Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.

Who's doing this? 75% of the Fortune 500 companies have some kind of smart data or semantics program underway, most under the banner of 360 initiatives, comprehensive enterprise data systems, or machine learning/data science projects. Amazon has recently added linked data capabilities to their AWS infrastructure with the Neptune project, and social media giants have built their entire data infrastructure around smart ontological data. Moreover, China, Japan, England, the OECD, and the United States have all moved critical data resources into semantic form, and semantics has become one of the hottest areas for investment banks such as Wells Fargo, Morgan Stanley, Citigroup, Goldman Sachs and others. It even ties into such cutting edge technologies as Blockchain and the Internet of Things.

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.

No matter what language or techniques are being applied, there are enough similarities between data science approaches that some broad parallels can be drawn. Independent of language and model specifics, generalizations can be teased out of data science methods to provide a reference point for the many ways to solve similar problems. Before tackling a complex data science problem developers often check GitHub and other repositories for ideas or snippets to avoid recreating wheels. However, according to IBM researcher Ioana Baldini much can be overlooked when casting such a wide net. The key is to build an ontology of data science methodologies, tie those to real code, and connect the dots via annotations and other code information for many problem sets that are not language or model specific.

Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we develop an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. We focus on code written for data science, although our method is more generally applicable. The semantic representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. This algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching.

IBM Almaden Research Center, 650 Harry Road, San Jose, California 95136 Abstract We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database of compoundtarget associations augmented with molecular ontological labels. It also contains two computational tools for prediction of new associations. We describe two drug-repurposing systems: one, Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based on an information retrieval strategy, and another, based on nonnegative matrix factorization together with compound similarity, that was inspired by recommender systems. We report the performance of both tools on a drug-repurposing task. 1 Introduction Drug repurposing is an efficient strategy for drug discovery, where new targets or activities are found for known drugs [1-5]. Drug repurposing requires the efficient representation of existing information about the activity of chemical compounds as drugs, and the development of algorithms that leverage such information and propose new indications.

The problem of vocabulary gap among students in the early years of school, and the resulting impact on school success have received significant attention in the past [3-5, 18, 20, 23]. Early introduction of vocabulary through either direct or indirect instruction helps children learn to read well and forms a strong foundation for literacy, which in turn helps children in accelerated reading to learn. Importantly, while reading new texts, children tend to connect the words they are familiar with to the words exposed in the texts; hence, greater and diverse vocabulary leads to better comprehension of the texts being read. Given the enormity of vocabulary in English language (and most languages in general), new word acquisition is an ongoing process for many years, and 1 sometimes is even lifelong for many people. However, the highest rate of vocabulary development happens in the early years, and teachers in elementary schools focus (often in their own ways, since no universally standardized word lists or procedures exist) a nontrivial amount of time in introducing words to children through both direct and implicit instruction.

Reasoning about exceptions in ontologies is nowadays one of the challenges the description logics community is facing. The paper describes a preferential approach for dealing with exceptions in Description Logics, based on the rational closure. The rational closure has the merit of providing a simple and efficient approach for reasoning with exceptions, but it does not allow independent handling of the inheritance of different defeasible properties of concepts. In this work we outline a possible solution to this problem by introducing a variant of the lexicographical closure, that we call skeptical closure, which requires to construct a single base. We develop a bi-preference semantics semantics for defining a characterization of the skeptical closure.

We survey the AI research carried out in Greece recently. A milestone for AI research in Greece came in 1988, when the Hellenic Artificial Intelligence Society (EETN) was founded as a nonprofit scientific organization devoted to organizing and promoting AI research in Greece and abroad. EETN is an affiliated society of the European Association for Artificial Intelligence (EurAI, formerly known as ECCAI). One of the many roles of EETN is the organization of conferences, workshops, summer schools, and other events, such as the Hellenic Conference on Artificial Intelligence (SETN). The first SETN was Science with a team well grounded in KR.