Browsing by Subject "entity linking"
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Item Automatically Generating Government Linked Data from Tables(AAAI, 2011-11-04) Mulwad, Varish; Finin, Tim; Joshi, AnupamMost open government data is encoded and published in structured tables found in reports, on the Web, and in spreadsheets or databases. Current approaches to generating Semantic Web representations from such data requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table using background knowledge from the Linked Open Data cloud. We represent a table’s meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.Item A Context-Aware Approach to Entity Linking(Association for Computational Linguistics, 2012-06-07) Stoyanov, veselin; Mayfield, James; Xu, Tan; Oard, Doug; Lawrie, Dawn; Oates, Tim; Finin, TimEntity linking refers to the task of assigning mentions in documents to their corresponding knowledge base entities. Entity linking is a central step in knowledge base population. Current entity linking systems do not explicitly model the discourse context in which the communication occurs. Nevertheless, the notion of shared context is central to the linguistic theory of pragmatics and plays a crucial role in Grice’s cooperative communication principle. Furthermore, modeling context facilitates joint resolution of entities, an important problem in entity linking yet to be addressed satisfactorily. This paper describes an approach to context-aware entity linking.Item Evaluating the Quality of a Knowledge Base Populated from Text(ACM, 2012-06-07) Mayfield, James; Finin, TimThe steady progress of information extraction systems has been helped by sound methodologies for evaluating their performance in controlled experiments. Annual events like MUC, ACE and TAC have developed evaluation approaches enabling researchers to score and rank their systems relative to reference results. Yet these evaluations have only assessed component technologies needed by a knowledge base population system; none has required the construction of a knowledge base that is then evaluated directly. We describe an approach to the direct evaluation of a knowledge base and an instantiation that will be used in a 2012 TAC Knowledge Base Population track.Item Extracting Information about Security Vulnerabilities from Web Text(IEEE, 2011-08-22) Mulwad, Varish; Li, Wenjia; Joshi, Anupam; Finin, Tim; Viswanathan, KrishnamurthyThe Web is an important source of information about computer security threats, vulnerabilities and cyber-attacks. We present initial work on developing a framework to detect and extract information about vulnerabilities and attacks from Web text. Our prototype system uses Wikitology, a general purpose knowledge base derived from Wikipedia, to extract concepts that describe specific vulnerabilities and attacks, map them to related concepts from DBpedia and generate machine understandable assertions. Such a framework will be useful in adding structure to already existing vulnerability descriptions as well as detecting new ones. We evaluate our approach against vulnerability descriptions from the National Vulnerability Database. Our results suggest that it can be useful in monitoring streams of text from social media or chat rooms to identify potential new attacks and vulnerabilities or to collect data on the spread and volume of existing ones.Item Generating Linked Data by Inferring the Semantics of Tables(2011-09-03) Mulwad, Varish; Finin, Tim; Joshi, AnupamVast amounts of information is encoded in structured tables found in documents, on the Web, and in spreadsheets or databases. Integrating or searching over this information benefits from understanding its intended meaning. Evidence for a table's meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. We represent a table's meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table. Using background knowledge from the Linked Open Data cloud, we jointly infer the semantics of column headers, table cell values (e.g.,strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples. We motivate the value of this approach using tables from the medical domain, discussing some of the challenges presented by these tables and describing techniques to tackle them.Item T2LD: Interpreting and Representing Tables as Linked Data(2010-11-07) Mulwad, Varish; Finin, Tim; Syed, Zareen; Joshi, AnupamWe describe a framework and prototype system for interpreting tables and extracting entities and relations from them, and producing a linked data representation of the table’s contents. This can be used to annotate the table or to add new facts to the linked data collection.Item Using linked data to interpret tables(2010-11-08) Mulwad, Varish; Finin, Tim; Syed, Zareen; Joshi, AnupamVast 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, con firm 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.