Nowadays, the demand on querying and searching the Semantic Web is increasing. Some systems have ... more Nowadays, the demand on querying and searching the Semantic Web is increasing. Some systems have adopted IR (Information Retrieval) approaches to index and search the Semantic Web data due to its capability to handle the web-scale data and efficiency on query answering. Additionally, the huge volumes of data on the Semantic Web are frequently updated. Thus, it further requires effective update mechanisms for these systems to handle the data change. However, the existing update approaches only focus on document. It still remains a big challenge to update IR index specially designed for semantic data in the form of triples, which are finer grained structured objects rather than unstructured documents. In this paper, we present a well-designed update mechanism on the IR index for triples. Our approach provides flexible and effective update mechanism by dividing the index into blocks. It reduces the number of update operations during the insertion of triples. At the same time, it preserves the efficiency on query processing and the capability to handle large scale semantic data. Experimental results show that the index update time is a fraction of that by complete reconstruction w.r.t the portion of the inserted triples. Moreover, the query response time is not notably affected. Thus, it is capable to make newly arrived semantic data immediately searchable for users.
Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to ha... more Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintain- ing such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel dif- ficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an inte- grated solution to make Wikipedia authoring easier based on RDF graph match- ing, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and au- to-completes internal links between Wikipedia articles for the user; 2) a catego- ry suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
In recent years, more and more images have been uploaded and published on the Web. Along with tex... more In recent years, more and more images have been uploaded and published on the Web. Along with text Web pages, images have been becoming important media to place relevant advertisements. Visual contextual advertising, a young research area, refers to finding relevant text advertisements for a target image without any textual information (e.g., tags). There are two existing approaches, advertisement search based on image annotation, and more recently, advertisement matching based on feature translation between images and texts. However, the state of the art fails to achieve satisfactory results due to the fact that recommended advertisements are syntactically matched but semantically mismatched. In this paper, we propose a semantic approach to improving the performance of visual contextual advertising. More specifically, we exploit a large high-quality image knowledge base (ImageNet) and a widely-used text knowledge base (Wikipedia) to build a bridge between target images and advertisements. The image-advertisement match is built by mapping images and advertisements into the respective knowledge bases and then finding semantic matches between the two knowledge bases. The experimental results show that semantic match outperforms syntactic match significantly using test images from Flickr. We also show that our approach gives a large improvement of 16.4% on the precision of the top 10 matches over previous work, with more semantically relevant advertisements recommended.
ABSTRACT Measuring semantic relatedness plays an important role in information retrieval and Natu... more ABSTRACT Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent entities, we propose a more comprehensive approach by leveraging Linked Open Data (LOD) to solve these problems. LOD consists of lots of data sources from different domains and provides rich a priori knowledge about the entities in the world. By exploiting the semantic associations in LOD, we propose a novel algorithm, called LODDO, to measure the semantic relatedness between named entities. The experimental results show the high performance and robustness of our approach.
Abstract. As an extension to the current Web, Semantic Web will not only contain structured data ... more Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches ...
... Another representative keyword based semantic search application is OntoLook [5]: a prototype... more ... Another representative keyword based semantic search application is OntoLook [5]: a prototyperelation-based search engine. OntoLook mentioned the weakness of keyword query in the context of semantic web and inferred possible relations among keywords to improve the ...
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11, 2011
In recent years, online shopping is becoming more and more popular. Users type keyword queries on... more In recent years, online shopping is becoming more and more popular. Users type keyword queries on product search systems to find relevant products, accessories, and even related products. However, existing product search systems always return very similar products on the first several pages instead of taking diversity into consideration. In this paper, we propose a novel approach to address the
The increasing amount of data on the Semantic Web offers opportunities for semantic search. Howev... more The increasing amount of data on the Semantic Web offers opportunities for semantic search. However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query's syntax or the underlying ontology. Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries. However, these approaches yet feature only very basic query ranking and do not scale to large repositories. We tackle the scalability problem by proposing a novel clusteredgraph structure that corresponds to only a summary of the original ontology. The so reduced data space is then used in the exploration for the computation of top-k queries. Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements w.r.t. the query and the importance of ontology elements. The experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes.
Adaptive hypermedia systems can alleviate information overload on the Web by personalising the de... more Adaptive hypermedia systems can alleviate information overload on the Web by personalising the delivery of resources to the user. These systems are however afflicted with difficulties in the acquisition of user data as well as the general lack of user control on and transparency of the systems' adaptive behavior. In this paper, we argue that the use of rules on top of ontologies can enable adaptive functionality that is both transparent and controllable for users. To this end, we sketch ODAS, a domain ontology for adaptive hypermedia systems, and a model for the specification of adaptation rules.
Web Semantics: Science, Services and Agents on the World Wide Web, 2009
The Web as a global information space is developing from a Web of documents to a Web of data. Thi... more The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we present Hermes, an infrastructure for data Web search that addresses a number of challenges involved in realizing search on the data Web.
Web Semantics: Science, Services and Agents on the World Wide Web, 2011
The Web contains a large amount of documents and an increasing quantity of structured data in the... more The Web contains a large amount of documents and an increasing quantity of structured data in the form of RDF triples. Many of these triples are annotations associated with documents. While structured queries constitute the principal means to retrieve structured data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both textual and structured data can address more complex information needs. However, hybrid search on the large scale Web environment faces several challenges. First, there is a need for repositories that can store and index a large amount of semantic data as well as textual data in documents, and manage them in an integrated way. Second, methods for hybrid query answering are needed to exploit the data from such an integrated repository. These methods should be fast and scalable, and in particular, they shall support flexible ranking schemes to return not all but only the most relevant results. In this paper, we present CE 2 , an integrated solution that leverages mature information retrieval and database technologies to support large scale hybrid search. For scalable and integrated management of data, CE 2 integrates off-the-shelf database solutions with inverted indexes. Efficient hybrid query processing is supported through novel data structures and algorithms which allow advanced ranking schemes to be tightly integrated. Furthermore, a concrete ranking scheme is proposed to take features from both textual and structured data into account. Experiments conducted on DBpedia and Wikipedia show that CE 2 can provide good performance in terms of both effectiveness and efficiency.
Efficient support for querying large-scale resource description framework (RDF) triples plays an ... more Efficient support for querying large-scale resource description framework (RDF) triples plays an important role in semantic web data management. This paper presents an efficient RDF query engine to evaluate SPARQL queries, where the inverted index structure is employed for indexing the RDF triples. A set of operators on the inverted index was developed for query optimization and evaluation. Then a
The Web as a global information space is developing from a Web of documents to a Web of data. Thi... more The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer lim- ited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with pre- cise answers as results. In this paper, we present SearchWebDB, an infrastructure for data web search that addresses a number of challenges involved in realizing search on the data web: To pro- vide an end-user oriented interface, we support expressive keyword search by translating user information needs into structured queries. We integrate heterogeneous web data sources with automatically computed mappings. Schema-level mappings are exploited in con- structing structured queries against the integrated schema. These structured queries are decomposed into queries against the local web data sources, which are then processed in a distributed way. Finally, heterogeneous result sets are combined using an algorithm called map join, making use of data-level mappings. In evaluation experiments with real life data sets from the data web we show the practicability and scalability of the SearchWebDB infrastructure.
The Web contains a large amount of documents and increasingly, also semantic data in the form of ... more The Web contains a large amount of documents and increasingly, also semantic data in the form of RDF triples. Many of these triples are annotations that are associated with documents. While structured query is the principal mean to retrieve semantic data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both documents and semantic data can address more complex information needs. In this paper, we present CE 2 , an integrated solution that leverages mature database and information retrieval technologies to tackle challenges in hybrid search on the large scale. For scalable storage, CE 2 integrates database with inverted indices. Hybrid query processing is supported in CE 2 through novel algorithms and data structures, which allow for advanced ranking schemes to be integrated more tightly into the process. Experiments conducted on Dbpedia and Wikipedia show that CE 2 can provide good performance in terms of both effectiveness and efficiency.
Revising ontologies in Description Logics (DLs) deals with the problem of incorporating a new ont... more Revising ontologies in Description Logics (DLs) deals with the problem of incorporating a new ontology into an old one consistently. This problem is important as DLs underpin W3C standard Web ontology language OWL and ontologies may evolve during their construction. Existing revision operators in DLs are mostly generalizations of belief revision operators in propositional logic. Recently, there has been some work on revising ontologies in DL-Lite family, which is a family of DLs that provides tractable reasoning services. In (Qi and Du 2009; ...
Keyword queries enjoy widespread usage as they represent an intuitive way of specifying informati... more Keyword queries enjoy widespread usage as they represent an intuitive way of specifying information needs. Recently, answering keyword queries on graph-structured data has emerged as an important research topic. The prevalent approaches build on dedicated indexing techniques as well as search algorithms aiming at finding substructures that connect the data elements matching the keywords. In this paper, we introduce a novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model. Instead of computing answers directly as in previous approaches, we first compute queries from the keywords, allowing the user to choose the appropriate query, and finally, process the query using the underlying database engine. Thereby, the full range of database optimization techniques can be leveraged for query processing. For the computation of queries, we propose a novel algorithm for the exploration of top-k matching subgraphs. While related techniques search the best answer trees, our algorithm is guaranteed to compute all k subgraphs with lowest costs, including cyclic graphs. By performing exploration only on a summary data structure derived from the data graph, we achieve promising performance improvements compared to other approaches.
Nowadays, the demand on querying and searching the Semantic Web is increasing. Some systems have ... more Nowadays, the demand on querying and searching the Semantic Web is increasing. Some systems have adopted IR (Information Retrieval) approaches to index and search the Semantic Web data due to its capability to handle the web-scale data and efficiency on query answering. Additionally, the huge volumes of data on the Semantic Web are frequently updated. Thus, it further requires effective update mechanisms for these systems to handle the data change. However, the existing update approaches only focus on document. It still remains a big challenge to update IR index specially designed for semantic data in the form of triples, which are finer grained structured objects rather than unstructured documents. In this paper, we present a well-designed update mechanism on the IR index for triples. Our approach provides flexible and effective update mechanism by dividing the index into blocks. It reduces the number of update operations during the insertion of triples. At the same time, it preserves the efficiency on query processing and the capability to handle large scale semantic data. Experimental results show that the index update time is a fraction of that by complete reconstruction w.r.t the portion of the inserted triples. Moreover, the query response time is not notably affected. Thus, it is capable to make newly arrived semantic data immediately searchable for users.
Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to ha... more Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintain- ing such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel dif- ficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an inte- grated solution to make Wikipedia authoring easier based on RDF graph match- ing, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and au- to-completes internal links between Wikipedia articles for the user; 2) a catego- ry suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.
Proceedings of the sixth ACM conference on Recommender systems - RecSys '12, 2012
In recent years, more and more images have been uploaded and published on the Web. Along with tex... more In recent years, more and more images have been uploaded and published on the Web. Along with text Web pages, images have been becoming important media to place relevant advertisements. Visual contextual advertising, a young research area, refers to finding relevant text advertisements for a target image without any textual information (e.g., tags). There are two existing approaches, advertisement search based on image annotation, and more recently, advertisement matching based on feature translation between images and texts. However, the state of the art fails to achieve satisfactory results due to the fact that recommended advertisements are syntactically matched but semantically mismatched. In this paper, we propose a semantic approach to improving the performance of visual contextual advertising. More specifically, we exploit a large high-quality image knowledge base (ImageNet) and a widely-used text knowledge base (Wikipedia) to build a bridge between target images and advertisements. The image-advertisement match is built by mapping images and advertisements into the respective knowledge bases and then finding semantic matches between the two knowledge bases. The experimental results show that semantic match outperforms syntactic match significantly using test images from Flickr. We also show that our approach gives a large improvement of 16.4% on the precision of the top 10 matches over previous work, with more semantically relevant advertisements recommended.
ABSTRACT Measuring semantic relatedness plays an important role in information retrieval and Natu... more ABSTRACT Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent entities, we propose a more comprehensive approach by leveraging Linked Open Data (LOD) to solve these problems. LOD consists of lots of data sources from different domains and provides rich a priori knowledge about the entities in the world. By exploiting the semantic associations in LOD, we propose a novel algorithm, called LODDO, to measure the semantic relatedness between named entities. The experimental results show the high performance and robustness of our approach.
Abstract. As an extension to the current Web, Semantic Web will not only contain structured data ... more Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches ...
... Another representative keyword based semantic search application is OntoLook [5]: a prototype... more ... Another representative keyword based semantic search application is OntoLook [5]: a prototyperelation-based search engine. OntoLook mentioned the weakness of keyword query in the context of semantic web and inferred possible relations among keywords to improve the ...
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11, 2011
In recent years, online shopping is becoming more and more popular. Users type keyword queries on... more In recent years, online shopping is becoming more and more popular. Users type keyword queries on product search systems to find relevant products, accessories, and even related products. However, existing product search systems always return very similar products on the first several pages instead of taking diversity into consideration. In this paper, we propose a novel approach to address the
The increasing amount of data on the Semantic Web offers opportunities for semantic search. Howev... more The increasing amount of data on the Semantic Web offers opportunities for semantic search. However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query's syntax or the underlying ontology. Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries. However, these approaches yet feature only very basic query ranking and do not scale to large repositories. We tackle the scalability problem by proposing a novel clusteredgraph structure that corresponds to only a summary of the original ontology. The so reduced data space is then used in the exploration for the computation of top-k queries. Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements w.r.t. the query and the importance of ontology elements. The experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes.
Adaptive hypermedia systems can alleviate information overload on the Web by personalising the de... more Adaptive hypermedia systems can alleviate information overload on the Web by personalising the delivery of resources to the user. These systems are however afflicted with difficulties in the acquisition of user data as well as the general lack of user control on and transparency of the systems' adaptive behavior. In this paper, we argue that the use of rules on top of ontologies can enable adaptive functionality that is both transparent and controllable for users. To this end, we sketch ODAS, a domain ontology for adaptive hypermedia systems, and a model for the specification of adaptation rules.
Web Semantics: Science, Services and Agents on the World Wide Web, 2009
The Web as a global information space is developing from a Web of documents to a Web of data. Thi... more The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we present Hermes, an infrastructure for data Web search that addresses a number of challenges involved in realizing search on the data Web.
Web Semantics: Science, Services and Agents on the World Wide Web, 2011
The Web contains a large amount of documents and an increasing quantity of structured data in the... more The Web contains a large amount of documents and an increasing quantity of structured data in the form of RDF triples. Many of these triples are annotations associated with documents. While structured queries constitute the principal means to retrieve structured data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both textual and structured data can address more complex information needs. However, hybrid search on the large scale Web environment faces several challenges. First, there is a need for repositories that can store and index a large amount of semantic data as well as textual data in documents, and manage them in an integrated way. Second, methods for hybrid query answering are needed to exploit the data from such an integrated repository. These methods should be fast and scalable, and in particular, they shall support flexible ranking schemes to return not all but only the most relevant results. In this paper, we present CE 2 , an integrated solution that leverages mature information retrieval and database technologies to support large scale hybrid search. For scalable and integrated management of data, CE 2 integrates off-the-shelf database solutions with inverted indexes. Efficient hybrid query processing is supported through novel data structures and algorithms which allow advanced ranking schemes to be tightly integrated. Furthermore, a concrete ranking scheme is proposed to take features from both textual and structured data into account. Experiments conducted on DBpedia and Wikipedia show that CE 2 can provide good performance in terms of both effectiveness and efficiency.
Efficient support for querying large-scale resource description framework (RDF) triples plays an ... more Efficient support for querying large-scale resource description framework (RDF) triples plays an important role in semantic web data management. This paper presents an efficient RDF query engine to evaluate SPARQL queries, where the inverted index structure is employed for indexing the RDF triples. A set of operators on the inverted index was developed for query optimization and evaluation. Then a
The Web as a global information space is developing from a Web of documents to a Web of data. Thi... more The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer lim- ited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with pre- cise answers as results. In this paper, we present SearchWebDB, an infrastructure for data web search that addresses a number of challenges involved in realizing search on the data web: To pro- vide an end-user oriented interface, we support expressive keyword search by translating user information needs into structured queries. We integrate heterogeneous web data sources with automatically computed mappings. Schema-level mappings are exploited in con- structing structured queries against the integrated schema. These structured queries are decomposed into queries against the local web data sources, which are then processed in a distributed way. Finally, heterogeneous result sets are combined using an algorithm called map join, making use of data-level mappings. In evaluation experiments with real life data sets from the data web we show the practicability and scalability of the SearchWebDB infrastructure.
The Web contains a large amount of documents and increasingly, also semantic data in the form of ... more The Web contains a large amount of documents and increasingly, also semantic data in the form of RDF triples. Many of these triples are annotations that are associated with documents. While structured query is the principal mean to retrieve semantic data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both documents and semantic data can address more complex information needs. In this paper, we present CE 2 , an integrated solution that leverages mature database and information retrieval technologies to tackle challenges in hybrid search on the large scale. For scalable storage, CE 2 integrates database with inverted indices. Hybrid query processing is supported in CE 2 through novel algorithms and data structures, which allow for advanced ranking schemes to be integrated more tightly into the process. Experiments conducted on Dbpedia and Wikipedia show that CE 2 can provide good performance in terms of both effectiveness and efficiency.
Revising ontologies in Description Logics (DLs) deals with the problem of incorporating a new ont... more Revising ontologies in Description Logics (DLs) deals with the problem of incorporating a new ontology into an old one consistently. This problem is important as DLs underpin W3C standard Web ontology language OWL and ontologies may evolve during their construction. Existing revision operators in DLs are mostly generalizations of belief revision operators in propositional logic. Recently, there has been some work on revising ontologies in DL-Lite family, which is a family of DLs that provides tractable reasoning services. In (Qi and Du 2009; ...
Keyword queries enjoy widespread usage as they represent an intuitive way of specifying informati... more Keyword queries enjoy widespread usage as they represent an intuitive way of specifying information needs. Recently, answering keyword queries on graph-structured data has emerged as an important research topic. The prevalent approaches build on dedicated indexing techniques as well as search algorithms aiming at finding substructures that connect the data elements matching the keywords. In this paper, we introduce a novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model. Instead of computing answers directly as in previous approaches, we first compute queries from the keywords, allowing the user to choose the appropriate query, and finally, process the query using the underlying database engine. Thereby, the full range of database optimization techniques can be leveraged for query processing. For the computation of queries, we propose a novel algorithm for the exploration of top-k matching subgraphs. While related techniques search the best answer trees, our algorithm is guaranteed to compute all k subgraphs with lowest costs, including cyclic graphs. By performing exploration only on a summary data structure derived from the data graph, we achieve promising performance improvements compared to other approaches.
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Papers by Haofen Wang