Chandrasekaran B.,Josephson J.R., Benjamins V. R., (1999) What Are Ontologies, and Why Do We Need Them?

An Ontology model provides much the same information, except a data model is specifically related to data only. The data model provides entities that will become tables in a Relational Database Management System (RDBMS), and the attributes will become columns with specific data types and constraints, and the relationships will be identifying and nonidentifying foreign key constraints. What a data model does not provide is a machine-interpretable definition of the vocabulary in a specific domain. Data Models will not contain vocabulary that defines the entire domain, but rather the data dictionary will contain information on the entities and attributes associated with a specific data element. This is where ontologies come in.

Peer review is the process by which experts in some discipline comment on the quality of the works of others in that discipline. Peer review of written works is firmly embedded in current academic research practice where it is positioned as the gateway process and quality control mechanism for submissions to conferences, journals, and funding bodies across a wide range of disciplines. It is probably safe to assume that peer review in some form will remain a cornerstone of academic practice for years to come, evidence-based criticisms of this process in computer science22,32,45 and other disciplines23,28 notwithstanding. While parts of the academic peer review process have been streamlined in the last few decades to take technological advances into account, there are many more opportunities for computational support that are not currently being exploited. The aim of this article is to identify such opportunities and describe a few early solutions for automating key stages in the established academic peer review process. When developing these solutions we have found it useful to build on our background in machine learning and artificial intelligence: in particular, we utilize a feature-based perspective in which the handcrafted features on which conventional peer review usually depends (for example, keywords) can be improved by feature weighting, selection, and construction (see Flach17 for a broader perspective on the role and importance of features in machine learning). Twenty-five years ago, at the start of our academic careers, submitting a paper to a conference was a fairly involved and time-consuming process that roughly went as follows: Once an author had produced the manuscript (in the original sense, that is, manually produced on a typewriter, possibly by someone from the university's pool of typists), he or she would make up to seven photocopies, stick all of them in a large envelope, and send them to the program chair of the conference, taking into account that international mail would take 3–5 days to arrive. On their end, the program chair would receive all those envelopes, allocate the papers to the various members of the program committee, and send them out for review by mail in another batch of big envelopes. Reviews would be completed by hand on paper and mailed back or brought to the program committee meeting. Finally, notifications and reviews would be sent back by the program chair to the authors by mail. Submissions to journals would follow a very similar process.

Rudolph 2012; Lutz, Seylan, and Wolter 2012), ontology Logics allow equivalent facts to be expressed in many different learning (Konev, Ozaki, and Wolter 2016; Lehmann and ways. The fact that ontologies are developed by a Hitzler 2010), rewriting ontologies into less expressive logics number of different people and grow over time can lead to (Carral et al. 2014; Lutz, Piro, and Wolter 2011), concepts that are more complex than necessary. For example, abduction (Du, Wang, and Shen 2015; Klarman, Endriss, below is a simplified definition of the medical concept and Schlobach 2011), and knowledge revision (Grau, Kharlamov, Clotting from the Galen ontology (Rector et al. 1994): and Zheleznyakov 2012; Qi, Liu, and Bell 2006).

Structured knowledge about concepts plays an increasingly important role in areas such as information retrieval. The available ontologies and knowledge graphs that encode such conceptual knowledge, however, are inevitably incomplete. This observation has led to a number of methods that aim to automatically complete existing knowledge bases. Unfortunately, most existing approaches rely on black box models, e.g. formulated as global optimization problems, which makes it difficult to support the underlying reasoning process with intuitive explanations. In this paper, we propose a new method for knowledge base completion, which uses interpretable conceptual space representations and an explicit model for inductive inference that is closer to human forms of commonsense reasoning. Moreover, by separating the task of representation learning from inductive reasoning, our method is easier to apply in a wider variety of contexts. Finally, unlike optimization based approaches, our method can naturally be applied in settings where various logical constraints between the extensions of concepts need to be taken into account.

To ensure completeness Description Logics (DLs) are popular languages for knowledge of the method, the so-called refinement step is used that recomputes representation and reasoning. They are the underlying the abstraction based on new (sound) entailments formalism for the standardized Web Ontology Language obtained from a previous abstraction. This has the added OWL, which is widely used in many application areas. Recent benefit that not only consistency but also the full materialization years have also seen an increasing interest in ontologybased of the ABox can be computed without (rather expensive) data access, where a TBox with background knowledge, explanation computations or repeated consistency often expressed in a DL language, is used to enrich checks. This paper significantly advances the abstraction refinement datasets (ABoxes), which are then accessible via queries.

We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Nonrecursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets of up to 11GB.

Scientific data is continuously generated throughout the world. However, analyses of these data are typically performed exactly once and on a small fragment of recently generated data. Ideally, data analysis would be a continuous process that uses all the data available at the time, and would be automatically re-run and updated when new data appears. We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available. Novel contributions of this approach include a framework to trigger new analyses appropriate for the available data through lines of inquiry that support progressive hypothesis evolution, and a representation of hypothesis revisions with provenance records that can be used to inspect the results. We implemented our approach in the DISK framework, and evaluated it using two scenarios from cancer multi-omics: 1) data for new patients becomes available over time, 2) new types of data for the same patients are released. We show that in all scenarios DISK updates the confidence on the original hypotheses as it automatically analyzes new data.

Probabilistic databases (PDBs) are usually incomplete, e.g., containing only the facts that have been extracted from the Web with high confidence. However, missing facts are often treated as being false, which leads to unintuitive results when querying PDBs. Recently, open-world probabilistic databases (OpenPDBs) were proposed to address this issue by allowing probabilities of unknown facts to take any value from a fixed probability interval. In this paper, we extend OpenPDBs by Datalog+/- ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NP^PP-complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints.

Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.