Most of the existing techniques to product discovery rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats giving rise to the problem of interoperability. Above all, due to the continuously increasing influx of data, the manual labeling is getting costlier. Integrating the descriptions of different products into a single representation requires organizing all the products across vendors in a single taxonomy. Practically relevant and quality product categorization standards are still limited in number; and that too in academic research projects where we can majorly see only prototypes as compared to industry. This work presents a cost-effective solution for e-commerce on the Data Web by employing an unsupervised approach for data classification and exploiting the knowledge graphs for matching. The proposed architecture describes available products in web ontology language OWL and stores them in a triple store. User input specifications for certain products are matched against the available product categories to generate a knowledge graph. This mullti-phased top-down approach to develop and improve existing, if any, tailored product recommendations will be able to connect users with the exact product/service of their choice.

Various model-based diagnosis scenarios require the computation of most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, we propose two novel diagnostic search algorithms, called RBF-HS (Recursive Best-First Hitting Set Search) and HBF-HS (Hybrid Best-First Hitting Set Search), which build upon tried and tested techniques from the heuristic search domain. RBF-HS can enumerate an arbitrary predefined finite number of fault explanations in best-first order within linear space bounds, without sacrificing the desirable soundness or completeness properties. The idea of HBF-HS is to find a trade-off between runtime optimization and a restricted space consumption that does not exceed the available memory. In extensive experiments on real-world diagnosis cases we compared our approaches to Reiter's HS-Tree, a state-of-the-art method that gives the same theoretical guarantees and is as general(ly applicable) as the suggested algorithms. For the computation of minimum-cardinality fault explanations, we find that (1) RBF-HS reduces memory requirements substantially in most cases by up to several orders of magnitude, (2) in more than a third of the cases, both memory savings and runtime savings are achieved, and (3) given the runtime overhead is significant, using HBF-HS instead of RBF-HS reduces the runtime to values comparable with HS-Tree while keeping the used memory reasonably bounded. When computing most probable fault explanations, we observe that RBF-HS tends to trade memory savings more or less one-to-one for runtime overheads. Again, HBF-HS proves to be a reasonable remedy to cut down the runtime while complying with practicable memory bounds.

In this article, we develop a framework for comparing ontologies and place a number of the more prominent ontologies into it. We have selected 10 specific projects for this study, including general ontologies, domain-specific ones, and one knowledge representation system. The comparison framework includes general characteristics, such as the purpose of an ontology, its coverage (general or domain specific), its size, and the formalism used. It also includes the design process used in creating an ontology and the methods used to evaluate it. Characteristics that describe the content of an ontology include taxonomic organization, types of concept covered, top-level divisions, internal structure of concepts, representation of part-whole relations, and the presence and nature of additional axioms.

Research on semantic web services promises greater interoperability among software agents and web services by enabling content-based automated service discovery and interaction and by utilizing . Although this is to be based on use of shared ontologies published on the semantic web, services produced and described by different developers may well use different, perhaps partly overlapping, sets of ontologies. Interoperability will depend on ontology mappings and architectures supporting the associated translation processes. The question we ask is, does the traditional approach of introducing mediator agents to translate messages between requestors and services work in such an open environment? This article reviews some of the processing assumptions that were made in the development of the semantic web service modeling ontology OWL-S and argues that, as a practical matter, the translation function cannot always be isolated in mediators.

By extending Cyc's ontology and KB approximately 2%, Cycorp and Cleveland Clinic Foundation (CCF) have built a system to answer clinical researchers' ad hoc queries. The query may be long and complex, hence only partially understood at first, parsed into a set of CycL (higher-order logic) fragments with open variables. But, surprisingly often, after applying various constraints (medical domain knowledge, common sense, discourse pragmatics, syntax), there is only one single way to fit those fragments together, one semantically meaningful formal query P. The system, SRA (for Semantic Research Assistant), dispatches a series of database calls and then combines, logically and arithmetically, their results into answers to P. Seeing the first few answers stream back, the user may realize that they need to abort, modify, and re-ask their query. Even before they push ASK, just knowing approximately how many answers would be returned can spark such editing. Besides real-time ad hoc query-answering, queries can be bundled and persist over time. One bundle of 275 queries is rerun quarterly by CCF to produce the procedures and outcomes data it needs to report to STS (Society of Thoracic Surgeons, an external hospital accreditation and ranking body); another bundle covers ACC (American College of Cardiology) reporting.

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses.

While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis.

Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question Answering (VQA). However, existing VQA models represent relationships as a combination of object-level visual features which constrain a model to express interactions between objects in a single domain, while the model is trying to solve a multi-modal task. In this paper, we propose a general purpose semantic relationship parser which generates a semantic feature vector for each subject-predicate-object triplet in an image, and a Mutual and Self Attention (MSA) mechanism that learns to identify relationship triplets that are important to answer the given question. To motivate the significance of semantic relationships, we show an oracle setting with ground-truth relationship triplets, where our model achieves a ~25% accuracy gain over the closest state-of-the-art model on the challenging GQA dataset. Further, with our semantic parser, we show that our model outperforms other comparable approaches on VQA and GQA datasets.

The rapid advancement of Artificial intelligence and its branches like machine learning, deep learning, which function on extracting relevant information and generating insights from data to find sustainable and decisive solutions, is nothing new. But to run these algorithms, organizations need data and code. To translate this necessity into something meaningful, we need data science. While this discipline proliferates into an exciting and diverse technology that incorporates a mixture of deep specialization and broad applications, we also realize the value it brings to the table. Further, data science helps organizations communicate with stakeholders, customers, track and analyze trends, and determine if the collected data is actually of any help or simply a waste of a database farm.

Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly used in critical, high-stakes settings such as healthcare. Explanations have often added to an AI system in a non-principled, post-hoc manner. With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities. We design an explanation ontology to model both the role of explanations, accounting for the system and user attributes in the process, and the range of different literature-derived explanation types. We indicate how the ontology can support user requirements for explanations in the domain of healthcare. We evaluate our ontology with a set of competency questions geared towards a system designer who might use our ontology to decide which explanation types to include, given a combination of users' needs and a system's capabilities, both in system design settings and in real-time operations. Through the use of this ontology, system designers will be able to make informed choices on which explanations AI systems can and should provide.