Aaron N. Kaplan

Towards a consistent logical framework for ontological analysis

In their framework for ontological analysis, Guarino and Weltyprovide a number of insights that are useful for guiding the designof taxonomic hierarchies. However, the formal statements of theseinsights as logical schemata are flawed in a number of ways,including inconsistent notation that makes the intended semanticsof the logic unclear, false claims of logical consequence, anddefinitions that provably result in the triviality of some of theirproperty features. This paper makes a negative contribution, bydemonstrating these flaws in a rigorous way, but also makes apositive contribution wherever possible, by identifying theunderlying intuitions that the faulty definitions were intended tocapture, and attempting to formalize those intuitions in a moreaccurate way.

Query reformulation and refinement using NLP-based sentence clustering

We have developed an interactive query refinement tool that helps users search a knowledge base for solutions to problems with electronic equipment. The system is targeted towards non-technical users, who are often unable to formulate precise problem descriptions on their own. Two distinct but interrelated functionalities support the refinement of a vague, non-technical initial query into a more precise problem description: a synonymy mechanism that allows the system to match non-technical words in the query with corresponding technical terms in the knowledge base, and a novel refinement mechanism that helps the user build up successively longer and more precise problem descriptions starting from the seed of the initial query. A natural language parser is used both in the application of context-sensitive synonymy rules and the construction of the refinement tree.

Finding Quality: A Multilingual Search Engine for Educational Research

To develop a field specific and multilingual search-engine, numerous algorithms are needed in addition to a general-purpose search engine. Here we describe the focal areas of development done in EERQI: Automatic classification for educational research, multilingual retrieval, query extension and relevance ranking. The classification algorithms, developed in EERQI enable a crawler to identify relevant objects with respect to a scientific field; the multilingual algorithms allow the retrieval of documents in several languages; query extension proposes related query terms to the user; relevance ranking is enhanced by semantic analysis.

Creation and Maintenance of Query Expansion Rules

In an information retrieval system, a thesaurus can be used for query expansion, i.e. adding words to queries in order to improve recall. We propose a semi-automatic and interactive approach for the creation and maintenance of domain-specific thesauri for query expansion. Domain-specific thesauri are especially required in highly technical domains where the use of general thesauri for query expansion introduces more noise than useful results. Our semi-automatic approach to thesaurus creation constitutes a good compromise between fully manual approaches, which produce high-quality thesauri but at a prohibitively high cost, and fully automatic approaches, which are cheap but produce thesauri of limited quality. This article describes our approach and the architecture of the system implementing it, named Cannelle. It exploits user query logs and natural language processing to identify valuable synonymy candidates, and allows editors to interactively explore and validate these candidates in the context of a domain-specific searchable knowledge base. We evaluated the system in the domain of online troubleshooting, where the proposed method yielded an improvement in the quality of the search results obtained.