Searching Semantic Web Objects Based on Class Hierarchies
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This document summarizes a presentation on searching semantic web objects based on class hierarchies. The presentation discusses using class hierarchies to filter and restrict queries, recommends subclasses to refine searches, and uses an inverted index to match query terms to classes. It also covers heuristics for determining provenance of class typing information and an algorithm for recommending subclasses based on coverage of search results.
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More from Gong Cheng
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The document discusses content-based dataset search (CBDS) as an improvement over metadata-based dataset search (MBDS). It presents ACORDAR, a test collection for ad hoc CBDS using synthetic and TREC queries on RDF datasets. Evaluation results showed that both metadata and dataset content are useful, and that TREC queries are more difficult. CBDS faces challenges including scalability, tractability, and heterogeneity, but is likely to trend as it provides higher relevance and explainability than MBDS.
Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Gr...
This document presents an algorithm called CORE for generating compact yet relaxable answers to keyword queries over knowledge graphs. CORE aims to balance answer compactness, defined as having a bounded diameter, with answer completeness, defined as covering the most query keywords. It provides theoretical foundations for the existence of such answers and uses a best-first search approach. An evaluation shows CORE efficiently computes answers that are more complete than alternatives while remaining compact.
Semantic Data Retrieval: Search, Ranking, and Summarization
Gong Cheng presented on semantic data retrieval, including entity retrieval and association retrieval from semantic graphs. He discussed two main challenges: efficiently searching large graphs for associations within a diameter bound, and ranking the retrieved associations. For the first challenge, he proposed algorithms using path finding, pruning, and result deduplication. For the second challenge, he conducted a user study and found that association size was the most important ranking factor. Other proposed measures like entity homogeneity and relation heterogeneity had mixed user preferences.
Semantic Web related top conference review
The document summarizes key topics in semantic web and knowledge graph research from 2014-2017, including conferences, hot research areas, applications, and papers. It discusses trends such as increasing focus on knowledge graph applications, integration, and construction using techniques like neural networks. Notable news includes Google calling for dataset metadata and Wikidata creating its 31 millionth entity. The road ahead may involve greater knowledge graph commercialization, enrichment, and making knowledge graphs more accessible on the Web.
Relatedness-based Multi-Entity Summarization
The document proposes a new approach called relatedness-based multi-entity summarization (MES) to generate concise summaries of related entities. It formulates MES as a quadratic multidimensional knapsack problem (QMKP) to select important and diverse intra-entity features while also selecting inter-entity features that indicate relatedness. It presents an algorithm called REMES based on the grasp heuristic to solve the QMKP formulation. A user study shows REMES outperforms other entity summarization methods at multi-entity summarization tasks.
Generating Illustrative Snippets for Open Data on the Web
We propose generating illustrative snippets from datasets to serve with metadata on dataset search engines. Currently, only metadata is shown. Snippets would help users understand the contents faster by covering important types and entities, using familiar entities, and keeping entities related. We formulate the snippet generation as a maximum-weight-and-coverage connected graph problem to optimize for these qualities. Experimental results show our snippets outperform baselines.
Efficient Algorithms for Association Finding and Frequent Association Pattern...
The document presents efficient algorithms for association finding and frequent association pattern mining in large graph data. It describes the problems of finding all associations connecting a set of query entities within a diameter constraint and mining frequent association patterns. The basic solutions and optimizations for association finding using distance-based pruning and distance oracles are discussed. For frequent pattern mining, it addresses generating a canonical code to uniquely represent patterns and counting code occurrences to determine frequency. Experiments on real datasets demonstrate the efficiency and scalability of the approaches.
Summarizing Semantic Data
This document discusses summarizing semantic data, including entity descriptions, entity associations, and semantic datasets. It describes extractive and abstractive summarization methods. For entity descriptions, intrinsic metrics like frequency, centrality, informativeness, and diversity are used to rank property-value pairs for the summary. Extrinsic metrics also utilize external knowledge and context. Similar methods are applied to summarizing entity associations by ranking paths between entities. Summarizing semantic datasets involves selecting a representative subset of the data.
HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
This document summarizes the HIEDS approach to hierarchical dataset summarization. HIEDS aims to provide multigranular summaries that preserve dataset structure and are comprehensible. It models summarization as a multidimensional knapsack problem to maximize subgroup cohesion and moderateness while disallowing large overlap. HIEDS uses a greedy strategy for efficient solving but requires non-trivial implementation. Experiments show HIEDS outperforms the baseline by generating hierarchical rather than flat groups with better trade-offs and less redundancy.
Taking up the Gaokao Challenge: An Information Retrieval Approach
This document describes an information retrieval approach for answering questions from the Gaokao, China's national college entrance exam. It retrieves relevant concept pages, quotes, and disambiguates terms from Wikipedia. It ranks pages based on centrality within vector spaces of words, links, and categories, filtering within relevant historical categories. It assesses answer options based on the extent the question and pages can entail each option. In experiments, it correctly answered 43.09% of questions answerable from Wikipedia and 31.28% of questions outside Wikipedia's scope.
Summarizing Entity Descriptions for Effective and Efficient Human-centered En...
Presented at WWW'15, Florence.
Gong Cheng, Danyun Xu, Yuzhong Qu. Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking. In Proceedings of the 24th International World Wide Web Conference (WWW), pages 184--194, 2015.
Explass: Exploring Associations between Entities via Top-K Ontological Patter...
This document describes Explass, a system for exploring associations between entities via top-k ontological patterns and facets. It discusses challenges in exploring the over 1,000 associations within 4 hops in DBpedia and proposes two exploration methods: clustering associations into patterns and using entity/property classes as facets. The key steps involve mining significant patterns as frequent itemsets and selecting k patterns based on frequency, informativeness, and overlap. A demo of Explass on DBpedia is presented along with results of a user study comparing it to other approaches.
Facilitating Human Intervention in Coreference Resolution with Comparative En...
The document presents a method for facilitating human intervention in coreference resolution by providing comparative entity summaries. It describes using properties and values of candidate coreferent entities to generate summaries that reflect their commonality and differences. The optimal summary maximizes commonality, difference, identity information and diversity, subject to a length limit. An evaluation involved human subjects verifying coreferent relationships for different summarization approaches. The comparative summary approach was found to improve verification efficiency without affecting accuracy as much as only showing common properties or entire descriptions.
More from Gong Cheng (20)
Towards Content-Based Dataset Search - Test Collections and Beyond
Towards Content-Based Dataset Search - Test Collections and Beyond
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Generating Compact and Relaxable Answers to Keyword Queries over Knowledge Gr...
Semantic Data Retrieval: Search, Ranking, and Summarization
Semantic Data Retrieval: Search, Ranking, and Summarization
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Efficient Algorithms for Association Finding and Frequent Association Pattern...
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HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization
Taking up the Gaokao Challenge: An Information Retrieval Approach
Taking up the Gaokao Challenge: An Information Retrieval Approach
Summarizing Entity Descriptions for Effective and Efficient Human-centered En...
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Explass: Exploring Associations between Entities via Top-K Ontological Patter...
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Facilitating Human Intervention in Coreference Resolution with Comparative En...
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Searching Semantic Web Objects Based on Class Hierarchies
- 1. Searching Semantic Web Objects Based on Class Hierarchies Gong Cheng , Weiyi Ge, Honghan Wu, Yuzhong Qu Institute of Web Science, School of Computer Science and Engineering Southeast University, Nanjing, China Presentation at the WWW 2008 Workshop: Linked Data on the Web (LDOW2008) http://iws.seu.edu.cn/services/falcons/
- 2. Information Islands vs. Web of Data
- 3. Information Islands vs. Web of Data
- 4. Information Islands vs. Web of Data
- 7. Demo
- 8. Demo
- 9. Demo
- 10. Navigating Class Hierarchies for Query Restriction Filtering results Recommending subclasses
- 22. Architecture
- 23. RDF Sentence