Using Semantic Queries to Enable Dynamic Service Invocation for Processes in the Internet of Things

Huber, Steffen; Seiger, Ronny; Kühnert, André; Schlegel, Thomas

doi:10.1109/icsc.2016.75

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…Some other recent applications of QE are plagiarism detection [203], event search [89,15,43], text classification [269], patent retrieval [180,181,268], dynamic process in IoT [122,123], classification of e-commerce [128], biomedical IR [1], enterprise search [174], code search [205], parallel computing in IR [179] and twitter search [151,304]. Table 7 summarizes some of the prominent and recent applications of QE in literature based on the above discussion.…”

Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

221

104

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With the ever increasing size of the web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. Query Expansion (QE) plays a crucial role in improving searches on the Internet. Here, the user's initial query is reformulated by adding additional meaningful terms with similar significance. QE -as part of information retrieval (IR) -has long attracted researchers' attention. It has become very influential in the field of personalized social document, question answering, cross-language IR, information filtering and multimedia IR. Research in QE has gained further prominence because of IR dedicated conferences such as TREC (Text Information Retrieval Conference) and CLEF (Conference and Labs of the Evaluation Forum). This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications -bringing out similarities and differences.There is a huge amount of data available on the Internet, and it is growing exponentially. This unconstrained information-growth has not been accompanied by a corresponding technical advancement in the approaches for extracting relevant information [191]. Often, a web-search does not yield relevant results. There are multiple reasons for this. First, the keywords submitted by the user can be related to multiple topics; as a result, the search results are not focused on the topic of interest. Second, the query can be too short to capture appropriately what the user is looking for. This can happen just as a matter of habit (e.g., the average size of a web search is 2.4 words [257,255]). Third, the user is often not sure about what he is looking for until he sees the results. Even if the user knows what he is searching for, he does not know how to formulate an appropriate query (navigational queries are exceptions to this [51]). QE plays an important part in fetching relevant results in the above cases. Most web queries fall under the following three fundamental categories [51, 139] :-Informational Queries: Queries that cover a broad topic (e.g., India or journals) for which there may be thousands of relevant results. -Navigational Queries: Queries that are looking for specific website or URL (e.g., ISRO).-Transactional Queries: Queries that demonstrate the user's intent to execute a specific activity (e.g., downloading papers or buying books).2 Hiteshwar Kumar Azad, Akshay DeepakCurrently, user-queries are mostly processed using indexes and ontologies, which work on exact matches and are hidden from the users. This leads to the problem of term mismatch: user queries and search index are not based on the same set of terms. This is also known as the vocabulary problem [99]; it results from a combination of synonymy and polysemy. Synonymy refers to multiple words with common meaning, e.g., "buy" and "purchase". Polysemy refers to words with multiple meanings, e.g., "mouse" (a computer device or an animal). Synonymous and polysemou...

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Section: Other Applicationsmentioning

confidence: 99%

Query expansion techniques for information retrieval: A survey

Azad

Deepak

2019

Information Processing & Management

221

104

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“…MobIoT takes care of registry performance and search functionality [47]. In [48] a resource detection infrastructure has been developed based on Q-learning techniques to enable context awareness. In this method, heterogeneous neighboring detection protocol are developed which reduce energy dissipation and detection delay.…”

Section: Introduction To Resource Detection In Iot and Big Datamentioning

confidence: 99%

Resource Management in a Peer to Peer Cloud Network for IoT

Javadpour

Wang

Rezaei

2020

Wireless Pers Commun

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“…The protocol has been developed based on the clouds and enterprise computing environment. Huber et al (2016) presents the semantic access method for the internet of things and this helps to make the communication between the web services and IoT devices. This technique uses the capability based scheme and for the resource allocation process used the context sensitive method.…”

Section: Introductionmentioning

confidence: 99%

Semantic Web Service Discovery for Mobile Web Services

Bhuvaneswari¹,

Karpagam

2017

IJBIDM

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The process of service discovery is the most important task in web services. But this service discovery process may degrade network performance due to the mobile environment. To overcome these issues, a new approach called 'semantic mobile web services (SMWS)' is proposed. This proposed semantic mobile web services is used for a better discovery process even in the high mobile environment. By using the query request (QRY REQ), response (RSP) packets, user can identify the location of the mobile node as well as discover the web services with the minimum utilisation of bandwidth resources. During the service discovery process, a service registrar is included between service requester and service provider to reduce overhead. The process of matchmaking produces the exact match responses for the respective requestor queries. This helps to increase the quality of performance and network efficiency. Simulation has analysed the performance of the proposed semantic mobile web services.

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Using Semantic Queries to Enable Dynamic Service Invocation for Processes in the Internet of Things

Cited by 14 publications

References 17 publications

Query expansion techniques for information retrieval: A survey

Query expansion techniques for information retrieval: A survey

Resource Management in a Peer to Peer Cloud Network for IoT

Semantic Web Service Discovery for Mobile Web Services

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