Oktie Hassanzadeh

Publications
Influence

Framework for Evaluating Clustering Algorithms in Duplicate Detection

Oktie Hassanzadeh, Fei Chiang, R. Miller, H. Lee
Computer Science
Proc. VLDB Endow.
1 August 2009

In this paper, we use Stringer to evaluate the quality of the clusters (groups of potential duplicates) obtained from several unconstrained clustering algorithms used in concert with approximate join techniques. Expand

Linked Movie Data Base

Oktie Hassanzadeh, Mariano P. Consens
Computer Science
LDOW
2009

We present a novel way of creating and maintaining large quantities of high quality links by employing state-of-the-art approximate join techniques for finding links, and providing additional RDF metadata about the quality of the links. Expand

Toward a complete dataset of drug-drug interaction information from publicly available sources

Serkan Ayvaz, J. Horn, +9 authors R. Boyce
Computer Science, Medicine
J. Biomed. Informatics
1 June 2015

A comprehensive dataset of all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. Expand

Linked open drug data for pharmaceutical research and development

Matthias Samwald, A. Jentzsch, +8 authors Susie Stephens
Computer Science, Medicine
J. Cheminformatics
16 May 2011

In this paper, we present past and ongoing work of Linking Open Drug Data (LODD) and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing. Expand

Schema Management for Document Stores

Lanjun Wang, Oktie Hassanzadeh, +4 authors Chen Wang
Computer Science
Proc. VLDB Endow.
1 May 2015

We present a schema management framework for document stores that discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. Expand

Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings

V. Efthymiou, Oktie Hassanzadeh, Mariano Rodriguez-Muro, V. Christophides
Computer Science
International Semantic Web Conference
21 October 2017

In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information. Expand

Linking Open Drug Data

A. Jentzsch, J. Zhao, Oktie Hassanzadeh, Kei-Hoi Cheung, Matthias Samwald, B. Andersson
Computer Science
I-SEMANTICS
2009

The development of new therapies for diseases requires the integration of large amounts of biomedical data from many different sources. Expand

Instance-Based Matching of Large Ontologies Using Locality-Sensitive Hashing

S. Duan, Achille Fokoue, Oktie Hassanzadeh, Anastasios Kementsietsidis, Kavitha Srinivas, M. J. Ward
Computer Science
International Semantic Web Conference
11 November 2012

In this paper, we describe a mechanism for ontology alignment using instance based matching of types (or classes). Expand

Creating probabilistic databases from duplicated data

Oktie Hassanzadeh, R. Miller
Computer Science
The VLDB Journal
1 October 2009

We present a flexible modular framework for scalably creating a probabilistic database out of a dirty relation of duplicated data and overview the challenges raised in utilizing this framework for large relations of string data. Expand

Discovering Linkage Points over Web Data

Oktie Hassanzadeh, Ken Q. Pu, +4 authors C. Ho
Computer Science
Proc. VLDB Endow.
1 April 2013

We present a framework consisting of a library of efficient lexical analyzers and similarity functions, and a set of search algorithms for effective and efficient identification of linkage points over Web data. Expand

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