Semantic Query Processing Research Articles

FFO: Forest Fire Ontology and Reasoning System for Enhanced Alert and Management Services

Forest fires or wildfires pose a serious threat to property, lives, and the environment. Early detection and mitigation of such emergencies, therefore, play an important role in reducing the severity of the impact caused by wildfire. Unfortunately, there is often an improper or delayed mechanism for forest fire detection which leads to destruction and losses. These anomalies in detection can be due to defects in sensors or a lack of proper information interoperability among the sensors deployed in forests. This paper presents a lightweight ontological framework to address these challenges. Interoperability issues are caused due to heterogeneity in technologies used and heterogeneous data created by different sensors. Therefore, through the proposed Forest Fire Detection and Management Ontology (FFO), we introduce a standardized model to share and reuse knowledge and data across different sensors. The proposed ontology is validated using semantic reasoning and query processing. The reasoning and querying processes are performed on real-time data gathered from experiments conducted in a forest and stored as RDF triples based on the design of the ontology. The outcomes of queries and inferences from reasoning demonstrate that FFO is feasible for the early detection of wildfire and facilitates efficient process management subsequent to detection.

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

Semantic connection set-based massive RDF data query processing in Spark environment

Resource Description Framework (RDF) is a data representation of the Semantic Web, and its data volume is growing rapidly. Cloud-based systems provide a rich platform for managing RDF data. However, there is a performance challenge in the distributed environment when RDF queries, which contain multiple join operations, such as network reshuffle and memory overhead, are processed. To get over this challenge, this paper proposes a Spark-based RDF query architecture, which is based on Semantic Connection Set (SCS). First of all, the proposed Spark-based query architecture adopts the mechanism of re-partitioning class data based on vertical partitioning, which can reduce memory overhead and spend up index data. Secondly, a method for generating query plans based on semantic connection set is proposed in this paper. In addition, some statistics and broadcast variable optimization strategies are introduced to reduce shuffling and data communication costs. The experiments of this paper are based on the latest SPARQLGX on the Spark platform RDF system. Two synthetic benchmarks are used to evaluate the query. The experiment results illustrate that the proposed approach in this paper is more efficient in data search than contrast systems.

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

Combined bit map representation and its applications to query processing of resource description framework on GPU

Resource description framework (RDF) is a common representation in semantic web context, including the web data sources and their relations in the URI form. With the growth of data accessible on the Internet, the RDF data currently contains millions of relations. Thus, answering a semantic query requires going through large amounts of data relations, which is time consuming. In this work, we present a representation framework, combined bit map representation (CBM), which compactly represents RDF data while helping speed up semantic query processing using graphics processing units (GPUs). Since GPUs have limited memory size, without compaction the RDF data cannot be entirely stored in the GPU memory; the CBM structure enables more RDF data to reside in the GPU memory. Since GPUs have many processing elements, their parallel use speeds up RDF query processing. The experimental results show that the proposed representation can reduce the size of RDF data by 70%. Furthermore, the search time on this representation using the GPU is 60% faster than with conventional implementation.

Top-k User-Defined Vertex Scoring Queries in Edge-Labeled Graph Databases

We consider identifying highly ranked vertices in large graph databases such as social networks or the Semantic Web where there are edge labels. There are many applications where users express scoring queries against such databases that involve two elements: (i) a set of patterns describing relationships that a vertex of interest to the user must satisfy and (ii) a scoring mechanism in which the user may use properties of the vertex to assign a score to that vertex. We define the concept of a partial pattern map query (partial PM-query), which intuitively allows us to prune partial matchings, and show that finding an optimal partial PM-query is NP-hard. We then propose two algorithms, PScore_LP and PScore_NWST, to find the answer to a scoring (top- k ) query. In PScore_LP, the optimal partial PM-query is found using a list-oriented pruning method. PScore_NWST leverages node-weighted Steiner trees to quickly compute slightly sub-optimal solutions. We conduct detailed experiments comparing our algorithms with (i) an algorithm (PScore_Base) that computes all answers to the query, evaluates them according to the scoring method, and chooses the top- k , and (ii) two Semantic Web query processing systems (Jena and GraphDB). Our algorithms show better performance than PScore_Base and the Semantic Web query processing systems—moreover, PScore_NWST outperforms PScore_LP on large queries and on queries with a tree structure.

Meta Search Engine with Semantic Analysis and Query Processing

Meta Search Engine with Semantic Analysis and Query Processing

Semantic Event Region Query Processing in Sensor Networks

Semantic Event Region Query Processing in Sensor Networks

Real-Time Data Analytics and Event Detection for IoT-Enabled Communication Systems

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today's ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, i) we dene and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.

Real-time data analytics and event detection for IoT-enabled communication systems

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, (i) we define and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; (ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; (iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, (iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.

Semantic Annotation for Supporting Context-Aware Information Retrieval in the Transportation Project Environmental Review Domain

AbstractAlthough transportation practitioners nowadays have an unprecedented level of access to information, substantial gaps still exist in their ability to efficiently and reliably find the right information, at the right time, for the task or decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach that can capture and exploit the conceptualization of user needs, decision context, and content meanings in order to support the retrieval of information that is more relevant to decision making. The proposed IR approach includes three primary components: semantic annotation (SA), semantic query processing (SQP), and semantic document ranking (SDR). This paper focuses on SA for IR for supporting the transportation project environmental review (TPER) decision-making process. It proposes an epistemology-based SA algorithm for automatically annotating Web pages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology i...

Assessment on precision-imprecision essentials in Semantic query processing.

Background: Database systems users’ works with assumptions that stored data represent the correct subset of real world data and make quick decision based on imprecise knowledge in day to day life for survivals. The challenge is to make the system to recover from the imperfections i.e. not simply correction but make compatible. Method: The semantic mapping technology provide root for this, but it delays the processing and response time. That is mapping semantics for user input to retrieve the relevant information. Sometimes the information may be irrelevance but for other input it may exact so we should not remove it instead, managed to utilize appropriately to minimize the processing time. Relevance may not be prior defined but also be taken as user feedback based on the obtain output. Result: Processing time will be considerably minimized and user can find convenience nearest relevance needed. Applications: During on line application it will be more convenient for user to find the relevance and get their output satisfactory in an elapsed time.

Schema Matching Quality: Thesaurus as the Matcher

Thesaurus is used in many Information Retrieval (IR) applications such as data integration, data warehousing, semantic query processing and classifiers. It was also utilized to solve the problem of schema matching. Considering the fact of existence of many thesauri for a certain area of knowledge, the quality of schema matching results when using different thesauri in the same field is not predictable. In this paper, we propose a methodology to study the performance of the thesaurus in solving schema matching. The paper also presents results of experiments using different thesauri. Precision, recall, F-measure, and similarity average were calculated to show that the quality of matching changed according to the used thesaurus.

Effect of thesaurus size on schema matching quality

Thesaurus is used in many Information Retrieval (IR) applications such as data integration, data warehousing, semantic query processing and schema matching. Schema matching or mapping is one of the most important basic steps in data integration. It is the process of identifying the semantic correspondence or equivalent between two or more schemas. Considering the fact of the existence of many thesauri for identical knowledge domain, the quality and the change in the results of schema matching when using different thesauri in specific knowledge field are not predictable. In this research, we studied the effect of thesaurus size on schema matching quality by conducting many experiments using different thesauri. In addition, a new method in calculating the similarity between vectors extracted from thesaurus database is proposed. The method is based on the ratio of individual shared elements to the elements in the compound set of the vectors. Moreover, we explained in details the efficient algorithm used in searching thesaurus database. After describing the experiments, results that show enhancement in the average of the similarity is presented. The completeness, effectiveness, and their harmonic mean measures were calculated to quantify the quality of matching. Experiments on two different thesauri show positive results with average Precision of 35% and a less value in the average of Recall. The effect of thesaurus size on the quality of matching was statically insignificant; however, other factors affecting the output and the exact value of change are still in the focus of our future study.

Enabling Ontology Based Semantic Queries in Biomedical Database Systems.

There is a lack of tools to ease the integration and ontology based semantic queries in biomedical databases, which are often annotated with ontology concepts. We aim to provide a middle layer between ontology repositories and semantically annotated databases to support semantic queries directly in the databases with expressive standard database query languages. We have developed a semantic query engine that provides semantic reasoning and query processing, and translates the queries into ontology repository operations on NCBO BioPortal. Semantic operators are implemented in the database as user defined functions extended to the database engine, thus semantic queries can be directly specified in standard database query languages such as SQL and XQuery. The system provides caching management to boosts query performance. The system is highly adaptable to support different ontologies through easy customizations. We have implemented the system DBOntoLink as an open source software, which supports major ontologies hosted at BioPortal. DBOntoLink supports a set of common ontology based semantic operations and have them fully integrated with a database management system IBM DB2. The system has been deployed and evaluated with an existing biomedical database for managing and querying image annotations and markups (AIM). Our performance study demonstrates the high expressiveness of semantic queries and the high efficiency of the queries.

시맨틱 웹 데이터에서 접미사 배열 기반의 경로 질의 처리 기법

서로 연결된 데이터들의 의미를 컴퓨터가 이해하여 자동으로 처리할 수 있는 시맨틱 기술의 보급이 확산되고 있다. 시맨틱 웹에서 데이터에 대한 처리는 데이터 자체에 대한 접근뿐만 아니라 데이터 상호간의 연관성 즉, 데이터 상호간의 의미에 대한 이해와 접근을 중요시 하고 있다. 시맨틱 웹의 데이터와 그 연관성을 표현하기 위해 W3C에서는 RDF를 표준 형식으로 제정하였으며 RDF로 표현된 데이터에 대한 질의 처리를 지원하기 위해 여러 RDF 질의어가 제안되었으나 시맨틱 연관성을 고려한 질의어 정의와 이에 관련한 질의 처리 기법은 계속적인 연구가 필요한 분야이다. 본 논문에서는 RDF 질의 처리를 위해 소개된 접미사 배열 기반의 인덱싱 기법을 기반으로 시맨틱 연관성의 대표적 유형인 <TEX>${\rho}$</TEX>-path 질의를 처리하기 위한 방법을 제안한다. 제안된 질의 처리 방법의 성능 평가를 위해 다른 두 가지 형태의 처리 방법을 구현하여 실험적으로 비교하였다. 평균 질의 처리 시간 측정을 통해 제안 기법이 다른 두 가지 처리 방법에 비해 각각 약 1.8~2.5배와 3.8~11배의 우수한 처리 성능을 보인다. The applying of semantic technologies that aim to let computers understand and automatically process the meaning of the interlinked data on the Web is spreading. In Semantic Web, understanding and accessing the associations between data that is, the meaning between data as well as accessing to the data itself is important. W3C recommended RDF (Resource Description Framework) as a standard format to represent both Semantic Web data and their associations and also proposed several RDF query languages in order to support query processing for RDF data. However further researches on the query language definition considering the semantic associations and query processing techniques are still required. In this paper, using the suffix array-based indexing scheme previously introduced for RDF query processing, we propose a query processing approach to handle <TEX>${\rho}$</TEX>-path query which is the representative type of semantic associations. To evaluate the query processing performance of the proposed approach, we implemented two different types of query processing approaches and measured the average query processing times. The experiments show that the proposed approach achieved 1.8 to 2.5 and 3.8 to 11 times better performance respectively than others two.

A New Bidirectional Method for Ontologies Matching

Recently, much research has focused on developing techniques for schema/ontology matching and mapping as it is required in many areas e.g. heterogeneous database integration, merging of ontologies, semantic query processing. In this paper, we present a novel method to detect and repair the list of homologs concepts; this last is based on the bidirectional comparison (Checking) between the two matched ontologies to filter this list of concepts. The used measures are classical just to show the reliability and realisability of our method, but we can generalize it for supporting other advanced measures.

Rice Genes in Oryzabase

AbstractIn addition to maintaining a rice gene list, Oryzabase, a database of rice science, has also been hosting a website to submit newly identified genes. The current rice gene dictionary is based on a traditional rice trait gene dictionary and extended through time with new genes which are manually retrieved from journal articles. Currently over 4000 genes are listed. However it is still insufficient in both quantity and quality.Recently we started to apply automated extraction before manual annotation to enhance the efficiency of gene extraction. To improve the quality of information, we started collecting relevant DNA accessions and LOCUS-ID. In Oryzabase, we try to employ the help of users to overcome this information insufficiency by adding a comment box in each gene page to allow researchers to write additional information. Besides adding GO (gene ontology) and TO (trait ontology) to the genes, PO (plant ontology) has been newly assigned to enable better semantic query processing. The current status of gene extraction and ontology assignment and its related problems will be introduced.Oryzabase is available at "http://www.shigen.nig.ac.jp/rice/oryzabase/":http://www.shigen.nig.ac.jp/rice/oryzabase/.

System Π: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

RDF is the data interchange layer for the Semantic Web. In order to manage the increasing amount of RDF data, an RDF repository should provide not only the necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards these goals, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System Π, to pursue a better tradeoff among system scalability, query efficiency, and inference capabilities. System Π takes a hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD * semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining some new algebra operators. In addition, MD5 hash value of URI and schema level cache are introduced as practical implementation techniques. The results of performance evaluation on the LUBM benchmark and a real data set show that System Π has a better combined metric value than other comparable systems.

A framework for the massive knowledge Web

AbstractThis paper proposes a framework consisting of a semantic object model and a scalable platform for supporting effective publishing and sharing of massive knowledge on Web 2.0. The platform integrates the P2P computing techniques to provide users with flexible tools to manage personalized resources and services. The semantic object model provides an object‐oriented semantic description language and a semantic object query language for publishing and querying resources. Semantic indexing mechanisms are used to support efficient semantic query processing. The semantic object model enables users to define and manipulate semantics with more explicit object semantics and can be well adapted to the P2P platform to support scalable and extensible management on semantics‐rich Web resources. Copyright © 2008 John Wiley &amp; Sons, Ltd.