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Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day‐to‐day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work. This article is categorized under:Fundamental Concepts of Data and Knowledge > Human Centricity and User InteractionFundamental Concepts of Data and Knowledge > Knowledge RepresentationTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

Segmenting scientific abstracts into discourse categories like background, objective, method, result, and conclusion is useful in many downstream tasks like search, recommendation and summarization. This task of classifying each sentence in the abstract into one of a given set of discourse categories is called sequential sentence classification. Existing machine learning‐based approaches to this problem consider the content of only the abstract to obtain the neural representation of each sentence, which is then labelled with a discourse category. But this ignores the semantic information offered by the discourse labels themselves. In this paper, we propose LIHT, Label Informed Hierarchical Transformers – a method for sequential sentence classification that explicitly and hierarchically exploits the semantic information in the labels to learn label‐aware neural sentence representations. The hierarchical model helps to capture not only the fine‐grained interactions between the discourse labels and the words in the abstract at the sentence level but also the potential dependencies that may exist in the label sequence. Thus, LIHT generates label‐aware contextual sentence representations that are then labelled with a conditional random field. We evaluate LIHT on three publicly available datasets, namely, PUBMED‐RCT, NICTA‐PIBOSO and CSAbstract. The incremental gain in F1‐score in all the three cases over the respective state‐of‐the‐art approaches is around 1%. Though the gains are modest, LIHT establishes a new performance benchmark for this task and is a novel technique of independent interest. We also perform an ablation study to identify the contribution of each component of LIHT in the observed performance, and a case study to visualize the roles of the different components of our model. [ABSTRACT FROM AUTHOR]

With the advent of digital publishing and online databases, the volume of textual data generated by scientific research has increased exponentially. This makes it increasingly difficult for academics to keep up with new breakthroughs and synthesise important information for their own work. Abstracts have long been a standard feature of scientific papers, providing a concise summary of the paper’s content and main findings. In recent years, some journals have begun to provide research highlights as an additional summary of the paper. The aim of this article is to create research highlights automatically by using various sections of a research paper as input. We employ a pointer-generator network with a coverage mechanism and pretrained ELMo contextual embeddings to generate the highlights. Our experiments shows that the proposed model outperforms several competitive models in the literature in terms of ROUGE, METEOR, BERTScore, and MoverScore metrics. [ABSTRACT FROM AUTHOR]

Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work. [ABSTRACT FROM AUTHOR]

In today's networks, the widespread use of real-time traffic such as video and audio applications demand special service guarantee in terms of throughput, delay, and jitter, thus making quality of service (QoS) a key problem. IEEE 802.16 mesh networks are likely to be the basis of next-generation last mile network connectivity. So, providing QoS is one of the major designing goals in IEEE 802.16 mesh network. While the standard defines five service classes for PMP mode, no standard defined service classes exist for mesh mode. In this paper, we describe a differentiated service (DiffServ) architecture for QoS support in IEEE 802.16 mesh network by considering a basic requirement for QoS guarantee--delay. A new cross-layer routing metric is proposed, namely, expected scheduler delay (ESD). An efficient distributed scheme is proposed to calculate ESD and route the packets using source routing mechanism. This scheme is capable of differentiating between delay sensitive and best-effort traffic and route packets accordingly. Finally, the results of the proposed scheme are compared with the standard schemes that take hop count as a routing metric. [ABSTRACT FROM AUTHOR]

Topology‐transparent scheduling (TTS) for wireless networks involves generation of schedules which do not need update even when the network topology is constantly changing. TTS schemes designed with Steiner systems support the largest number of nodes—among all cover‐free families—for a given frame length. In this paper, we propose a TTS scheme using Steiner systems for a network in which nodes are divided into multiple classes of different priorities. Our proposed scheme called "Steiner system‐based Topology‐transparent Priority Scheduling" (STPS) produces priority‐sensitive schedules using different Steiner systems for the different priority classes. We explain our proposed method through illustrative examples and show its superiority over a competitive scheme in the literature with respect to the frame length. [ABSTRACT FROM AUTHOR]