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Property graphs (PG) and RDF graphs are two popular database graph models, but they are not interoperable: data modeled in PG cannot be directly integrated with other data modeled in RDF. This lack of interoperability also impedes the use of the tools of one model when data are modeled in the other. In this paper, we propose PRSC, a configurable conversion to transform a PG into an RDF graph. This conversion relies on PG schemas and user-defined mappings called PRSC contexts. We also formally prove that a subset of PRSC contexts, called well-behaved contexts, can be used to reverse back to the original PG, and provide the related algorithm. Algorithms for conversion and reversion are available as open-source implementations. [ABSTRACT FROM AUTHOR]

Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding whole legal documents. Some research efforts have been made in this area, but there is still a lot to be done to have legal information available more easily to be found. Thus, due to the large number of published legal documents and the high degree of connectivity, simple access to the document is not enough. It is necessary to recover the related legal framework for a specific need. In other words, the retrieval of the set of legal documents and their parts related to a specific subject is necessary. Therefore, in this work, we present a proposal of a RDF-based graph to represent and search parts of legal documents, as the output of a set of terms that represents the pursued legal information. Such a proposal is well-grounded on an ontological view, which makes possible to describe the general structure of a legal system and the structure of legal documents, providing this way the grounds for the implementation of the proposed RDF graph in terms of the meaning of their parts and relationships. We posed several queries to retrieve parts of legal documents related to sets of words and the results were significant. [ABSTRACT FROM AUTHOR]

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications. [ABSTRACT FROM AUTHOR]

Solving the Arithmetic Word Problems (AWPs) using AI techniques has attracted much attention in recent years. We feel that the current AWP solvers are under-utilizing the relevant domain knowledge. We present a knowledge- and learning-based system that effectively solves AWPs of a specific type--those that involve transfer of objects from one agent to another (Transfer Cases (TC)). We represent the knowledge relevant to these problems as TC Ontology. The sentences in TC-AWPs contain information of essentially four types: before-transfer, transfer, after-transfer, and query. Our system (KLAUS-Tr) uses statistical classifier to recognize the types of sentences. The sentence types guide the information extraction process used to identify the agents, quantities, units, types of objects, and the direction of transfer from the AWP text. The extracted information is represented as an RDF graph that utilizes the TC Ontology terminology. To solve the given AWP, we utilize semantic web rule language (SWRL) rules that capture the knowledge about how object transfer affects the RDF graph of the AWP. Using the TC ontology, we also analyze if the given problem is consistent or otherwise. The different ways in which TC-AWPs can be inconsistent are encoded as SWRL rules. Thus, KLAUS-Tr can identify if the given AWP is invalid and accordingly notify the user. Since the existing datasets do not have inconsistent AWPs, we create AWPs of this type and augment the datasets. We have implemented KLAUS-Tr and tested it on TC-type AWPs drawn from the All-Arith and other datasets. We find that TC-AWPs constitute about 40% of the AWPs in a typical dataset like All-Arith. Our system achieves an impressive accuracy of 92%, thus improving the state-of-theart significantly. We plan to extend the system to handle AWPs that contain multiple transfers of objects and also offer explanations of the solutions. [ABSTRACT FROM AUTHOR]

Resource Description Framework (RDF) graphs have become an important data source for many knowledge discovery algorithms and data mining tasks. However, most complex analyses that use knowledge discovery algorithms require data in a vector representation format. As a result, several RDF entity embedding techniques have emerged in which entities in the RDF graph are represented as low-dimensional vectors. These techniques generate sequences of entities using graph walk and use language modeling techniques to extract the feature from the sequences used to learn the embedding. However, sequences produced by graph walks only capture structural context; they are unable to capture latent context, such as semantically related information, which is an important property of RDF data. In this paper, we present a novel method that consists of a series of steps that generate sequences. These sequences not only capture structural context but also semantic property. The method for structural context includes (1) a new concept of similar entities in which tradeoffs are made between similar outgoing edges and outgoing nodes and (2) a new structural similarity, which calculates the similarity between two entities in each sequence. We can generate sequences based on structural similarity so that similar entities contain sequences with similar structures. The method for the semantic property combines sequences with the same semantic to generate latent sequences that cannot be generated by traversing the graph. This paper presents experimental results and a case study using real graphs to show that the proposed method outperforms existing methods in terms of quality and efficiency. [ABSTRACT FROM AUTHOR]

Purpose: The Simple Knowledge Organization Sys tem (SKOS) is a widely used data model for sharing and linking knowledge organization sys tems on the web. It offers a cos t-effective way to migrate exis ting knowledge organization sys tems to the Semantic Web. To integrate ASFA into the Semantic Web, the ASFA dataset needs to be converted and deployed as an RDF graph based on SKOS. To achieve this, the records in ASFA's Iran MARC format mus t be reengineered. This s tudy aims to re-engineer the ASFA dataset using data mining in the CRISP framework and deploy it on the open-source platform Skosmos. Method: The s tudy used the developmental-applied type of research and employed the CRISP-D.M. methodology, unsupervised type, and hierarchical clus tering technique for data mining to s tart the project, we firs t needed to unders tand the business goal. This goal was to convert the ASFA dataset into the SKOS data model, creating an RDF graph. It was discovered that ASFA's heritage data comprises 1,880 records categorized into 18 fields, including education, literature, communication, economy, his tory, Sufism and mys ticism, sociology, geography, law, psychology, linguis tics, religion, political science, philosophy, technology, experimental science, librarianship and information, management, culture, and art. The data was prepared by identifying and correcting missing and outlier data and before s tarting the project, our team needed to fully comprehend the business's objective. The ultimate goal was to convert the ASFA dataset into the SKOS data model. This was done to better comprehend the business objective. Creating an RDF graph. The modeling s tage utilized the hierarchical clus tering technique macrocode in Excel to generate target feature values. The model was evaluated through a visual inspection technique and random sampling method. In the sixth s tep, Iran MARC data was converted to SKOS as an RDF graph using the SkosPlay tool, and the data was transferred to the Vocbench platform. ASFA Dataset was deployed on the Skosmos platform using the Turtle format. Findings: The main finding of this s tudy is the deployment and development of ASFA Dataset based on SKOS/RDF on the open source platform Skosmos at kosmos.nlai.ir. The total number of records increased to 11,880 records creating collection records for clus tering. One of the important findings during the data preparation s tage was the compilation of the mapping table between SKOS core elements and Iran MARC fields. Conclusion: By integrating s tages of methodologies used in the literature review within the CRISP framework, an innovative method was developed for converting thesauri into a lightweight ontology based on SKOS/RDF graph format. [ABSTRACT FROM AUTHOR]

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of partial evaluation: if the volume of intermediate results is large, a large number of repeated partial matches will be generated; and the assembly computation handled by the master would be a bottleneck. In this paper, we propose an optimal partial evaluation algorithm and a filter method to reduce partial matches by exploring the computing characteristics of partial evaluation and assembly framework. (1) An index structure named inner boundary node index (IBN-Index) is constructed to prune for graph exploration to improve the searching efficiency of the partial evaluation phase. (2) The boundary characteristics of local partial matches are utilized to construct a boundary node index (BN-Index) to reduce the number of local partial matches. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Linked Open Data (LOD) is an emerging Web technology to store and publish structured data in the form of interlinked knowledgebases like DBpedia, Freebase, Wikidata, and Yago. It uses structured data from multiple domains, and it can be used to conceptualize a concept of interest. Recently, researchers have shown that incorporating contextual features in recommender systems improves rating prediction accuracy. However, identification of contextual features for building context-aware recommender systems is a major bottleneck. To this end, in this article, we present the development of a context-based recommender system, CRecSys, for item ratings prediction in movie domain. CRecSys extracts item-based contextual features from the underlying dataset and generates an RDF graph to model items and their contextual features for computing context-based items similarity using graph matching techniques and item-based collaborative filtering. It uses LOD and two well-known movie data sources - Rotten Tomatoes and IMDB for item profiling using a dataset of 1300 movies. CRecSys is experimentally evaluated over two movie datasets, one is generated by the authors and second is the MovieLens-1M benchmark dataset. CRecSys is also compared with ten baselines and two state-of-the-art recommendation methods, and performs significantly better. It is also empirically established that CRecSys is able to effectively deal with some of the open challenges like cold-start and limited content problems of the traditional recommender systems.

The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs. Linguistics). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains. [ABSTRACT FROM AUTHOR]

Summary: With the increasing size of the RDF dataset, the application of parallel computing frameworks is applying more and more widely in big data. In order to realize the parallel query of data, an edge partitioning algorithm is proposed based on RDF sentence graph. Firstly, the algorithm transforms the RDF graph into an RDF sentence graph using coarsening and partitioning. Secondly, the minimum degree vertex partitioning algorithm is put forward to partition the RDF sentence graph. Finally, according to the equivalence thought between edge segmentation of RDF sentence and vertex segmentation of RDF graph, the intersection between RDF subgraphs is the vertex cut set of RDF graph to achieve parallel query of RDF data. The experimental results show that the algorithm's partitioning time and efficiency are better than the traditional algorithms. [ABSTRACT FROM AUTHOR]

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With the proliferation of knowledge graphs, massive RDF graphs have been published on the Web. As an essential type of queries for RDF graphs, Regular Path Queries (RPQs) have been attracting increasing research efforts. However, the existing query processing approaches mainly focus on RPQs under the standard semantics, which cannot provide the provenance of the answer sets. We propose a distributed Pregel-based approach DP2RPQ to evaluating provenance-aware RPQs over big RDF graphs. Our method employs Glushkov automata to keep track of matching processes of RPQs in parallel. Meanwhile, three optimization strategies are devised according to the cost model, including vertex-computation optimization, message-communication reduction, and counting-paths alleviation, which can reduce the intermediate results of the basic DP2RPQ algorithm dramatically and overcome the counting-paths problem to some extent. The proposed algorithms are verified by extensive experiments on both synthetic and real-world datasets, which show that our approach can efficiently answer the provenance-aware RPQs over large RDF graphs. Furthermore, the RPQ semantics of DP2RPQ is richer than that of RDFPath, and the performance of DP2RPQ is still far better than that of RDFPath. [ABSTRACT FROM AUTHOR]

SPARQL, the W3C standard for RDF query languages, has gained significant popularity in recent years. An increasing amount of effort is currently being exerted to improve the functionality and usability of SPARQL-based search engines. However, explaining missing items in the results of SPARQL queries or the so-called why-not question has not received sufficient attention. In this study, we first formalize why-not questions on SPARQL queries and then propose a novel explanation model, called answering why-not questions on SPARQL (ANNA) to answer why-not questions using a divide-and-conquer strategy. ANNA adopts a graph-based approach and an operator-based approach to generate logical explanations at the triple pattern level and the query operator level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations in terms of both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Linked data is a promising way to publish media data as resources on the Web and interlink them with other resources. While significant amounts of image, audio and video fragments have been tagged and exposed as linked data, searching and explaining the unexpected query results have been rarely studied. To improve the functionality and usability of SPARQL-based multimedia search engines, we focus on explaining missing items in the query results, or the so-called why-not question in this paper. We first formalize why-not questions on multimedia SPARQL queries and then propose a novel explanation model to answer why-not questions. Our model adopts a to generate logical explanations at the basic graph pattern level, the filter constraint level, or the multimedia function level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations both in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a new secured data model for cloud computing is proposed which uses partial resource description framework (RDF) encryption and token based access control system in which sensitive data in an RDF graph is encrypted and all other non-sensitive data are publicly lucid. The security process, the decryption process, and query process are the three essential procedures in this framework. The consequence of the security process is the encrypted data, encrypted metadata, and plain text fragments. The proposed technique permits the token based access control system for the decryption procedure. The query process incorporates the map reduce framework is for lessening the immense measure of employments. At long last, the query answer is sent to the user in light of the access token list (AT-list) of the system administrator. Our test comes about demonstrate that, the performance of the proposed technique is assessed in view of the precision, recall and execution time of the framework. Our proposed approach is actualized using Java and keep running on Windows XP framework and the Lehigh University Benchmark (LUBM) datasets are used for our examination. In the paper this new secured RDF data model is deployed and tested using AWS elastic beanstalk. [ABSTRACT FROM AUTHOR]

The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases. [ABSTRACT FROM AUTHOR]

This paper is inspired by ideas from the field of theoretical Mathematics, used for the partitioning of abstract spaces into equivalence classes, and applies analogous concepts in order to propose a classification of SPARQL queries into equivalence classes. The novel concepts of relevant queries and covering query are introduced in a manner appropriate for the study of SPARQL queries. These new definitions shed new light on the relations among SPARQL queries. They enable the formal identification of similar queries and this leads to the partition of SPARQL queries into equivalence classes of relevant queries. This work also discusses how the covering query relating two or more relevant queries can be useful from the perspective of computational cost when evaluating composite queries composed of simpler relevant queries. Hence, the introduction of the concept of relevance between queries provides not only obvious theoretical advantages, but also concrete practical ones, which in many cases have the potential to lower the computational cost of query evaluation. [ABSTRACT FROM AUTHOR]

Although SPARQL has been the predominant query language over RDF (Resource Description Framework) graphs, some query intentions cannot be captured well using only SPARQL syntax. On the other hand, keyword search enjoys widespread usage because of its intuitive way of specifying information needs, but suffers from the problem of low precision. To maximize the advantages of both SPARQL and keyword search, we introduce a novel paradigm that combines them and propose a hybrid query (called a S PARQL- K eyword (SK) query) that integrates SPARQL and keyword search. To answer SK queries efficiently, we propose a novel integrated query algorithm based on a structural index. We also present a distance-based optimization technique to further improve the efficiency of SK queries evaluation. We test our method in three large real RDF graphs and the experiments demonstrate both the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]