Camilo Thorne

Aggregate queries over ontologies

Answering queries over ontologies is an important issue for the Semantic Web. Aggregate queries were widely studied for relational databases but almost no results are known for aggregate queries over ontologies. In this work we investigate the latter problem. We propose syntax and semantics for epistemic aggregate queries over ontologies and study query answering for MAX, MIN, COUNT, CNTD, SUM, AVG queries for the ontology language DL-LiteA.

Process Fragment Recognition in Clinical Documents

We describe a first experiment on automated activity and relation identification, and more in general, on the automated identification and extraction of computer-interpretable guideline fragments from clinical documents. We rely on clinical entity and relation (activities, actors, artifacts and their relations) recognition techniques and use MetaMap and the UMLS Metathesaurus to provide lexical information. In particular, we study the impact of clinical document syntax and semantics on the precision of activity and temporal relation recognition.

The data complexity of the syllogistic fragments of English

Pratt and Thirds syllogistic fragments of English can be used to capture, in addition to syllogistic reasoning, many other kinds of common sense reasoning, and, in particular (i) knowledge base consistency and (ii) knowledge base query answering, modulo their FO semantic representations. We show how difficult, in terms of semantic (computational) complexity and data complexity (i.e., computational complexity w.r.t. the number of instances declared in a knowledge base), such reasoning problems are. In doing so, we pinpoint also those fragments for which the reasoning problems are tractable (in PTime) or intractable (NP-hard or coNP-hard).

English Querying over Ontologies: E-QuOnto

Relational database (DB) management systems provide the standard means for structuring and querying large amounts of data. However, to access such data the exact structure of the DB must be know, and such a structure might be far from the conceptualization of a human being of the stored information. Ontologies help to bridge this gap, by providing a high level conceptual view of the information stored in a DB in a cognitively more natural way. Even in this setting, casual end users might not be familiar with the formal languages required to query ontologies. In this paper we address this issue and study the problem of ontology-based data access by means of natural language questions instead of queries expressed in some formal language. Specifically, we analyze how complex real life questions are and how far from the query languages accepted by ontology-based data access systems, how we can obtain the formal query representing a given natural language question, and how can we handle those questions which are too complex wrt the accepted query language.

Designing Efficient Controlled Languages for Ontologies

We describe a methodology to recognize efficient controlled natural languages (CLs) that compositionally translate into ontology languages, and as such are suitable to be used in natural language front-ends to ontology-based systems. Efficiency in this setting is defined as the tractability (in the sense of computational complexity theory) of logical reasoning in such fragments, measured in the size of the data they aim to manage. In particular, to identify efficient CLs, we consider fragments corresponding to the DL-Lite family of description logics, known to underpin data intensive ontologies and systems. Our methodology exploits the link between syntax and semantics of natural language captured by categorial grammars, controlling the use of lexical terms that introduce logical structure outside the allowed fragments. A major role is played by the control of function words introducing logical operators in first-order formal semantics meaning representations. Finally, we conduct a preliminary analysis of semantically parsed English written corpora to show how empirical methods may be useful in identifying CLs that provide good trade-offs between coverage and efficiency.

Controlled Aggregate Tree Shaped Questions over Ontologies

Controlled languages (CLs) are ambiguity-free subsets of natural languages such as English offering a good trade-off between the formal rigor of ontology and query languages and the intuitive appeal of natural language. They compositionally map (modulo a compositional translation *** (·)) into (or express ) formal query languages and ontology languages. Modulo compositionality, they inherit the computational properties of such ontology/query languages. In the setting of OBDAS, we are interested in capturing query answering and measuring computational complexity w.r.t. the data queried (a.k.a. data complexity ). In this paper we focus in defining a CL capable of expressing a subset SQL aggregate queries , and study its data complexity w.r.t. several ontology languages and extensions of the query language.

Controlled English ontology-based data access

As it is well-known, querying and managing structured data in natural language is a challenging task due to its ambiguity (syntactic and semantic) and its expressiveness. On the other hand, querying, e.g., a relational database or an ontology-based data access system is a well-defined and unambigous task, namely, the task of evaluating a formal query (e.g., an SQL query) of a limited expressiveness over such database. However these formal query languages may be difficult to learn and use for the casual user and ambiguity may compromise the interface. To bridge this gap, the use of controlled language interfaces has been proposed. As a measure of their efficiency for data access, we propose to consider data complexity, which is the complexity of query evaluation measured in the size of the data. We study a familiy of controlled languages that express several fragments of OWL, ranging from tractable (LogSpace and PTime) to intractable (coNP-hard) in data complexity, singling out which constructs give rise to each computational property.