We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with handcrafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.

We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with two of the world's largest ontologies.

Representing computational activities is still an open problem in the field of Activity-Aware Computing. In this paper, drawn from our experiences in developing activity-aware applications in support of two populations: nurses working in hospitals and elders living independently; we defined the Context Aware Representational (CARe) model. CARe is an ontology that enables the representation and management of computational activities. We illustrate, through application scenarios, that the CARe ontology is flexible enough to enable developers to c

Ontologies are not only becoming a widespread formalism to create the knowledge base of current intelligent and semantic systems, but they are also suitable for modeling context information in ubiquitous applications, which require expressive representation and reasoning languages. In this paper, we discuss different approaches for ontological context management, as well as a proposal to represent and exploit significance-based relations with standard and fuzzy ontologies.

We build a mechanism to form an ontology of entities which improves a relevance of matching and searching microtext. Ontology construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Ontology and syntactic generalization are applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources to microtext processing.

We describe work on representing and using a rich notion ofcontext that goes beyond current networking applications focusingmostly on location. Our context model includes locationand surroundings, the presence of people and devices,inferred activities and the roles people fill in them. A keyelement of our work is the use of collaborative informationsharing where devices share and integrate knowledge abouttheir context. This introduces a requirement that users canset appropriate levels of privacy to protect the personal informationbeing collected and the inferences that can be drawnfrom it. We use Semantic Web technologies to model contextand to specify high-level, declarative policies specifying informationsharing constraints. The policies involve attributesof the subject (i.e., information recipient), target (i.e., the information)and their dynamic context (e.g., are the parties copresent).We discuss our ongoing work on context representationand inference and present a model for protecting andcontrolling the sharing of private data in context-aware mobileapplications.

Mobile applications can be greatly enhanced if they have information about the situation of the user. Situations may be inferred by analyzing several types of contextual information drawn from device sensors, such as location, motion, ambiance and proximity. To capture a richer understanding of users’ situations, we introduce an ontology describing the relations between background knowledge about the user and contexts inferred from sensor data. With the right combination of machine learning and semantic modeling, it is possible to create high-level interpretations of user behaviors and situations. However, the potential of understanding and interpreting behavior with such detailed granularity poses significant threats to personal privacy. We propose a framework to mitigate privacy risks by filtering sensitive data in a context-aware way, and maintain provenance of inferred situations as well as relations between existing contexts when sharing information with other parties.

This paper introduces 'just enough' principles and 'systems engineering' approach to the practice of ontology development to provide a minimal yet complete, lightweight, agile and integrated development process, supportive of stakeholder management and implementation independence.

For large scale enterprise implementations, a key problem, that has not been tackled much, is the ability to automatically map users’ requirements to reference process models. We present a tool called Process Model Requirements Gap Analyzer (ProcGap), which uses a combination of natural language processing, information retrieval and semantic reasoning to automatically match and map textual requirements to industry-specific process models. We present the results of mapping requirements from an industry project to an existing process model. We compare our approach to two previously implemented approaches and show that our approach outperforms them. In a case study, we also found that a user group with ProcGap had better performance than a user group that performed the same task manually.

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. Recently, the Datalog+/- family of ontology languages was introduced; in Datalog+/-, rules are tuple-generating dependencies (TGDs), i.e., Datalog rules with the possibility of having existentially-quantified variables in the head. In this paper we introduce a novel Datalog+/- language, namely sticky sets of TGDs, which allows for a wide class of joins in the body, while enjoying at the same time a low query-answering complexity. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show some extensions of sticky sets of TGDs, and how functional dependencies and so-called negative constraints can be added to a sticky set of TGDs without increasing the complexity of query answering. Our language thus properly generalizes both classical database constraints and most widespread tractable description logics.