Visualizing and querying semantic social networks

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.

In a rapidly changing environment, a shift toward decentralized structure for organizations seems inevitable. Semantic Web technologies combined with social network analysis make the transition of a centralized organization to a decentralized one much easier. By increasing controllability and manageability of a decentralized structure, these tools can improve functionality and performance. A framework for creation and maintenance of a semantic social network is introduced and two real world organizations have been analyzed. After selecting one organization suitable for decentralization, network datasets were generated from its structure. Generated datasets have been used as the input of the framework to create a semantic social network for the corresponding organization. Social network analysis was then performed on both semantic and non semantic social networks. Gain of performance was observed in many areas such as decision making, flexibility, reliability and throughput. Semantic social network also enables other functionalities along ease of decentralization such as gain of knowledge about dynamic structure of the organization. These functionalities are also discussed.

Semantic social network (SSN) is an extension of the current social network wherein meaningful data is added with each piece of information, known as metadata. This metadata is processed by machines to make decisions based on intelligent algorithms. SSN gives a virtual social circle where individuals can share information, ideas, news and stay connected with their friends and family. The semantic social information can be used to measure various behavioral traits of SSN individuals using metrics. There are several metrics developed to calculate the individual’s traits of the semantic social network. However, few metrics may produce wrong results in the absence of time consideration during their calculations. In this article, we have proposed a novel technique to improve the accuracy of activity factor metric calculation and another metric to calculate peak active time for an activity. The extracted results from our novel SSN metrics can help authorities to make better decisions in providing offers, rewards, suggestions, and badges for the SSN members as per their needs and interests.

Effect of bioactive protein supplementation on equine gait kinematics

This paper presents an adaptive e-learning system which supports collaborative learning based on a location-based social network and semantic modeling. In the system, a social network among e-learning learners is dynamically constructed on the basis of the location information of learners using GPS sensors for collaborative learning. In addition, user modeling for semantic social network and adaptive e-learning is supported. For these, a mechanism that supports the construction of a dynamic social network using the location information of smart phones is provided. The user modeling for the construction of a semantic social network and the support of adaptive e-learning considers the information such as the users' achievement, preference, and learning goal. Through this system, e-learners can find mentors who are able to facilitate their learning and create communities for collaborative learning offline and online. The support of a location-based social network service and semantic user model in our system would increase interactions among e-learners and improve satisfaction regarding their mobile learning environment.

A synthesis of semantic social network and attraction theory for innovating community-based e-service

Computer manipulated social networks are usually built from the explicit assertion by users that they have some relation with other users or by the implicit evidence of such relations (e.g., co-authoring). However, since the goal of social network analysis is to help users to take advantage of these networks, it would be convenient to take more information into account. We introduce a three-layered model which involves the network between people (social network), the network between the ontologies they use (ontology network) and a network between concepts occurring in these ontologies. We explain how relationships in one network can be extracted from relationships in another one based on analysis techniques relying on this network specificity. For instance, similarity in the ontology network can be extracted from a similarity measure on the concept network. We illustrate the use of these tools for the emergence of consensus ontologies in the context of semantic peer-to-peer systems.

In this paper, we focus on the visualization of heterogeneous semantic networks obtained from multiple data sources. A semantic network comprising a set of entities and relationships is often used for representing knowledge derived from textual data or database records. Although the semantic networks created for the same domain at different data sources may cover a similar set of entities, these networks could also be very different because of naming conventions, coverage, view points, and other reasons. Since digital libraries often contain data from multiple sources, we propose a visualization tool to integrate and analyze the differences among multiple social networks. Through a case study on two terrorism-related semantic networks derived from Wikipedia and Terrorism Knowledge Base (TKB) respectively, the effectiveness of our proposed visualization tool is demonstrated.

We present a study on gender analysis of the Italian community of Information Systems—itAIS—based on a three-dimensional framework accounting for the context, the success, and the members’ attitudes. We represent the community as a semantic social network. We analyze the semantics of the information systems domain, the topology of the social network and the dynamics of interests by means of a suite of purposely-developed tools. The experimental work analyses the scientific papers accepted for presentation at the itAIS conference from 2007 to 2016. While the number of males is larger than that of females, we do not observe discrimination in the community. Moreover, despite observed diversity, Italian feminine scientists play a significant role in the community.

Semantic Web is the application of the knowledge advanced technologies for the Web and the distributed systems. One of the most novel technologies for the Web are the Web services. Web services are a simple solution to develop complex Web applications as social networks that they need the interoperability of many technologies. The interoperability has been got in this work by using service composition techniques. In this paper we propose a strategy to develop a semantic social network taking advantage of composition Web Services. Our proposal is concentrated in the annotation process. We have developed an annotation tool that produces an annotated post from a simple post, this process will be described in the subsection II-A.

Ontologies of public administrations: towards semantic social networks for citizens, administrations, and enterprises

Restoration strategies for native oyster populations rely on multiple sources of information, which often conflict due to time- and space-varying patterns in abundance and distribution. For instance, strategies based on population connectivity and disease resistance can differ, and extant and historical records of abundance and distribution are often at odds, such that the optimal strategy is unclear and valuable restoration sites may be excluded from consideration. This was the case for the Lynnhaven River subestuary of lower Chesapeake Bay, which was deemed unsuitable for Eastern Oyster restoration based on physical conditions, disease challenge, and extant oyster abundance. Consequently, we (i) evaluated previously unknown historical data from the 1800s, (ii) quantified extant oyster recruitment and abundance, physical conditions, and disease presence on constructed restoration reefs and alternative substrates, and (iii) assessed simulations from biophysical models to identify potential restoration sites in the metapopulation. The collective data distinguished numerous restoration sites (i) in the polyhaline zone (salinity 18.4-22.2) where disease resistance is evolving, (ii) where oysters were abundant in the late 1800s-early 1900s, (iii) of recent high recruitment, abundance and survival, despite consistent and elevated disease challenge, and (iv) interconnected as a metapopulation via larval dispersal. Moreover, a network of constructed restoration reefs met size structure, abundance and biomass standards of restoration success. These findings demonstrate that assumptions about the suitability of sites for oyster restoration based on individual processes can be severely flawed, and that in-depth examination of multiple processes and sources of information are required for oyster reef restoration plans to maximize success. We use these findings and previous information to recommend a strategy for successful restoration of subtidal oyster reefs throughout the range of the Eastern Oyster.

In the big data era, data are usually described by multiple information sources in many practical application fields, and it is infeasible way to mine interesting knowledge from a centralized huge source directly after aggregating all information sources, due to the problems of massive data, privacy concerns, and potential transmission cost. To mine decision rules from multiple information sources, therefore, we can mine local decision rules at different local information sources, and then put these rules to form a set of global decision rules. In this paper, we first present a formal representation of decision rule, which depends on the neighborhood granulation of each sample from the viewpoint of granular computing. Then, we obtain the weight of each local information source based on the consensus measure principle between local information sources. Finally, a weighting model for mining global decision rules via synthesizing all local decision rules is proposed. Extensive experimental results demonstrate that the proposed decision rules synthesization model is effective and scalable.

Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction researches, it is assumed either a snapshot of the social network or a social network with some missing links is available. Most existing researches therefore approach this problem by exploring the topological structure of the social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary information available. In this work, we introduce the pseudo cold start link prediction with multiple sources as the problem of predicting the structure of a social network when only a small subgraph of the social network is known and multiple heterogeneous sources are available. We propose a two-phase supervised method: the first phase generates an efficient feature selection scheme to find the best feature from multiple sources that is used for predicting the structure in the social network. In the second phase, we propose a regularization method to control the risk of over-fitting induced by the first phase. We assess our method empirically over a large data collection obtained from Youtube. The extensive experimental evaluations confirm the effectiveness of our approach.

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO‐LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO‐LDA model and the semantic modularity are verified by experimental analysis.