This book constitutes the thoroughly refereed papers of the 8th Chinese Conference on The Semantic Web and Web Science, CSWS 2014, held in Wuhan, China, in August 2014. The 22 research papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in topical sections such as ontology reasoning and learning; semantic data generation and management; and semantic technology and applications.
COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID-19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering (QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 Knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge (i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset (called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph OpenKG-COVID19 launched by OpenKG①, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop data while obtaining the best result with significant improvements in the 3-hop. And it is more efficient to be used in the QA system for users. Moreover, the user study shows that the system not only provides accurate and interpretable answers but also is easy to use and comes with smart tips and suggestions.
Recently more and more structured data in the form of RDF triples have been published and integrated into Linked Open Data (LOD). While the current LOD contains hundreds of data sources with billions of triples, it has a small number of distinct relations compared...
Medical Visual Question Answering (Med-VQA) is a task in the field of Artificial Intelligence where a medical image is given with a related question, and the task is to provide an accurate answer to the question. It involves the integration of computer vision,...
Online advertising represents a growing part of the revenues of major Internet service providers such as Google and Yahoo. A commonly used strategy is to place advertisements (ads) on the search result pages according to the users' submitted queries. Relevant ads are likely to be clicked by a user and to increase the revenues of both advertisers and publishers. However, bid phrases defined by ad-owners are usually contained in limited number of ads. Directly matching user queries with bid phrases often results in finding few appropriate ads. To address this shortcoming, query expansion is often used to increase the chances to match the ads. Nevertheless, query expansion on top of the traditional inverted index faces efficiency issues such as high time complexity and heavy I/O costs. Moreover, precision cannot always be improved, sometimes even hurt due to the involvement of additional noise. In this paper, we propose an efficient ad search solution relying on a block-based index able to tackle the issues associated with query expansion. Our index structure places clusters of similar bid phrases in corresponding blocks with their associated ads. It reduces the number of merge operations significantly during query expansion and allows sequential scans rather than random accesses, saving I/O costs. We adopt flexible block sizes according to the clustering results of bid phrases to further optimize the index structure for efficient ad search. The pre-computation of such clusters is achieved through an agglomerative iterative clustering algorithm. Finally, we adapt the spreading activation mechanism to return the top-k relevant ads, improving search precision. The experimental results of our prototype, AdSearch, show that we can indeed return a larger number of relevant ads without sacrificing execution speed.
In this paper, we are the first to construct a software programming taxonomy from Stackoverflow. More precisely, we propose a machine learning based method with novel features to capture the hierarchical semantic structure of tags in Stackoverflow. A graph pruning algorithm is applied to eliminate the conflicts by constructing a Directed Acyclic Graph (DAG). As a result, our dataset, named Software.zhishi.schema, contains 38,205 concepts together with 36,249 subsumption relations. In order to further test the usability of our published data, we adopt a similarity computing task of words from software programming which is one of the most fundamental tasks in the software repository mining area. The results show that our dataset can outperform other knowledge bases due to its high coverage with finergrained domain concepts.
Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.
Disease prediction, which aims to predict possible future diseases for patients, is a fundamental research problem in medical informatics. Many studies have proposed the introduction of external knowledge to enhance deep learning models which have achieved good results, but as most of these studies only consider using single-hop relationship information from knowledge graphs or simply introduce partial knowledge graphs, fail to effectively mine knowledge paths to understand the relationships between diseases. To this end, we propose a new approach, which uses existing medical knowledge graphs for multi-hop reasoning to guide the self-attention based transformer model for disease prediction. Specifically, we design a reinforcement learning algorithm to perform path reasoning in the knowledge graph to obtain explicit disease progression paths and fusion them with the original Electronic Health Records (EHR) data. After embedding we capture the implicit relationships between the deep knowledge and the original EHR information through several transformer encoders based on the self-attention mechanism to better extract features. At the same time, multi-hop knowledge is more interpretable than single-hop knowledge in terms of disease prediction. Experimental results on the real-world medical dataset MIMIC-III show the superiority of the proposed approach compared to a series of state-of-the-art baselines. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code has been uploaded to https://github.com/15536385781/DKRDP
The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD * semantics and has shown promising results. In this paper, we move a step forward to consider...
Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search and question answering. However, most of the knowledge graph construction works focus on organizing and discovering textual knowledge in a structured representation, while paying little attention to the proliferation of visual resources on the Web. To consolidate this recent trend, in this paper, we present Richpedia, aiming to provide a comprehensive multi-modal knowledge graph by distributing sufficient and diverse images to textual entities in Wikidata. We also set Resource Description Framework links (visual semantic relations) between image entities based on the hyperlinks and descriptions in Wikipedia. The Richpedia resource is accessible on the Web via a faceted query endpoint, which provides a pathway for knowledge graph and computer vision tasks, such as link prediction and visual relation detection.
