Takayuki Iida

Designing mobile search interface with query term extraction

Web searching with mobile phones is still not as easy as with Personal Computers (PCs). To solve the problem, we proposed a one-touch search application using first query term extraction from browsed Web pages [1]. This paper reports on a preliminary experiment that we conducted with 50 subjects in order to design the interface of the application. The results of the experiment indicate that although providing query term candidates to users reduces the cost of query input, the complexity of the query input process poses a problem.

First query term extraction from current webpage for mobile applications

Inputting query terms on a mobile terminal is frustrating because of device limitations though mobile web search is becoming popular. Query prediction is a promising approach for mobile search. In the literature, however, little attention has been paid to the first query term, though there are many reports on query expansion or second query recommendation. In this paper, we propose a first query prediction method that enables users to search related information for the current browsed webpage with an easier interface such as touch operation without any background search processes. The proposed method consists of body-text extraction, candidate query term extraction, and a scoring process. We also implemented a one-touch search application that works for Japanese webpages at a practical speed on a smartphone. According to our closed evaluation with 299 webpages and open evaluation with 14 users, our method achieved practical quality.

Semi-automatic Evaluation System for Supporting Term Extraction Application Development

Important term extraction from the current web page is a common process in many client-based web applications such as query-free search, search query recommendation, information extraction, user profiling, and personalization. However, the tuning cost is large for deciding parameters when updating modules or dictionaries, or when porting an application for other languages or devices. We propose an evaluation system to collect web pages and corresponding correct data with visual check and automatic tuning of parameters. We confirmed the practicality of this system through the development and the two-month refinement of first query term extraction module.

Pattern-based semantic tagging for ontology population

Ontology population has emerged as an increasingly importantproblem in semantic web services. In this paper, we propose a method usingnamed entity recognition that extracts keywords from Web pages in order topopulate a product ontology. The semantic classification determines meaningsof terms and phrases by heuristic rules after the morphological analysis. Inaddition, our method classifies vocabularies into different semantic tags. Firstly,it records several lists of semantic tags to a history database. Then, we definesome rules from the lists to extract a product name. Finally, the rules build andrefine the product ontology semi-automatically. According to an evaluation,proposed method achieved 87.1% precision and 87.4% recall. Thus, it cansuggest some instances, and it decreases cost of updating the ontology.