Ontology matching is one of the key research topics in Semantic Web. In the last few years, many matching methods have been proposed to generate matches between different ontologies either automatically or semi-automatically. To select appropriate ones, users need...
The Semantic Web is evolving very quickly. There are already many theories and tools to model various kinds of semantics using ontologies. However after organizations completed modeling the ontology structure, the ontologies must also be filled with instances and...
Recently, search engine companies like Google and Baidu are building their own knowledge graphs to empower the next generation of Web search. Due to the success of knowledge graphs in search, customers from vertical sectors are eager to embrace KG related technologies to develop domain specific semantic platforms or applications. However, they lack skills or tools to achieve the goal. In this paper, we present an integrated tool VKGBuilder to help users manage the life cycle of knowledge graphs. We will describe three modules of VKGBuilder in detail which construct, store, search and explore knowledge graphs in vertical domains. In addition, we will demonstrate the capability and usability of VKGBuilder via a real-world use case in the library industry.
Compared with plain-text resources, the ones in “semi-semantic” web sites, such as Wikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a “collaborative...
Adopting keyword query interface to semantic search on RDF data can help users keep away from learning the SPARQL query syntax and understanding the complex and fast evolving data schema. The existing approaches are divided into two categories: instance-based approaches and schema-based approaches. The instance-based approaches relying on the original RDF graph can generate precise answers but take a long processing time. In contrast, the schema-based approaches relying on the reduced summary graph require much less processing time but cannot always generate correct answers. In this paper, we propose a novel approach based on a hybrid graph which can achieve significant improvements on processing time with a limited accuracy drop compared with instance-based approaches, and meanwhile, can achieve promising accuracy gains at an affordable time cost compared with schema-based approaches.
Chinese Knowledge Base Question Answering (CKBQA) aims to predict answers for Chinese natural language questions by reasoning over facts in the knowledge base. In recent years, more attention has been paid to complex problems in KBQA, including multi-hop questions,...
<sec> <title>BACKGROUND</title> With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data. </sec> <sec> <title>OBJECTIVE</title> To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character. </sec> <sec> <title>METHODS</title> In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain–specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19. </sec> <sec> <title>RESULTS</title> OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described. </sec> <sec> <title>CONCLUSIONS</title> A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge. </sec>
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.
Linking Open Data (LOD) is the largest community effort for semantic data publishing which converts the Web from a Web of document to a Web of interlinked knowledge. While the state of the art LOD contains billion of triples describing millions of entities, it has...
Entity linking, which aims to find entities in given text, plays an important role in the trend of shifting from Web of documents to Web of knowledge. In this paper, we present ZhishiLink, an entity linking system targeting the largest Chinese linked open data -...
With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data.To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character.In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19.OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described.A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
