Matches in ScholarlyData for { ?s ?p ?o. }
- 211 type InProceedings.
- 211 label "NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data".
- 211 sameAs 211.
- 211 abstract "A variety of query approaches have been proposed by the semantic web community to explore and query semantic data. Each was developed for a specific task and employed its own interaction mechanism; each query mechanism has its own set of advantages and drawbacks. Most semantic web search systems employ only one approach, thus being unable to exploit the benefits of alternative approaches. Motivated by a usability and interactivity perspective, we propose to combine two query approaches (graph-based and natural language) as a hybrid query approach. In this paper, we present NL-Graphs which aims to exploit the strengths of both approaches, while ameliorating their weaknesses. NL-Graphs was conceptualised and developed from observations, and lessons learned, in several evaluations with expert and casual users. The results of evaluating our approach with expert and casual users on a large semantic dataset are very encouraging; both types of users were highly satisfied and could effortlessly use the hybrid approach to formulate and answer queries. Indeed, success rates showed they were able to successfully answer all the evaluation questions.".
- 211 hasAuthorList authorList.
- 211 isPartOf proceedings.
- 211 keyword "Hybrid Query".
- 211 keyword "Information Visualisation".
- 211 keyword "Natural Language Processing".
- 211 keyword "Semantic Search".
- 211 keyword "User Interface".
- 211 title "NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data".
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- item-1 hasContent khadija-elbedweihy.
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- item-2 hasContent suvodeep-mazumdar.
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- item-3 hasContent stuart-wrigley.
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- item-4 hasContent fabio-ciravegna.
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- 227 creator amit-sheth.
- 227 creator chitra-venkataramani.
- 227 creator pavan-kapanipathi.
- 227 creator prateek-jain.
- 227 type InProceedings.
- 227 label "User Interests Identification on Twitter Using a Hierarchical Knowledge Base".
- 227 sameAs 227.
- 227 abstract "Twitter, due to its massive growth as a social networking platform has been in focus to analyze its user generated content for personalization and recommendation tasks. A common challenge across these tasks is identifying user interests from tweets. Lately, semantic enrichment of Twitter posts to determine (entity-based) user interests has been an active area of research. The advantages of these approaches include interoperability, information reuse and the availability of knowledge-bases to be exploited. However, exploiting these knowledge bases for identifying user interests still remains a challenge. In this work, we focus on exploiting hierarchical relationships present in knowledge-bases to infer richer user interests expressed as \textit{Hierarchical Interest Graph}. We argue that the hierarchical semantics of concepts can enhance the existing systems to personalize or recommend items based on varied level of conceptual abstractness. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted hierarchical interests in the graph being relevant to a given user's interests.".
- 227 hasAuthorList authorList.
- 227 isPartOf proceedings.
- 227 keyword "Hierarchical Interest Graph".
- 227 keyword "Personalization".
- 227 keyword "Semantic Web".
- 227 keyword "Twitter".
- 227 keyword "User Profile".
- 227 keyword "Wikipedia".
- 227 title "User Interests Identification on Twitter Using a Hierarchical Knowledge Base".
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- item-1 hasContent pavan-kapanipathi.
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- item-2 hasContent prateek-jain.
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- item-3 hasContent chitra-venkataramani.
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- item-4 hasContent amit-sheth.
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- 26 creator danyun-xu.
- 26 creator gong-cheng.
- 26 creator yuzhong-qu.
- 26 type InProceedings.
- 26 label "Facilitating Human Intervention in Coreference Resolution with Comparative Entity Summaries".
- 26 sameAs 26.
- 26 abstract "A primary challenge to Web data integration is coreference resolution, namely identifying entity descriptions from different data sources that refer to the same real-world entity. Increasingly, solutions to coreference resolution have humans in the loop. For instance, many active learning, crowdsourcing, and pay-as-you-go approaches solicit user feedback for verifying candidate coreferent entities computed by automatic methods. Whereas reducing the number of verification tasks is a major consideration for these approaches, very little attention has been paid to the efficiency of performing each single verification task. To address this issue, in this paper, instead of showing the entire descriptions of two entities for verification which are possibly lengthy, we propose to extract and present a compact summary of them, and expect that such length-limited comparative entity summaries can help human users verify more efficiently without significantly hurting the accuracy of their verification. Our approach exploits the common and different features of two entities that best help indicate (non-)coreference, and also considers the diverse information on their identities. Experimental results show that verification is 2.7--2.9 times faster when using our comparative entity summaries, and its accuracy is not notably affected.".
- 26 hasAuthorList authorList.
- 26 isPartOf proceedings.
- 26 keyword "comparative entity summary".
- 26 keyword "coreference resolution".
- 26 keyword "entity consolidation".
- 26 keyword "entity summarization".
- 26 title "Facilitating Human Intervention in Coreference Resolution with Comparative Entity Summaries".
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- item-1 hasContent danyun-xu.
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- item-2 hasContent gong-cheng.
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