Performing multi-hop queries on a large-scale Knowledge Graph (KG) presents significant computational challenges. Although subgraph matching-based methods show ability, they return empty/few answers when querying missing KG. An emerging solution involves embedding both entities and multi-hop queries of KG into a vector space, where the proximity between queries and entities reflects their semantic relevance. However, current embedding-based methods fail to adequately address three critical aspects: temporal semantics, spatial semantics, and fine-grained fuzzy semantics in multi relational queries. Thus, we propose a multi-hop query method on fuzzy spatiotemporal KG (STKG) by fine-grained embedding. We first develop an element-level embedding mechanism that maps fuzzy spatiotemporal semantics into each component. We then construct ordered multi-hop relations as composable operations on paths by Hermitian dot products. We finally use a Path-Constraint Resource Allocation (PCRA) to model path reliability. In addition, we validate our method on fuzzy spatiotemporal datasets. Results show that the method outperforms several state-of-the-art models and can effectively handle fuzzy spatiotemporal knowledge chain queries.

A large amount of work focus on static and temporal knowledge graph (KG) queries via subgraph reasoning, but it fails to consider the space-sensitivity and fuzziness of KG. Subgraph query in fuzzy spatiotemporal knowledge graph (f-STKG) is a fundamental and challenging task. Although a few efforts exist dedicated tof-STKG queries based on subgraph matching, they are sensitive to missing information in the knowledge base. To this end, we propose thef-STKG query method by subgraph reasoning using embeddings. The method aims to capture the semantic similarity between the query subgraph and candidate subgraph off-STKG to accomplish subgraph matching. We first embedf-STKGs into vector space to formf-STKG vectors. Then, we construct a global subgraph off-STKG using Composition-based Graph Convolutional Neural Networks (CompGCN), which allows global information adjacent to this subgraph to be integrated. Subsequently, the query subgraph off-STKG is constructed using mean pooling and Gated Recurrent Unit (GRU), as well as the candidate subgraph of thef-STKG is constructed using CompGCN and GRU. This facilitates the integration of structural and sequence pattern information of subgraphs. Finally, we measured the semantic similarity between this query subgraph and the candidate subgraph using the subgraph matching function. In addition, we designed experiments to validate the effectiveness of our method on two datasets. Results show that our method outperforms state-of-the-art baselines.

Knowledge graphs (KGs) use resource description framework (RDF) triples to model various crisp and static resources in the world. Meanwhile, knowledge embedded into vector space can imply more meanings. Much real-world information, however, is often uncertain and dynamic. Existing KG embedding (KGE) models are insufficient to deal with uncertain dynamic knowledge in vector spaces. To overcome this drawback, this article concentrates on an embedding module for the distributed representation of uncertain dynamic knowledge and proposes a strongly adaptive fuzzy spatiotemporal RDF embedding model (FSTRE). Specifically, we first propose a fine-grained fuzzy spatiotemporal RDF model, which provides the underlying representation framework for FSTRE. Then, within the complex vector space, spatial and temporal information is embedded by projection and rotation, respectively. Fine-grained fuzziness penetrates each element of the spatiotemporal five-tuples by a modal length of the anisotropic vectors. By using geometric operations as its transformation operator, FSTRE can capture the rich interaction between crisp and static knowledge and fuzzy spatiotemporal knowledge. We performed an experimental evaluation of FSTRE based on the built fuzzy spatiotemporal KG. It was shown that our FSTRE model is superior to state-of-the-art methods and can handle complex fuzzy spatiotemporal knowledge.

The proliferation of uncertain spatiotemporal data has led to an increasing demand for fuzzy spatiotemporal knowledge modeling in various applications. However, performing multi-hop query modeling on incomplete fuzzy spatiotemporal knowledge graphs (KGs) poses significant challenges. Recently, embedding-based multi-hop KG querying approaches have gained attention. Yet, these approaches often overlook KG uncertainty and spatiotemporal sensitivity, resulting in the neglect of fuzzy spatiotemporal information during multi-hop path reasoning. To address these challenges, we propose a embedding-based multi-hop query model for fuzzy spatiotemporal KG. We use quaternion to jointly embed spatiotemporal entities, and relations are represented as rotations from spatiotemporal subject to object. We incorporate uncertainty by the scoring function's bias factor, allowing for relaxation embedding. This approach facilitates the learning of a richer representation of fuzzy spatiotemporal KGs in vector space. By exploiting the inherent non-commutative compositional pattern of quaternions, we construct more accurate multi-hop paths within fuzzy spatiotemporal KGs, thus improving path reasoning performance. To evaluate the effectiveness of our model, we conduct experiments on two fuzzy spatiotemporal KG datasets, focusing on link prediction and path query answering. Results show that our proposed method significantly outperforms several state-of-the-art baselines in terms of performance metrics.

An event refers to an occurrence that occurs at a specific place, time, and event type and has a significant impact on our society or nature. Uncertain event knowledge also exists widely in the real world. Knowledge graph uses a resource description framework to describe deterministic and static knowledge in the real world. And knowledge graph embedding can embed the nodes and relations in the knowledge graph into vector space to represent more data features. However, real-world knowledge is often dynamic and uncertain, and these knowledge also changes with the state of events. Existing knowledge graph embedding models cannot solve the uncertainty and dynamically changing information of events in this embedding framework. To solve this problem, we study an embedding model for uncertain, dynamic state representation and propose a strongly adaptive fuzzy event embedding model (FERKE). Specifically, we first propose a fuzzy event embedding model that combines coarse-grained and fine-grained fuzziness, which provides an underlying representation framework for FERKE. Then, in the hyperbolic embedding space, the nodes and relations in the fuzzy event graph and the fine-grained fuzziness of each element are, respectively, embedded. FERKE utilizes the information aggregation and transmission of heterogeneous graph neural network to capture the rich interactions between fuzzy events and entity knowledge. We conduct an experimental evaluation of FERKE based on the constructed fuzzy event dataset. The results show that the FERKE proposed in this study is better than existing methods and can handle complex fuzzy event knowledge.

Resource Description Framework (RDF) is widely used in semantic extraction, unified organization, and intelligent processing of large amounts of data because of its machine intelligibility. For example, knowledge graph based on RDF is commonly used in intelligent search, recommendation system, and smart medical treatment. And RDF is used to express the relationship between entities and process the semantics of data. Many efforts have been made to convert various data (such as relational database, XML, and JSON) into RDF. Yet, the effective generation of usable RDF data is still an urgent problem to be solved. With the wide use of NoSQL database, massive data is stored in NoSQL database, but the research on generating RDF from NoSQL database is not emphasized. We put forward a formal definition of MongoDB, and according to this definition, we propose a method of automatically extracting data from MongoDB and building corresponding RDF. Based on this method, we have also implemented a prototype system named M2R to validate method performance. The experimental results show that our approach is feasible and efficient.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

The Resource Description Framework and RDF Schema recommended by the World Wide Web Consortium provide a flexible model for semantically representing information about resources on the Web, which are playing an increasingly important role in intelligent processing of large-scale data. With the widespread acceptance and applications of RDF(S), construction of RDF(S) is of increasing importance. Automatic construction of RDF(S) with diverse data has attracted more attention. In this paper, we propose a novel approach for constructing an RDF(S) with object-oriented databases that are suitable for non-traditional applications. We propose the formal rules of mapping an object-oriented database model into a RDF(S) model based on the formal definitions of these two models. We develop a tool named OODB2RDF to verify our approach.

Existing event extraction methods independently identify and classify each argument role separately, ignoring the interdependence between different parameter roles. Further, these methods rely on simple vectors to represent word embeddings. By embedding explicit syntactic constraints in the attention mechanism, we address these shortcomings by using dependency syntax to guide the text modeling. Specifically, we use dependency syntax to guide the BERT model for Chinese event argument role extraction, which mainly consists of three stages. First, the self-attention method guided by a dependency syntactic parsing tree is embedded in the Transformer computing framework of the BERT model. In addition to obtaining a deep two-way linguistic representation of a word according to its context information, this method also expresses the long-distance syntactic dependency relationships between words based on context information. Secondly, the designed conditional layer normalization method is applied to the event argument extraction model in order to integrate the semantic information of trigger words into the text, thereby improving the accuracy of the argument role extraction. Lastly, conditional random fields (CRFs) are used to determine the optimal sequence of labels at the sentence level based on the dependence relationships between adjacent labels. According to the experimental results, the constructed model outperforms several strong baselines in the Chinese event argument extraction task on the ACE2005 dataset and the iFLYTEKA.I.2020 dataset.

As a ubiquitous form of data in the natural world, time has been widely used in many domains such as the military, finance, medical treatment, and environment. Temporal data models that are used to model the dynamic development process of data are constantly proposed, such as temporal database and temporal XML. With the rapid development and popularization of the Web, the requirement of dealing with diverse data efficiently is becoming more important. As a new generation of data exchange language, JSON (JavaScript Object Notation) has been widely used because it is lightweight, has fast parsing, and has high transmission efficiency. In this paper, we propose a novel temporal JSON data model. Based on the proposed temporal JSON data model, we further propose two temporal JSON query languages, t-JSONPath, and t-JSONiq, which are the temporal extensions to JSONPath and JSONiq, respectively. With the proposed temporal JSON data model, we demonstrate our temporal JSON query languages with typical temporal query examples and evaluate their query response times.

With the enrichment of the RDF (resource description framework), integrating diverse data sources may result in RDF data duplication. Failure to effectively detect the duplicates brings redundancies into the integrated RDF datasets. This not only increases unnecessarily the size of the datasets, but also reduces the dataset quality. Traditionally a similarity calculation is applied to detect if a pair of candidates contains duplicates. For massive RDF data, a simple similarity calculation may lead to extremely low efficiency. To detect duplicates in the massive RDF data, in this paper we propose a detection approach based on RDF data clustering and similarity measurements. We first propose a clustering method based on locality sensitive hashing (LSH), which can efficiently select candidate pairs in RDF data. Then, a similarity calculation is performed on the selected candidate pairs. We finally obtain the duplicate candidates. We show through experiments that our approach can quickly extract the duplicate candidates in RDF datasets. Our approach had the highest F score and time performance in the OAEI (Ontology Alignment Evaluation Initiative) 2019 competition.

Knowledge graph (KG) completion aims to infer new facts from incomplete knowledge graphs. Most existing solutions focus on learning from time-aware fact triples and ignore the spatial information. In reality, knowledge graphs can evolve with time as well as the changing locations, such as the flight domain. Therefore, integrating spatiotemporal information into knowledge graph representation is important for the knowledge graph completion. To address this problem, this paper proposes two Spatio Temporal-aware knowledge graph completion models based on the Sequence Encoder, namely STSE and S-TSE, which incorporate the spatial and temporal information into relations. Specifically, the model consists of two steps: spatiotemporal-aware relation encoding and final scoring function evaluation. The first stage composes the spatiotemporal information into different tokens. Then two methods are proposed to obtain the embedding of spatiotemporal-aware relation by utilizing the Recursive Neural Network. The second stage proposes different scoring functions for two models. Empirically evaluation of the proposed models is conducted on spatiotemporal-aware KG completion task on two public datasets. Experimental results demonstrate the effectiveness of the proposal for spatiotemporal knowledge graph completion.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) have been used for meta-data management on the Web. With the rapid development of Web-based applications, the growth of RDF(S) data is increasing dramatically. In the context of open Web, uncertainty naturally arises in RDF and RDFS (RDF(S) in short). To manage large-scale fuzzy RDF(S) data efficiently and effectively, we propose, in this paper, a fuzzy RDF(S) storage schema with fuzzy HBase databases (FHDBs). On the basis, we investigate how to query in FHDBs. We propose a set of query algorithms. We implement a prototype system to demonstrate the feasibility of our approach.

Abstract Fuzzy spatio‐temporal knowledge is required in a wide range of application fields, including GIS, spatio‐temporal database, and artificial intelligence. Ontology, as a formal method of knowledge representation, plays a key role in the Semantic Web. Therefore, how to extend ontologies to represent fuzzy spatio‐temporal knowledge needs to be solved. In this paper, we propose an approach for representing fuzzy spatio‐temporal knowledge with ontologies and investigate a formal construction of fuzzy spatio‐temporal ontologies based on fuzzy Petri nets. First, we propose a formal definition of fuzzy spatio‐temporal ontologies. In addition, based on fuzzy description logic, a description logic named the fuzzy spatio‐temporal description logic, which provides a logical basis for the fuzzy spatio‐temporal ontologies, is presented. Furthermore, a fuzzy spatio‐temporal web ontology language, which is fuzzy and spatio‐temporal extension of standard ontology language OWL, is proposed to make the fuzzy spatio‐temporal ontologies easy to use and support efficient reasoning. On the basis of fuzzy spatio‐temporal ontologies, we investigate a formal approach for constructing fuzzy spatio‐temporal ontologies from fuzzy spatio‐temporal Petri nets, ie, transforming fuzzy spatio‐temporal Petri nets (including Petri net model and Petri net instance) into fuzzy spatio‐temporal ontologies at both structure and instance levels. Finally, we prove the correctness of the transformation and provide a detailed transformation example.

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object-oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.

Vagueness (or imprecision) naturally arises in real-world Resource Description Framework (RDF) data. The way we query RDF data is a crucial subject because of the vagueness and the wide connectivity of such data. Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, there are a few works that have been led on RDF graph database. In this study, we dealt with a specific type of fuzzy quantified queries in the context of a (fuzzy) RDF graph database. We first show how these queries can be expressed and interpreted in a quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. A query processing strategy based on subgraph matching queries is also proposed. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is, enumerating all RDF isomorphism from the given RDF graph patterns into the data graph. The algorithm is based on a backtracking strategy, which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we perform some experiments to study its performances. The results of these experiments show that the method of dealing with fuzzy quantification in a query is feasible.

eXtensible Markup Language (XML) has been the de facto standard of data representation and exchange over the Web. In addition, imprecise and uncertain data are inherent in the real world. Although fuzzy data have been extensively investigated in the context of the relational model, the classical relational database model and its fuzzy extension to date do not satisfy the need of modeling complex objects with imprecision and uncertainty on the Web. On the basis of possibility theory, this paper concentrates on fuzzy information modeling in the fuzzy XML model and the fuzzy IFO model. In particular, the formal approach to mapping a fuzzy IFO model to a fuzzy document-type definition model is developed.

Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.

JSON is a simple, compact and light weighted data exchange format to communicate between web services and client applications. NoSQL document stores evolve with the popularity of JSON, which can support JSON schema-less storage, reduce cost, and facilitate quick development. However, NoSQL still lacks standard query language and supports eventually consistent BASE transaction model rather than the ACID transaction model. This is very challenging and a burden on the developer. The relational database management systems (RDBMS) support JSON in binary format with SQL functions (also known as SQL/JSON). However, these functions are not standardized yet and vary across vendors along with different limitations and complexities. More importantly, complex searches, partial updates, composite queries, and analyses are cumbersome and time consuming in SQL/JSON compared to standard SQL operations. It is essential to integrate JSON into databases that use standard SQL features, support ACID transactional models, and has the capability of managing and organizing data efficiently. In this article, we empower JSON to use relational databases for analysis and complex queries. The authors reveal that the descriptive nature of the JSON schema can be utilized to create a relational schema for the storage of the JSON document. Then, the powerful SQL features can be used to gain consistency and ACID compatibility for querying JSON instances from the relational schema. This approach will open a gateway to combine the best features of both worlds: the fast development of JSON, consistency of relational model, and efficiency of SQL.

The Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a World Wide Web Consortium recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. In real-world applications, there are many practical situations where the descriptions of objects are accompanied by a degree of uncertainty. In an open Web environment, it is common case that the data from different sources may often contain inconsistent or imprecise information. In this paper, a fuzzy RDF data model is proposed. We present the syntax and semantics of fuzzy RDF data model. In particular, we investigate in the paper how to formally map fuzzy RDF data to relational databases.

Keyword searches based on the keywords-to-SPARQL translation is attracting more attention because of a growing number of excellent SPARQL search engines. Current approaches for keyword search based on the keywords-to-SPARQL translation suffer from returning incomplete answers or wrong answers due to a lack of underlying schema information. To overcome these difficulties, in this article, we propose a new keyword search paradigm by translating keyword queries into SPARQL queries for exploring RDF data. An inter-entity relationship summary with complete schema information is distilled from the RDF data graph for composing SPARQL queries. To avoid potentially wasteful summary graph expansion, we develop a new search prioritization scheme by combining the degree of a vertex with the distance from the original keyword element. Starting from the ordered priority list that is built in advance, we apply the forward path index to faster find the top-k subgraphs, which are relevant to the conjunction of the entering keywords. The experimental results show that our approach is efficient and scalable.

This paper concentrates on modeling probabilistic events with fuzzy probability measures in relational databases and XML (Extensible Markup Language). Instead of crisp probability degrees or interval probability degrees, fuzzy sets are applied to represent imprecise probability degrees in relational databases and XML. A probabilistic XML model with fuzzy probability measures is introduced, which incorporates fuzzy probability measures to handle imprecision and uncertainty. In particular, the formal approach to reengineering the relational database model with fuzzy probability measures into the DTD (document type definition) model with fuzzy probability measures is developed in the paper.

Representing and identifying the topological relations between fuzzy spatiotemporal regions over time have an important significance in reasoning fuzzy spatiotemporal relations. The research issue of topological relations between fuzzy regions over time has attracted a lot of attention in the recent years. However, modeling topological relations needs to be further developed and it is still an open issue. In this paper, we extend the 9-Intersection model to establish a model for identification of topological relations between fuzzy spatiotemporal regions over time, and use extended intersection matrix to identify all possible topological relations. On the basis of our topological relation model, we extend two-dimensional Egg/Yolk model to three-dimensional space and calculate the corresponding intersection matrixes of each topological relations. Furthermore, after investigation of topological complexity and topological distance based on our proposed topological relation model, we propose a comprehensive conceptual neighborhood graph of the topological relations implemented on MATLAB.

Petri Net (PN) has proven to be quite effective tool for graphical modeling, mathematical modeling, simulation, and real time control by the use of places and transitions. However, information imprecision and uncertainty exist in many real-world applications, and PNs found themselves inadequate to address the problems of imprecision and uncertainty in data. Therefore, the Fuzzy Petri Net (FPN) was developed and had been employed in many different fields like communication, manufacturing, electronics, and etc. In particular, with the wide utilization of FPNs, many researchers suggest that FPNs should be reused and shared. Emerging the Semantic Web technologies, such as fuzzy ontology, can play an important role in this scenario. On this basis, in this paper, we propose a fuzzy ontology approach for representing FPNs. First, we propose a formal definition of FPNs. Then, we give a complete definition of fuzzy OWL ontologies, where fuzzy ontologies formulated in fuzzy OWL language are called fuzzy OWL ontologies. Based on the formalization of FPNs and fuzzy OWL ontologies, we further propose a fuzzy ontology approach for representing FPNs, where we translate the key features of FPNs into the elements of fuzzy OWL ontologies such as fuzzy classes, fuzzy properties, fuzzy individuals, and fuzzy axioms. Finally, based on the translated fuzzy ontologies, we briefly discuss how to reason on FPNs through the reasoning mechanism of fuzzy ontologies.

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.

XML has been the de-facto standard of information representation and exchange over the web. As the next generation of the Web language, XML is straightforwardly usable over the Internet. At the same time, the real world is filled with imprecision and uncertainty. However, the existed works fall short in their ability to model imprecise and uncertain data using XML. In this paper, we propose a new fuzzy XML data model based on XML Schema. With the model used, the fuzzy information in XML documents can be represented naturally. Along with the model, an associated algebra is presented formally. We also introduce how to use our algebra to capture queries expressed in XQuery. It shows that this model and algebra can establish a firm foundation for publishing and managing the histories of fuzzy data on the Web. © 2010 Wiley Periodicals, Inc.

Database integration is an important discipline for constructing heterogeneous multidatabases on one hand. On the other hand, fuzzy information has been introduced into databases and studied extensively. One of the main problems in integration of multidatabases developed in different sites is resolution of conflicts. This problem becomes very complex when databases contain fuzzy information. Little has been reported on integrating fuzzy multidatabases so far. In this paper, we focus on integrating possibility-based fuzzy relational databases. The new types of conflicts arising in schema level and instance level in the fuzzy multidatabase integration are identified. The resolutions of the new types of conflicts are proposed. In particular, a method for identifying the entities in the fuzzy multidatabase integration with incompatible keys is developed.

Ontology is an important part of the W3C standards for the Semantic Web, and how to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. However, information imprecision and uncertainty exist in many re

In order to find all occurrences of a twig pattern in XML documents, a considerable amount of twig pattern matching algorithms have been proposed. At the same time, previous work mainly focuses on twig pattern query under the complete semantics. However, there is often a need to produce partial answers because XML data may have missing sub-elements. Furthermore, the existed works fall short in their ability to support twig pattern query under different semantics in fuzzy XML. In this paper, we study the problem of twig matches in fuzzy XML. We begin by introducing the extended region scheme to accurately and effectively represent nodes information in fuzzy XML. We then discuss the fuzzy query semantics and compute the membership information by using Einstein operator instead of Zadeh's min-max technique. On the basis, we propose two efficient algorithms for querying twig under complete and incomplete semantics in fuzzy XML. The experimental results show that our proposed algorithms can perform on the fuzzy twig pattern matching efficiently.

Rules have been playing an increasingly important role in the Semantic Web. However, general rule languages are not capable of representing much imprecise and uncertain knowledge in the Semantic Web, nor are if-then rules. Combining if-then rules with OWL DL in the framework of fuzzy sets and possibility distribution, we propose fuzzy Semantic Web if-then Rule Language (f-SW-if-then-RL), and investigate its syntax and semantics. Considering nonmonotonicity, we employ unless rules to extend f-SW-if-then-RL, and f-SW-if-then-unless-RL appears. Two kinds of negation are introduced to express semantics of unless rules. And we extend rule interchange format R2ML to encode nonmonotonic fuzzy rules.

To deal with the problem of empty or too little answers returned from a Web database in response to a user query, this paper proposes a novel approach to provide relevant and ranked query results. Based on the user original query, we speculate how much the user cares about each specified attribute and assign a corresponding weight to it. This original query is then rewritten as an approximate query by relaxing the query criteria range. The relaxation order of all specified attributes and the relaxed degree on each specified attribute are varied with the attribute weights. For the approximate query results, we generate users' contextual preferences from database workload and use them to create a priori orders of tuples in an off-line preprocessing step. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at query time to expeditiously provide ranked answers. Results of a preliminary user study demonstrate that our query relaxation and results ranking methods can capture the user's preferences effectively. The efficiency and effectiveness of our approach is also demonstrated by experimental result.

Resource Description Framework (RDF) together with RDF Schema (RDFS) is important Semantic Web standards proposed from W3C. RDF is a knowledge representation language to deal with hard semantics in the description and manipulation of crisp metadata. However, information is often vague or ambiguous. Many metadata are fuzzy by nature, and the Semantic Web should be capable to represent fuzzy data. In this paper, a fuzzy RDF data model is presented including fuzzy RDF syntax and fuzzy RDF semantics. A distinctive feature of fuzzy RDF data model, which is based on fuzzy logic theory, is able to deal with special data whose values are no more crisp values, but may be a fuzzy set.

In the real world, human knowledge and natural language have a big deal of imprecision and uncertainty. Imprecision and uncertainty play in the semantic Web context, as well as to many applications that use description logics (DLs) to capture, represent and perform reasoning with domain knowledge. In this paper, a fuzzy extension of description logic languageSIis presented, which combines vague sets with SI. Its syntax, semantics and inference problems are investigated in the paper. Also the tableau algorithm is developed for reasoning in the vagueSI.

The Semantic Web is expected to process concept knowledge and data information in an intelligent and automatic way. Recent research has shown that OWL has a serious limitation on data types; i.e., it does not support customized data types and customized data type predicates. Furthermore, it canpsilat process imprecise and uncertain information which widely exists in the Semantic Web and Ontology. These issues are being addressed by W3C Semantic Web Best Practices and Development Working Group. In the current paper, we make the following two contributions to solve the above limitations: (i) present a new kind of fuzzy description logic F-SHOIQ(G) which can not only support the representation and reasoning of fuzzy concept knowledge, but also support fuzzy data information with customized fuzzy data types and customized fuzzy data type predicates; (ii) give the tableau algorithm for F-SHOIQ(G) and prompt a flexible reasoning architecture for fuzzy data type reasoning, also the design for fuzzy data type reasoner is discussed here. The example in paper witnesses the representation and reasoning capabilities of F-SHOIQ(G) go clearly beyond the other DLs.

Information imprecision and uncertainty exist in many real-world applications and hence fuzzy data modeling has been extensively investigated in various data models. This paper focuses on the representation and reasoning of fuzzy ER data model with description logic. Firstly, we give the formal definition and semantics of fuzzy ER model. Then based on the description logic DLR, a kind of new fuzzy description logic, i.e., fuzzy description logic FDLR (fuzzy DLR), is presented thoroughly. The definitions of syntax, semantics, and knowledge base form are given for FDLR. The fuzzy ER model with fuzzy description logic FDLR is investigated to translate fuzzy ER model into FDLR knowledge bases. With an example, the fact that the fuzzy ER model can be well represented by FDLR can be explained. The reasoning problem of satisfiability, subsumption relation, and redundancy of fuzzy ER model may reason automatically through reasoning mechanism of fuzzy description logic FDLR. The correctness of translation and reasoning problems are proved.

Sensors are used to monitor some physical phenomena such as contamination, climate, building, and so on. The sensors collect and communicate their readings to the sensor databases for making decisions and answering various user queries. Due to continuous changes and possible errors in these values, the data values recorded in sensor databases may differ from the actual status. Queries using these values can yield incorrect and misleading answers. In order to manage the imprecision between the actual sensor value and the database value, the framework representing imprecision of sensor data has been proposed, in which each data value is represented as an interval. In this paper, we examine the situation when answer imprecision can be represented qualitatively and quantitatively. In particular, we propose two new kinds of imprecise queries called Top-k imprecise query and imprecise threshold query. Also, we investigate techniques for evaluating the qualitative and quantitative queries.

Ontology is an important part of the W3C standards for the Semantic Web used to specify standard conceptual vocabularies to exchange data among systems, provide reusable knowledge bases, and facilitate interoperability across multiple heterogeneous systems and databases. However, current ontology is not sufficient for handling vague information that is commonly found in many application domains. A feasible solution is to import the fuzzy ability to extend the classical ontology. In this article, we propose a fuzzy ontology generation framework from the fuzzy relational databases, in which the fuzzy ontology consists of fuzzy ontology structure and instances. We simultaneously consider the schema and instances of the fuzzy relational databases, and respectively transform them to fuzzy ontology structure and fuzzy RDF data model. This can ensure the integrality of the original structure as well as the completeness and consistency of the original instances in the fuzzy relational databases.

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility-based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.