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Yezdi Lashkari

dblp:58/5467 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data models and query languages · 77% Information retrieval · 23%
Artificial intelligence
1 paper
Multi-agent systems · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages › query language
object-oriented query language
0.011994
Incomplete Path Expressions and their Disambiguation · SIGMOD Conference 1994
Information retrieval › query understanding
query disambiguation
0.011994
Incomplete Path Expressions and their Disambiguation · SIGMOD Conference 1994

Methods — techniques the papers use, named apart from their topics

transitive closure · 0.0optimal path computation · 0.0
YearPublicationVenuePosition
1994 Collaborative Interface Agents
Yezdi Lashkari, Max Metral, Pattie Maes
AAAI1
1994 Incomplete Path Expressions and their Disambiguation
abstract
When we, humans, talk to each other we have no trouble disambiguating what another person means, although our statements are almost never meticulously specified down to very last detail. We “fill in the gaps” using our common-sense knowledge about the world. We present a powerful mechanism that allows users of object-oriented database systems to specify certain types of ad-hoc queries in a manner closer to the way we pose questions to each other. Specifically, the system accepts as input queries with incomplete, and therefore ambiguous, path expressions. From them, it generates queries with fully-specified path expressions that are consistent with those given as input and capture what the user most likely meant by them. This is achieved by mapping the problem of path expression disambiguation to an optimal path computation (in the transitive closure sense) over a directed graph that represents the schema. Our method works by exploiting the semantics of the kinds of relationships in the schema and requires no special knowledge about the contents of the underlying database, i.e., it is domain independent. In a limited set of experiments with human subjects, the proposed mechanism was very successful in disambiguating incomplete path expressions.
Yannis E. Ioannidis, Yezdi Lashkari
SIGMOD Conference2