Se-Young Park

An automatic translation of tags for multimedia contents using folksonomy networks

This paper proposes a novel method to translate tags attached to multimedia contents for cross-language retrieval. The main issue in this problem is the sense disambiguation of tags given with few textual contexts. In order to solve this problem, the proposed method represents both tags and its translation candidates as networks of co-occurring tags since a network allows richer expression of contexts than other expressions such as co-occurrence vectors. The method translates a tag by selecting the optimal one from possible candidates based on a network similarity even when neither the textual contexts nor sophisticated language resources are available. The experiments on the MIR Flickr-2008 test set show that the proposed method achieves 90.44% accuracy in translating tags from English into German, which is significantly higher than the baseline methods of a frequency based translation and a co-occurrence-based translation.

Program plagiarism detection using parse tree Kernels

Many existing plagiarism detection systems fail in detecting plagiarism when there are an abundant garbage in the copied programs. This is because they do not use the structural information efficiently. In this paper, we propose a novel plagiarism detection system which uses parse tree kernels. By incorporating parse tree kernels into the system, it efficiently handles the structural information within source programs. A comparison with existing systems such as SID and JPlag shows that the proposed system can detect plagiarism more accurately due to its ability of handling structural information.

A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations

This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively.

Learning the emergent knowledge from annotated blog postings

Emergent knowledge does not come from a particular document or a particular knowledge source, but comes from a collection of documents or knowledge sources. This paper proposes a system which combines social web content and semantic web technology to process the emergent knowledge from the blogosphere. The proposed system regards blog postings as experiences of people on particular topics. By annotating postings in the selected domains with ontology vocabularies, the system collects experiences from various people into an ontology about people and experiences. The system processes this ontology with semantic rules to find the emergent knowledge. Users can access previously unavailable facts, concepts and trends which are emerging from social web content by using the proposed system.

Discriminating Meaningful Web Tables from Decorative Tables Using a Composite Kernel

Information extraction from world wide web has been paid great attention to. Since a table is a well-organized and summarized knowledge expression for a domain, it is of great importance to extract information from the tables. However, many tables in web pages are used not to transfer information but to decorate the pages. Therefore, it is one of the most critical tasks in web table mining to discriminate the meaningful tables from the decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for the discrimination. This paper proposes a novel method to discriminate them using a composite kernel which combines a parse tree kernel and a linear kernel. Since a web table is represented as a parse tree by a HTML parser, the parse tree kernel can be naturally used in determining the similarity between trees, and the linear kernel with content features is used to make up for the weak points of the parse tree kernel. The support vector machines with the composite kernel distinguish with high accuracy the meaningful tables from the decorative ones. A series of experiments show that the proposed method achieves the state-of-the-art performance.

An automatic ontology population with a machine learning technique from semi-structured documents

The manual design of an ontology usually defines the concepts for the domain, but the individual instances of the concepts are often missing though they are important in using the ontology as a knowledge base. This is due to high cost of the manual construction of individuals. In order to tackle this problem, this paper proposes an automatic method for ontology population. The knowledge source for ontology population used in this paper is the web tables of which structure is relatively well organized. Since a web table can be analyzed into a parse tree, the most appropriate concept within the ontology for a given web table is determined by a kernel method, so-called a parse tree kernel. Then, the table is populated as an individual of the concept. According to the experimental results on a large ontology with a great number of concepts, the proposed method achieves 62.35% of accuracy for a number of web tables.

Ontology Population from Unstructured and Semi-structured Texts

Legacy information search systems have limitation that it does not consider semantic information but just lexical information such as keywords. A semantic web is expected to solve such limitation of present systems. In constructing semantic web, an ontology is believed to be a must. However, the ontology construction is very difficult. It requires great human efforts, since the creation of individuals is a time consuming task. Thus, there is a potential need for automatic or semiautomatic ontology population system, which greatly alleviates the human efforts. This paper proposes a method for an ontology population, in which the population is processed by computing the overlap between instances and concepts. This method is very simple but shows high performance.

Self-Organizing n-gram Model for Automatic Word Spacing

An automatic word spacing is one of the important tasks in Korean language processing and information retrieval. Since there are a number of confusing cases in word spacing of Korean, there are some mistakes in many texts including news articles. This paper presents a high-accurate method for automatic word spacing based on self-organizing η-gram model. This method is basically a variant of η-gram model, but achieves high accuracy by automatically adapting context size.In order to find the optimal context size, the proposed method automatically increases the context size when the contextual distribution after increasing it dose not agree with that of the current context. It also decreases the context size when the distribution of reduced context is similar to that of the current context. This approach achieves high accuracy by considering higher dimensional data in case of necessity, and the increased computational cost are compensated by the reduced context size. The experimental results show that the self-organizing structure of η-gram model enhances the basic model.

A Syllable Kernel based Sentiment Classification for Movie Reviews

In this paper, we present an automatic sentiment classification method for on-line movie reviews that do not contain explicit sentiment rating scores. For the sentiment polarity classification, positive or negative, we use a Support Vector Machine classifier based on syllable kernel that is an extended model of string kernel. We give some experimental results which show that proposed syllable kernel model can be effectively used in sentiment classification tasks for on-line movie reviews that usually contain a lot of grammatical errors such as spacing or spelling errors.

Experience Search: Accessing the Emergent Knowledge from Annotated Blog Postings

Emergent knowledge does not come from a particular document or a particular knowledge source, but comes from a collection of documents or knowledge sources. This paper proposes a system which combines the social web contents and the semantic web technology to process the emergent knowledge from the blogosphere. The proposed system regards blog postings as experiences of people on particular topics. By annotating postings in the selected domains with ontology vocabularies, the system collects experiences from various people into an ontology about people and experiences. The system processes this ontology with semantic rules to find the emergent knowledge. Users can access previously unavailable facts, concepts and trends which are emerging from system.