Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?

Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs

heterogeneity of large cell carcinoma of the lung an immunophenotypic and mirna based analysis

Large cell carcinomas (LCCs) of the lung are heterogeneous and may be of different cell lineages. We analyzed 56 surgically resected lung tumors classified as LCC on the basis of pure morphologic grounds, using a panel of immunophenotypic markers (adenocarcinoma [ADC]-specific, thyroid transcription factor-1, cytokeratin 7, and napsin A; squamous cell carcinoma [SQCC]–specific, p63, cytokeratin 5, desmocollin 3, and Δnp63) and the quantitative analysis of microRNA-205 (microRNA sample score [mRSS]). Based on immunoprofiles 19 (34%) of the cases were reclassified as ADC and 14 (25%) as SQCC; 23 (41%) of the cases were unclassifiable. Of these 23 cases, 18 were classified as ADC and 5 as SQCC according to the mRSS. Our data show that an extended panel of immunohistochemical markers can reclassify around 60% of LCCs as ADC or SQCC. However, a relevant percentage of LCCs may escape convincing immunohistochemical classification, and mRSS could be used for further typing, but its clinical relevance needs further confirmation. Large cell carcinoma (LCC) of the lung is 1 of 4 major histopathologic tumor subtypes recognized by current classifications of lung tumors. However, although squamous cell carcinoma (SQCC), adenocarcinoma (ADC), and small cell carcinoma are well-defined entities with typical morphologic, immunophenotypic, and molecular features, LCCs, with the exception of the rare neuroendocrine, rhabdoid, basaloid, and lymphoepithelioma-like subtypes, are defined as poorly differentiated non–small cell tumors lacking features of ADC and SQCC. Therefore, the term LCC has frequently and improperly been used as a synonym of undifferentiated non–small cell lung carcinoma (NSCLC) and has been used as a "wastebasket" for tumors lacking a definite morphologic pattern. Studies show that, by using ancillary techniques, a relevant percentage of LCCs could be reclassified as SQCC or ADC. Gene profiling shows that most LCCs have profiles quite similar to ADC or SQCC. 1-3 Similarly, by using appropriate immunohistochemical stains, almost two thirds of LCCs can be reclassified as poorly differentiated ADC or SQCC. 4,5 These studies have profound clinical relevance because rendering a diagnosis of LCC may represent a challenge for oncologists who need accurate subtyping of lung cancers to provide patients with optimal targeted chemotherapeutic agents, showing different efficacy with specific NSCLC categories (usually effective for ADC and not for others). 6,

Topicality in logic-based ontologies

Abstract In this paper we examine several forms of modularity in logics as a basis for various conceptions of the topical structure of an ontology. Intuitively, a topic is a coherent fragment of the subject matter of the ontology. Different topics may play different roles: e.g., the main topic (or topics), side topics, or subtopics. If, at the lowest level, the subject matter of an ontology is characterized by the set of concepts of the on-tology, a topic is a “coherent ” subset of those concepts. Different forms of modularity induce different, more or less cognitively helpful, notions of coherence and thus distinct topical structures.

Logical Relevance in Ontologies

Abstract Most ontology development environments (ODEs) are term oriented and take a frame-based view of the information in an ontology about a given term. Even tools, such as Protégé 4, designed for axiom oriented development preserve the frame-based view as the central mode of interaction with the ontology. The frame-based approach has a number of advantages—most prominently that it is comfortable to people familiar with object oriented programming languages. However, in expressive languages the frame-based views suffer from being only sensitive to syntactic relations between axioms and terms, thus possibly missing key logical relations. In this paper, we first introduce a semantic notion of relevance between a term and axioms in an ontology, and we investigate the relation of this concept with the inseparability relation based on model Conservative Extensions. Unfortunately, we cannot use model conservativity to detect relevance since it is hard, or even impossible, to decide. Hence, we approximate model conservativity using two notions of modules based on locality, that can be efficiently computed, and provide logical guarantees, e.g. they preserve entailments over a given signature. In particular, we define relevance via Atomic Decomposition, that is a dependency graph showing the logical relations enforced by the two notions of modules between the axioms. We define a suitable labelling that allows us to locate axioms that are relevant for a term in the AD dependency structure. Finally, we describe an interesting consequence of such a view in terms of the models of an ontology.