Wenlei Mao

The phrase-based vector space model for automatic retrieval of free-text medical documents

Objective: To develop a document indexing scheme that improves the retrieval effectiveness for free-text medical documents. Design: The phrase-based vector space model (VSM) uses multi-word phrases as indexing terms. Each phrase consists of a concept in the unified medical language system (UMLS) and its corresponding component word stems. The similarity between concepts are defined by their relations in a hypernym hierarchy derived from UMLS. After defining the similarity between two phrases by their stem overlaps and the similarity between the concepts they represent, we define the similarity between two documents as the cosine of the angle between their corresponding phrase vectors. This paper reports the development and the validation of the phrase-based VSM. Measurement: We compare the retrieval effectiveness of different vector space models using two standard test collections, OHSUMED and Medlars. OHSUMED contains 105 queries and 14,430 documents, and Medlars contains 30 queries and 1033 documents. Each document in the test collections is judged by human experts to be either relevant or non-relevant to each query. The retrieval effectiveness is measured by precision and recall. Results: The phrase-based VSM is significantly more effective than the current gold standard-the stem-based VSM. Such significant retrieval effectiveness improvements are observed in both the exhaustive search and cluster-based document retrievals. Conclusion: The phrase-based VSM is a better indexing scheme than the stem-based VSM. Medical document retrieval using the phrase-based VSM is significantly more effective than that using the stem-based VSM.

Designing Triggers with Trigger-By-Example

One of the obstacles that hinder database trigger systems from their wide deployment is the lack of tools that aid users in creating trigger rules. Similar to understanding and specifying database queries in SQL3, it is difficult to visualize the meaning of trigger rules. Furthermore, it is even more difficult to write trigger rules using such text-based trigger rule languages as SQL3. In this paper, we propose TBE (Trigger-By-Example) to remedy such problems in writing trigger rules visually by using QBE (Query-By-Example) ideas. TBE is a visual trigger rule composition system that helps the users understand and specify active database triggers. TBE retains benefits of QBE while extending features to support triggers. Hence, TBE is a useful tool for novice users to create simple triggers in a visual and intuitive manner. Further, since TBE is designed to hide the details of underlying trigger systems from users, it can be used as a universal trigger interface.

Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining

Efficient document retrieval that answers a user query is achieved by indexing. The current technique uses word stems to index a document [1]. Such a technique suffers from the inability to match words in a query with their related words such as synonyms, hypernyms and hyponyms [2] in the documents. Therefore, there are recent attempts to index the document based on conceptual terms, however, the knowledge sources are usually incomplete. As a result, past research reveals that although using conceptual terms for document indexing can solve some of the problems, it cannot outperform the word-stem-based model [3, 4, 5, 6]. To remedy the deficiency of the knowledge sources, we propose a phrase-based indexing model where we parse a document into phrases based on the conceptual terms in domain specific knowledge sources, and calculate the similarity between two documents using both the similarity between the concepts and the common word stems in them. Including word stems in addition to concepts in document similarity evaluation compensates for the shortcoming of using concept terms alone caused by the incompleteness of the knowledge sources.

KMeX: A Knowledge-Based Digital Library for Retrieving Scenario-Specific Medical Text Documents

Publisher Summary This chapter presents a new knowledge-based approach to mitigate problems related to scenario-specific information retrieval (IR). The use of metathesaurus and semantic structure in the UMLS to extract key concepts from the free text for indexing, phrase-based indexing for representing similar concepts, and query expansion to improve the probability of matching query terms with the terms in the document is preferred. A scenario typically refers to a specific health care task, such as searching for treatment methods for a specific disease. Although traditional systems are useful for general information retrieval, these systems cannot support scenario-specific IR because: the terms in the query posed by the user may not use a standardized medical vocabulary; there is no effective technique to represent synonyms, phrases, and similar concepts in free text; and the terms used in a query and those used in a document for representing the same topic may be mismatched. A new knowledge-based approach for retrieving scenario-specific free-text documents has been developed, which consists of three integrated components: IndexFinder, phrase-based VSM, and knowledge-based query expansion. IndexFinder extracts key terms from free text, generating conceptual terms by permuting words in a sentence rather than using the traditional techniques based on NLP. The phrase-based VSM has been developed for document retrieval. Knowledge-based query expansion techniques and the phrase-based VSM can be used in conjunction to significantly improve precision and recall.

TBE: Trigger-By-Example

TBE (Trigger-By-Example) is proposed to assist users in writing trigger rules. TBE is a graphical trigger rule specification language and system to help users understand and specify active database triggers. Since TBE borrowed its basic idea from QBE, it retained many benefits of QBE while extending the features to support triggers. Hence, TBE is a useful tool for novice users to create simple trigger rules easily. Further, since TBE is designed to insulate the details of underlying trigger systems from users, it can be used as a universal trigger interface for rule formation.