centered semantic social network and to represent entities and relationships with ontology to improve retrieval performance of the semantic social network, we will design our ontology extended from FOAF, RELATIONSHIP and propose a new method to compute closeness among friends using resources on social networks. Furthermore, we evaluate our ontology-driven browsing on via implementing a prototype system.

Automatic Interactive Video Authoring Method via Object Recognition

Interactive video is a type of video which provides interactions for obtaining video related information or participating in video content. However, authors of interactive video need to spend much time to create the interactive video content. Many researchers have presented methods and features to solve the time-consuming problem. However, the methods are still too complicated to use and need to be automated. In this paper, we suggest an automatic interactive video authoring method via object recognition. Our proposed method uses deep learning based object recognition and an NLP-based keyword extraction method to annotate objects. To evaluate the method, we manually annotated the objects in the selected video clips, and we compared proposed method and manual method. The method achieved an accuracy rate of 43.16% for the whole process. This method allows authors to create interactive videos easily.

Enhanced user modeling based on link attributes for recommendation system

Recommendation Systems apply social relationship to enhance recommendation performance and allow users to get reliable information. This paper propose an enhanced user modeling based on link attributes of the social network. For this, similarity values for users in terms of their item preferences are calculated. A relationship-centric neighbor group is identified using social relationships explicitly presented by users, and these social relationships are represented by links considering information on both direct and indirect relationships. These link attributes consist of the link degree and strength. The link degree is identified by using six degrees of Kevin Bacon and is represented through numeric values for relationships between users. The link strength is calculated by considering the number of directly and indirectly connected relationships with the target user and is applied to the proposed user-modeling method. The experimental results demonstrate that the proposed recommender system using the user-modeling method based on link attributes shows better prediction accuracy and recommendation quality than benchmark systems, suggesting that user modeling based on link attributes in a recommender system can enhance the usefulness of recommendations.

Temporal Ontology Representation and Reasoning Using Ordinals and Sets for Historical Events

In question-and-answer (QA) systems, various queries need to be processed. In particular, those queries such as temporal information require complex query generation processes. This paper proposes a temporal representation model that can support qualitative and quantitative temporal information on historical ontology by applying the concept of ordinals and sets and introduces operators that allow a QA system to easily handle complex temporal queries. To verify the effectiveness of the proposed model and operators, historical scenarios are presented to show that they can effectively handle complex temporal queries.

Text-Based Semantic Video Annotation for Interactive Cooking Videos

Videos represent one of the most frequently used forms of multimedia applications. In addition to watching videos, people control slider bars of video players to find specific scenes and want detailed information on certain objects in scenes. However, it is difficult to support user interactions in current video formats because of a lack of metadata for facilitating such interactions. This paper proposes a text-based semantic video annotation system for interactive cooking videos to facilitate user interactions. The proposed annotation process includes three parts: the synchronization of recipes and corresponding cooking videos based on a caption-recipe alignment algorithm; the information extraction of food recipes based on lexico-syntactic patterns; and the semantic interconnection between recognized entities and web resources. The experimental results show that the proposed system is superior to existing alignment algorithms and effective in semantic cooking video annotation.

Temporal Interval Reasoning with Korean Historical Event

In historical event knowledge base, relationships between time event intervals are complex that is not easy to express its complex relations. We make the time event intervals reasoning in order to express complicated relations among events in Korean historical event based on 13 Allens temporal interval relations, but it takes too much time to do the reasoning. In pre-computed model, if we have quantitative information in Korean history dataset, we pre-compute time event relations from possible pair of quantitative event intervals to qualitative event relation triples with Allens operator model. In this paper, we propose effective hybrid algorithm, which is a combination of pre-computed model and backward chaining to get a real time query and perform the reasoning more effectively between time event intervals in Korean history dataset with more than 3 billion triples. As user imposes questions in English, we reformulate it into qualitative structure query in which consists of Allens operators and then look up for answer in the existing qualitative answers that are already pre-computed. Otherwise, we infer only necessary entries from quantitative temporal information to compute the inferred facts to get the answer during query time based on backward chaining. We implemented this approach with a Spark Scala framework, which is a new parallel system programming that is capable of processing large-scale dataset efficiently and speeding up our reasoning process. With this reasoning process, we get a real time query with response times in a small number of milliseconds.