Kenneth Baclawski et. al. PSB 2000 2002/11/7 Sa-Im Shin Knowledge Representation and Indexing using the Unified Medical Language System Kenneth Baclawski et. al. PSB 2000 2002/11/7 Sa-Im Shin

Contents Introduction Related Work Using the UMLS to Express Document Semantics Constructing Knowledge Representations Semantic Based User Interfaces Usability Survey Survey Results Conclusion After introduction and related work, I’ll explain the method of using ~, Constructing ~ and semantic ~. Then we’ll talk about usability ~ and survey ~, finally make conclusion.

Introduction Document Understanding Syntactic understanding Lexical scanning, Morphological Analysis, Parsing Semantic Understanding : Ontology Framework for understanding & representing knowledge Meaning of the vocabulary terms <- Vocabulary in the community Unified Medical Language System (UMLS) Rich & well structured ontology in biomedical domain Need for adaptation for application There are tow kind of document ~. One is the syntactic ~ like lexical ~. Our focused understanding is semantic ~ especially ontology. Ontology is the framework ~. And meaning ~ is represented by vocabulary ~. Unified ~ is the one of the famous ontology in bioinformatics. UMLS is rich ~, but it need some processing for adaptation ~.

Related Work Ontology-based techniques Representing, indexing & retrieving documents First stage (1992 ~): Relatively small ontology World Wide Web (1996~ ) General large ontology OntoSeek, InQuizit Ontology for biological knowledge Hafner, Fridman, Schulze-Kremer UMLS Larger than OntoSeek Knowledge representation by text processing Using typical NLP techniques Since 1990, there have been many ontology-based effort for representing, ~. At first, relatively small ontology is constructed. With the spread of world ~, general and large ~ appears like OntoSeek and InQuizit. Hafner, ~ are ontology ~. And UMLS is larger ~ using ~

Using the UMLS to Express Document Semantics Ontology components for knowledge representation Semantic categories (types) : 130 Semantic relationships : 50 Categorical links : 7000 Semantic concepts : 475,000 Concept maps : Mapping from source data to semantic concept (1,000,000) Categorization : Semantic concept to semantic category (600,000) Conceptual links : Concepts & Relationship (372,808) Explanation Knowledge Representation Rules Ontology ~ are like these. The numbers in this list are the number in the UMLS.

Constructing Knowledge Representations System interface Input : Document Output : visual representation Background knowledge Domain-independent knowledge Syntactic knowledge, POS & grammar rules Domain-dependent knowledge Semantic knowledge, Terminology of the domain This paper classifies background ~ into domain-~ and domain-~. Domain-~ contains syntactic ~, and semantic ~ are domain-.

Constructing Knowledge Representations Lexeme -> conceptual units (tokens) Original source data -> semantic concept of ontology Extracting elementary units (lexeme) Extracting protein & chemical names Parse tree & anaphoric references -> knowledge representation Knowledge representation rules : Condition & action Fragmenting & indexing Distributed high-performance indexing engine Sequence of tokens -> parse tree Anaphora construction : Pronoun -> Anaphoric references Reducing ambiguity by ontology This figure show the whole processing for constructing ~. First scanning module, we ~, used domain independent ~ is word ~ and domain-dependent ~ is the domain-~. Then throughout tokenization, we ~. We use ~ in domain-independent ~ and ~ in domain-dependent ~. In parsing ~, we ~ with domain-independent grammar ~ and domain-dependent relationship ~, and the result of this module are parse ~. With these parse ~ we represent knowledge with knowledge ~ containing various condition ~. And for fragmenting ~, we use distributed ~.

Semantic Based User Interfaces Developing user interface Insight into the queries & retrieved data Keynets Diagrammatic knowledge representation User interface is developed by diagrammatic ~ keynets to help understand queries ~.

Usability Survey Line Box question, description of a pharmacological product Medline keywords in the text appearing in the UMLS Corresponding biomedical keynet representation of biomedical text Line Relationship between two concepts Label : relationship type (property) Dot : Source of relationship (statement) cf) object / predicate This figure shows the shape of user survey. Diagram in the center is keynet representation. Box represents ~ with word ~. Line shows the relationship ~. Label express relationship ~ like “part of ~”, and dot mark source ~. And especially in the case of “part of”, we divide boxes into object ~. Survey contains three example. Biomedical text is a question, ~, biomedical keywords are medline ~, and previous biomedical keynet. Box Representing semantic concept Word or short phrase in UMLS

Usability Survey Feedback Semantic differential Feedback for keynet Interpreted nine questions Gauging the subject’s overall reaction 0 to 9 Feedback for keynet Using semantic differential Presentation of keynet Comparison of three examples Also we feedback our results using semantic ~. Semantic ~ is interpreted ~ to gauge ~ and the scale is 0 ~. Feedback evaluate the presentation ~ by comparison ~.

Survey Results Ease of Understanding Limit complexity High understandability Limit complexity More detailed views when user’s request Keyword vs. Keynet Keynet is more useful than keyword in some cases These survey results help easy understanding and limit complexity for high ~. More ~ are provided when ~. And keynet ~.

Conclusion Role & requirements of ontology Evaluation Constructing knowledge representation NLP tools for Using UMLS ontology Evaluation Usefulness of representing biomedical knowledge Favorable results As a conclusion, We construct knoledge ~ and NLP ~. and we evaluste the usefulness ~ then got favorable ~.

Future Works Short term goal Long term goal Reflecting Feedback Development of new class of semantic-based search engine New usability survey Long term goal Dynamic interaction with user Expanding subject areas Short term future ~ are reflecting ~ to ~ and new ~. Long term goal is implementing dynamic ~ and expanding ~.

Discussion Issue I think this paper lacks of evaluation part. In fact, evaluation for ontology and representation is difficult problem. Do you have good idea about evaluation methods for these topics ? What do you think about important evaluated faces in these topics ? In this paper, system provide user feedback for developing system. This feedback results contains user’s evaluation and grade of various aspects about the system. And I think usefulness of the expression in other application system is also very important factor. So, we can apply this result into input of other application system, and can compare the performance with other representation or without this result.