In natural language processing and information retrieval, the bag of words representation is used to implicitly represent the meaning of the text. Implicit semantics, however, are insufficient in supporting text or natural language based interfaces, which are adopted by an increasing number of applications. Indeed, in applications ranging from automatic ontology construction to question answering, explicit representation of semantics is starting to play a more prominent role. In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words. We draw the labels from a data driven semantic network that contains millions of highly connected concepts. The semantic network provides meaning to the concepts, and in turn, it provides meaning to the bag of words through the conceptual labels we generate. To achieve our goal, we use an information theoretic approach to trade-off the semantic coverage of a bag of words against the minimality of the output labels. Specifically, we use Minimum Description Length (MDL) as the criteria in selecting the best concepts. Our extensive experimental results demonstrate the effectiveness of our approach in representing the explicit semantics of a bag of words.

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.

Bounded-memory computability continues to be in the focus of those areas of AI and databases that deal with feasible computations over streams be it feasible arithmetical calculations on low-level streams or feasible query answering for declaratively specified queries on relational data streams or even feasible query answering for high-level queries on streams w.r.t. a set of constraints in an ontology such as in the paradigm of Ontology-Based Data Access (OBDA). In classical OBDA, a high-level query is answered by transforming it into a query on data source level. The transformation requires a rewriting step, where knowledge from an ontology is incorporated into the query, followed by an unfolding step with respect to a set of mappings. Given an OBDA setting it is very difficult to decide, whether and how a query can be answered efficiently. In particular it is difficult to decide whether a query can be answered in bounded memory, i.e., in constant space w.r.t. an infinitely growing prefix of a data stream.

We focus on handling conflicting and uncertain information in lightweight ontologies, where uncertainty is represented in a possibilistic logic setting. We use DL-Lite, a tractable fragment of Description Logic, to specify terminological knowledge (i.e., TBox). We assume the TBox to be stable and coherent, while its combination with a set of assertional facts (i.e., ABox) may be inconsistent. We address the problem of dealing with conflicts when the reliability relation between sources is only partially ordered. We propose to represent the uncertain ABox as a symbolic weighted base, where a strict partial preorder is applied on the weights.

There is a growing need for scalable semantic web repositories which support inference and provide efficient queries. There is also a growing interest in representing uncertain knowledge in semantic web datasets and ontologies. In this paper, I present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. I propose a system for materializing and storing inferred knowledge using this schema. I show experimental results that demonstrate that this solution simplifies inference queries and drastically improves results. I also propose and describe a solution for materializing and persisting uncertain information and probabilities. Thresholds and bit vectors are used to provide efficient query access to this uncertain knowledge. My goal is to provide a semantic web repository that supports knowledge inference, uncertainty reasoning, and Bayesian networks, without sacrificing performance or scalability.

We describe an approach to reducing the computational cost of identifying coreferent instances in heterogeneous semantic graphs where the underlying ontologies may not be informative or even known. The problem is similar to coreference resolution in unstructured text, where a variety of linguistic clues and contextual information is used to infer entity types and predict coreference. Semantic graphs, whether in RDF or another formalism, are semi-structured data with very different contextual clues and need different approaches to identify potentially coreferent entities. When their ontologies are unknown, inaccessible or semantically trivial, coreference resolution is difficult. For such cases, we can use supervised machine learning to map entity attributes via dictionaries based on properties from an appropriate background knowledge base to predict instance entity types, aiding coreference resolution. We evaluated the approach in experiments on data from Wikipedia, Freebase and Arnetminer and DBpedia as the background knowledge base.

The success of the Web of Data (WOD) is based on the thorough understanding of, and agreement upon, the se- mantics of data and ontologies. But the Web of Data as a whole is complex, and inherently messy, contex- tualised, opinionated, in short: it is a market-place of ideas, rather than a database. Existing paradigms are in- appropriate for dealing with this new type of knowledge structures. The urgency of dealing with the non-standard charac- teristics of the Web of Data has been recognised, and separate initiatives try to tackle its individual manifes- tations, e.g.

When the amount of RDF data is very large, it becomes more likely that the triples describing entities will contain errors and may not include the specification of a class from a known ontology. The work presented here explores the utilization of methods from machine learning to develop classifiers for identifying the semantic categorization of entities based upon the property names used to describe the entity. The goal is to develop classifiers that are accurate, but robust to errors and noise. The training data comes from DBpedia, where entities are categorized by type and densely described with RDF properties. The initial experimentation reported here indicates that the approach is promising.

Much of the focus on big data has been on the problem of processing very large sources. There is an equally hard problem of how to normalize, integrate, and transform the data from many sources into the format required to run large-scale analysis and visualization tools. We have previously developed an approach to semi-automatically mapping diverse sources into a shared domain ontology so that they can be quickly combined. In this paper we describe our approach to building and executing integration and restructuring plans to support analysis and visualization tools on very large and diverse datasets.

We present an implemented approach to transform natural language sentences into SPARQL, using background knowledge from ontologies and lexicons. Therefore, eligible technologies and data storage possibilities are analyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required.