YoungGyun Hahm

Linking Korean Predicates to Knowledge Base Properties

Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.

Toward matching the relation instantiation from DBpedia ontology to Wikipedia text: fusing FrameNet to Korean

Nowadays, there are many ongoing researches to construct knowledge bases from unstructured data. This process requires an ontology that includes enough properties to cover the various attributes of knowledge elements. As a huge encyclopedia, Wikipedia is a typical unstructured corpora of knowledge. DBpedia, a structured knowledge base constructed from Wikipedia, is based on DBpedia ontology which was created to represent knowledge in Wikipedia well. However, DBpedia ontology is a Wikipedia-Infobox-driven ontology. This means that although it is suitable to represent essential knowledge of Wikipedia, it does not cover all of the knowledge in Wikipedia text. In overcoming this problem, resources representing semantics or relations of words such as WordNet and FrameNet are considered useful. In this paper we determined whether DBpedia ontology is enough to cover a sufficient amount of natural language written knowledge in Wikipedia. We mainly focused on the Korean Wikipedia, and calculated the Korean Wikipedia coverage rate with two methods, by the DBpedia ontology and by FrameNet frames. To do this, we extracted sentences with extractable knowledge from Wikipedia text, and also extracted natural language predicates by Part-Of-Speech tagging. We generated Korean lexicons for DBpedia ontology properties and frame indexes, and used these lexicons to measure the Korean Wikipedia coverage ratio of the DBpedia ontology and frames. By our measurements, FrameNet frames cover 73.85% of the Korean Wikipedia sentences, which is a sufficient portion of Wikipedia text. We finally show the limitations of DBpedia and FrameNet briefly, and propose the outlook of constructing knowledge bases based on the experiment results.

Korean Linked Data on the Web: Text to RDF

Interlinking data coming from different sources has been a long standing goal [4] aiming to increase reusability, discoverability, and as a result the usefulness of information. Nowadays, Linked Open Data (LOD) tackles this issue in the context of semantic web. However, currently most of the web data is stored in relational databases and published as unstructured text. This triggers the need of (i) combining the current semantic technologies with relational databases; (ii) processing text integrating several NLP tools, and being able to query the outcome using the standard semantic web query language: SPARQL; and (iii) linking the outcome with the LOD cloud. The work presented here shows a solution for the needs listed above in the context of Korean language, but our approach can be adapted to other languages as well.