Query: semantic ai

title: SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
abstract: Knowledge representation is an important, long history topic in AI, and there have been a large amount of work for knowledge graph embedding which projects symbolic entities and relations into low dimensional, real valued vector space. However, most embedding methods merely concentrate on data fitting and ignore the explicit semantic expression, leading to uninterpretable representations. Thus, traditional embedding methods have limited potentials for many applications such as question answering, and entity classification. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR) which imposes a two level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments justify our model outperforms other state of the art baselines substantially.

title: AI Challenger A Large scale Dataset for Going Deeper in Image Understanding
abstract: Significant progress has been achieved in Computer Vision by leveraging large scale image datasets. However, large scale datasets for complex Computer Vision tasks beyond classification are still limited. This paper proposed a large scale dataset named AIC (AI Challenger) with three sub datasets, human keypoint detection (HKD) large scale attribute dataset (LAD) and image Chinese captioning (ICC) In this dataset, we annotate class labels (LAD) keypoint coordinate (HKD) bounding box (HKD and LAD) attribute (LAD) and caption (ICC) These rich annotations bridge the semantic gap between low level images and high level concepts. The proposed dataset is an effective benchmark to evaluate and improve different computational methods. In addition, for related tasks, others can also use our dataset as a new resource to pre train their models.

title: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web
abstract: The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world changing. However, we argue that we are just at the beginning of this age of "social machines" and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.

title: SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
abstract: An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.

title: SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis
abstract: Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word level natural language data and the concept level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.

title: Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing
abstract: Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical cyber social (PCS) big data consist of this IoT data, complemented by relevant Web based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries. Over the next two years, this column on IoT will explore many challenges and technologies on intelligent use and applications of IoT data.

title: Measuring Semantic Similarity and Relatedness with Distributional and Knowledge based Approaches
abstract: This paper provides a survey of different techniques for measuring semantic similarity and relatedness of word pairs. This covers both knowledge based approaches exploiting taxonomies like WordNet, and corpus based approaches which rely on distributional statistics. We introduce these techniques, provide evaluations of their result performance, and discuss their merits and shortcomings. A special focus is on word embeddings, a new technique which recently became popular with the AI community. While word embeddings are not fully understood yet, they show promising results for similarity tasks, and may also be suitable for capturing significantly more complex features like relational similarity.

title: Episodic and semantic components of autobiographical memories and imagined future events in post traumatic stress disorder
abstract: Individuals with post traumatic stress disorder (PTSD) tend to retrieve autobiographical memories with less episodic specificity, referred to as overgeneralised autobiographical memory. In line with evidence that autobiographical memory overlaps with one's capacity to imagine the future, recent work has also shown that individuals with PTSD also imagine themselves in the future with less episodic specificity. To date most studies quantify episodic specificity by the presence of a distinct event. However, this method does not distinguish between the numbers of internal (episodic) and external (semantic) details, which can provide additional insights into remembering the past and imagining the future. This study employed the Autobiographical Interview (AI) coding scheme to the autobiographical memory and imagined future event narratives generated by combat veterans with and without PTSD. Responses were coded for the number of internal and external details. Compared to combat veterans without PTSD, those with PTSD generated more external than internal details when recalling past or imagining future events, and fewer internal details were associated with greater symptom severity. The potential mechanisms underlying these bidirectional deficits and clinical implications are discussed.

title: The PORSCE II framework: using AI planning for automated Semantic Web service composition
abstract: This paper presents PORSCE II, an integrated system that performs automatic Semantic Web service composition exploiting artificial intelligence (AI) techniques, specifically planning. Essential steps in achieving Web service composition include the translation of the Web service composition problem into a solver ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to Web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domain independent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available Web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation.

title: Using Word Embedding to Enable Semantic Queries in Relational Databases
abstract: We investigate opportunities for exploiting Artificial Intelligence (AI) techniques for enhancing capabilities of relational databases. In particular, we explore applications of Natural Language Processing (NLP) techniques to endow relational databases with capabilities that were very hard to realize in practice. We apply an unsupervised neural network based NLP idea, Distributed Representation via Word Embedding, to extract latent information from a relational table. The word embedding model is based on meaningful textual view of a relational database and captures inter /intra attribute relationships between database tokens. For each database token, the model includes a vector that encodes these contextual semantic relationships. These vectors enable processing a new class of SQL based business intelligence queries called cognitive intelligence (CI) queries that use the generated vectors to analyze contextual semantic relationships between database tokens. The cognitive capabilities enable complex queries such as semantic matching, reasoning queries such as analogies, predictive queries using entities not present in a database, and using knowledge from external sources.