Matches in ScholarlyData for { ?s ?p ?o. }
- 317 keyword "caching".
- 317 keyword "distribution".
- 317 keyword "index compression".
- 317 title "Scalable Techniques for Document Identifier Assignment in Inverted Indexes".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent shuai-ding.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent josh-attenberg.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent torsten-suel.
- item-3 previous item-2.
- 324 creator bernhard-haslhofer.
- 324 creator niko-popitsch.
- 324 type InProceedings.
- 324 label "DSNotify: Handling Broken Links in the Web of Data".
- 324 sameAs 324.
- 324 abstract "The Web of Data has emerged as a way of exposing structured linked data on the Web. It builds on the central building blocks of the Web (URIs, HTTP) and benefits from its simplicity and wide-spread adoption. It does, however, also inherit the unresolved issues such as the broken link problem. Broken links constitute a major issue for actors consuming Linked Data as they require them to deal with reduced accessibility of data. We believe that the broken link problem is a major threat to the whole Web of Data idea and that both Linked Data consumers and providers will require solutions that deal with this problem. Since no general solutions for fixing such links in the Web of Data have emerged, we make three contributions into this direction: first, we provide a concise definition of the broken link problem and a comprehensive analysis of existing approaches. Second, we present DSNotify, a generic framework able to assist human and machine actors in fixing broken links. It uses heuristic feature comparison and employs a time-interval-based blocking technique for the underlying instance matching problem. Third, we derived benchmark datasets from knowledge bases such as DBpedia and evaluate the effectiveness of our approach with respect to the broken link problem. Our results show the feasibility of a time-interval-based blocking approach for systems that aim at detecting and fixing broken links in the Web of Data.".
- 324 hasAuthorList authorList.
- 324 isPartOf proceedings.
- 324 keyword "Blogs".
- 324 keyword "browsers".
- 324 keyword "consume the Semantic/Data Web".
- 324 keyword "crawlers".
- 324 keyword "harvesters".
- 324 keyword "other applications that produce".
- 324 keyword "search engines".
- 324 keyword "wikis".
- 324 title "DSNotify: Handling Broken Links in the Web of Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent niko-popitsch.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent bernhard-haslhofer.
- item-2 previous item-1.
- 325 creator danushka-bollegala.
- 325 creator mitsuru-ishizuka.
- 325 creator nguyen-duc.
- 325 creator yutaka-matsuo.
- 325 type InProceedings.
- 325 label "Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web".
- 325 sameAs 325.
- 325 abstract "Extracting semantic relations between entities is an important first step in various tasks in Web mining and natural language processing such as, information extraction, relation detection, and social network mining. A relation can be expressed extensionally by stating all the instances of that relation, or intensionally by defining all the paraphrases of that relation. For example, consider the ACQUISITION relation between two companies. An extensional definition of ACQUISITION contains all pairs of companies where one company is acquired by the other (e.g. (YouTube, Google) or (Powerset, Microsoft)). On the other hand we can intensionally define ACQUISITION as the relation described by lexical patterns such as, X is acquired by Y, or Y purchased X, where X and Y denote two companies. We utilize this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can efficiently extract a large number of relations from unlabeled data. We provide an efficient heuristic to find the parameters of the proposed co-clustering algorithm. Using the clusters produced by the algorithm, we train an L1 regularized logistic regression model to identify the representative patterns that describe the relation expressed by each cluster. We evaluate the proposed method in three different tasks: measuring relational similarity between entity pairs, open information extraction (Open IE), and classifying relations in a social network system. Experiments conducted using a benchmark dataset show that the proposed method improves existing relational similarity measures. Moreover, the proposed method significantly outperforms the current state-of-the-art Open IE systems in both precision and recall. The proposed method correctly classifies 53 relation types in an online social network containing 470,671 nodes and 35,652,475 edges, thereby demonstrating its efficacy in real-world relation detection tasks.".
- 325 hasAuthorList authorList.
- 325 isPartOf proceedings.
- 325 keyword "Relational data on the Web".
- 325 title "Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent danushka-bollegala.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent yutaka-matsuo.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent mitsuru-ishizuka.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent nguyen-duc.
- item-4 previous item-3.
- 327 creator bin-cui.
- 327 creator christian-jensen.
- 327 creator gao-cong.
- 327 creator xin-cao.
- 327 type InProceedings.
- 327 label "A General Framework for Exploring Category Information for Question Retrieval in Community Question Answer Archives".
- 327 sameAs 327.
- 327 abstract "Community Question Answering (CQA) has emerged as a popular type of service where users ask and answer questions and access historical question-answer pairs. CQA archives contain very large volumes of questions organized into a hierarchy of categories. As an essential function of CQA services, question search in a CQA archive aims to retrieve relevant historical questions and answers to a query question. In this paper, we propose a new approach to exploiting category information of questions to improve the performance of question search, and apply the approach to existing question search models, including one of the state of the art question search models. Experiments conducted on real CQA data demonstrate that the proposed techniques are capable of outperforming baseline methods significantly.".
- 327 hasAuthorList authorList.
- 327 isPartOf proceedings.
- 327 keyword "Query processing".
- 327 keyword "diversity".
- 327 keyword "ranking".
- 327 keyword "relevance".
- 327 keyword "retrieval".
- 327 title "A General Framework for Exploring Category Information for Question Retrieval in Community Question Answer Archives".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.