Zareen Syed

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Zareen Syed is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Creating and Exploiting a Hybrid Knowledge Base for Linked Data Information extraction
Knowledge base
Linked data
Semantic web
Wikipedia
Communications in Computer and Information Science English 2011 Twenty years ago Tim Berners-Lee proposed a distributed hypertext system based on standard Internet protocols. The Web that resulted fundamentally changed the ways we share information and services, both on the public Internet and within organizations. That original proposal contained the seeds of another effort that has not yet fully blossomed: a Semantic Web designed to enable computer programs to share and understand structured and semi-structured information easily. We will review the evolution of the idea and technologies to realize a Web of Data and describe how we are exploiting them to enhance information retrieval and information extraction. A key resource in our work is Wikitology, a hybrid knowledge base of structured and unstructured information extracted from Wikipedia. 0 0
Approaches for automatically enriching wikipedia AAAI Workshop - Technical Report English 2010 We have been exploring the use of Web-derived knowledge bases through the development of Wikitology - a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Open Data resources. In this paper, we describe approaches that aid in enriching Wikipedia and thus the resources that derive from Wikipedia such as the Wikitology knowledge base, DBpedia, Freebase and Powerset. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved. 0 0
Creating and exploiting a Web of semantic data Information extraction
Knowledge base
Semantic web
Wikipedia
ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings English 2010 Twenty years ago Tim Berners-Lee proposed a distributed hypertext system based on standard Internet protocols. The Web that resulted fundamentally changed the ways we share information and services, both on the public Internet and within organizations. That original proposal contained the seeds of another effort that has not yet fully blossomed: a Semantic Web designed to enable computer programs to share and understand structured and semi-structured information easily. We will review the evolution of the idea and technologies to realize a Web of Data and describe how we are exploiting them to enhance information retrieval and information extraction. A key resource in our work is Wikitology, a hybrid knowledge base of structured and unstructured information extracted from Wikipedia. 0 0
Unsupervised techniques for discovering ontology elements from Wikipedia article links FAM-LbR English 2010 0 0
Using linked data to interpret tables Entity linking
Human language technology
Information retrieval
Linked data
Semantic web
CEUR Workshop Proceedings English 2010 Vast amounts of information is available in structured forms like spreadsheets, database relations, and tables found in documents and on the Web. We describe an approach that uses linked data to interpret such tables and associate their components with nodes in a reference linked data collection. Our proposed framework assigns a class (i.e. type) to table columns, links table cells to entities, and inferred relations between columns to properties. The resulting interpretation can be used to annotate tables, confirm existing facts in the linked data collection, and propose new facts to be added. Our implemented prototype uses DBpedia as the linked data collection and Wikitology for background knowledge. We evaluated its performance using a collection of tables from Google Squared, Wikipedia and the Web. 0 0
Using wikitology for cross-document entity coreference resolution AAAI Spring Symposium - Technical Report English 2009 We describe the use of the Wikitology knowledge base as a resource for a variety of applications with special focus on a cross-document entity coreference resolution task. This task involves recognizing when entities and relations mentioned in different documents refer to the same object or relation in the world. Wikitology is a knowledge base system constructed with material from Wikipedia, DBpedia and Freebase that includes both unstructured text and semi-structured information. Wikitology was used to define features that were part of a system implemented by the Johns Hopkins University Human Language Technology Center of Excellence for the 2008 Automatic Content Extraction cross-document coreference resolution evaluation organized by National Institute of Standards and Technology. 0 0
Wikipedia as an Ontology for Describing Documents Ontology
Wikipedia
Information retrieval
Text classification
Proceedings of the Second International Conference on Weblogs and Social Media, AAAI, March 31, 2008 2008 Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to predict concepts common to a set of documents. We describe several algorithms to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments demonstrate the feasibility of extending the category system with new concepts identified as a union of pages from the page link graph. 0 0