Browsing for information by highlighting automatically generated annotations:a user study and evaluation

The realization of the Semantic Web is constrained by a knowledge acquisition bottleneck, i.e. the problem of how to add RDF mark-up to the millions of ordinary web pages that already exist. Information Extraction (IE) has been proposed as a solution to the annotation bottleneck. In the task based evaluation reported here, we compared the performance of users without access to annotation, users working with annotations which had been produced from manually constructed knowledge bases, and users working with annotations augmented using IE. We looked at retrieval performance, overlap between retrieved items and the two sets of annotations, and usage of annotation options. Automatically generated annotations were found to add value to the browsing experience in the scenario investigated. Copyright 2005 ACM.

Semantic annotation support in the absence of consensus

We are interested in the annotation of knowledge which does not necessarily require a consensus. Scholarly debate is an example of such a category of knowledge where disagreement and contest are widespread and desirable, and unlike many Semantic Web approaches, we are interested in the capture and the compilation of these conflicting viewpoints and perspectives. The Scholarly Ontologies project provides the underlying formalism to represent this meta-knowledge, and we will look at ways to lighten the burden of its creation. After having described some particularities of this kind of knowledge, we introduce ClaimSpotter, our approach to support its ‘capture’, based on the elicitation of a number of recommendations which are presented for consideration to our annotators (or analysts), and give some elements of evaluation.

Automatic Identification of False Friends in Parallel Corpora: Statistical and Semantic Approach

False friends are pairs of words in two languages that are perceived as similar but have different meanings. We present an improved algorithm for acquiring false friends from sentence-level aligned parallel corpus based on statistical observations of words occurrences and co-occurrences in the parallel sentences. The results are compared with an entirely semantic measure for cross-lingual similarity between words based on using the Web as a corpus through analyzing the words’ local contexts extracted from the text snippets returned by searching in Google. The statistical and semantic measures are further combined into an improved algorithm for identification of false friends that achieves almost twice better results than previously known algorithms. The evaluation is performed for identifying cognates between Bulgarian and Russian but the proposed methods could be adopted for other language pairs for which parallel corpora and bilingual glossaries are available.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Semantic representation for comparison of performance measurement indicators

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Az észlelt kultúraszemélyiség és hatása az ország turisztikai és üzleti célpontként való megítélésére (Investigation the effect of perceived culture – personality (CP) on the evaluation of the country as ideal business/tourist destination)

A kutatás fő célja annak vizsgálata, hogy egy adott kultúra észlelt személyisége milyen hatással van az ország ideális turisztikai, illetve üzleti célországként való megítélésére. A kultúraszemélyiség koncepciójának bemutatása után a kultúraszemélyiség-skála (culture personality, CP) fejlesztésének kezdeti eredményeit, majd a kultúraszemélyiség dimenzióinak vizsgálatát részletezik a szerzők. A skálakialakítás során – kvalitatív eredményeiket felhasználva – egy ötvenegy tulajdonságpárból álló szemantikus differenciálskálát fejlesztettek ki és teszteltek. Jelen cikkükben összehasonlítják Franciaország és Magyarország észlelt kultúraszemélyiségét, vizsgálva a magyar és a francia válaszadók véleményének hasonlóságait és különbségeit. Eredményeik azt mutatják, hogy nemcsak az egyes kultúrák személyiségét észlelik eltérően a válaszadók, de ugyanazon kultúra személyiségének bizonyos jellemzőit is eltérően ítélik meg. _____ The main purpose of our study was to investigate the effect of perceived culture – personality (CP) on the evaluation of the country as ideal business/tourist destination. After introducing the concept of culture-personality, initial results of our CP scale development are discussed, and dimensions of culture-personality will be examined. As a result of the scale construction procedure – based on our extensive qualitative studies –, a 51-item semantic differential scale has been developed and tested. Similarities and differences of perceived culture-personalities of France and Hungary among Hungarian and French respondents are described.

A semantic paradigm for intelligent data access

An implementation of Sem-ODB—a database management system based on the Semantic Binary Model is presented. A metaschema of Sem-ODB database as well as the top-level architecture of the database engine is defined. A new benchmarking technique is proposed which allows databases built on different database models to compete fairly. This technique is applied to show that Sem-ODB has excellent efficiency comparing to a relational database on a certain class of database applications. A new semantic benchmark is designed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic model. A fixed predefined implementation is not enforced allowing the database designer to choose the most efficient structures available in the DBMS tested. The results of the benchmark are analyzed. ^ A new high-level querying model for semantic databases is defined. It is proven adequate to serve as an efficient native semantic database interface, and has several advantages over the existing interfaces. It is optimizable and parallelizable, supports the definition of semantic userviews and the interoperability of semantic databases with other data sources such as World Wide Web, relational, and object-oriented databases. The query is structured as a semantic database schema graph with interlinking conditionals. The query result is a mini-database, accessible in the same way as the original database. The paradigm supports and utilizes the rich semantics and inherent ergonomics of semantic databases. ^ The analysis and high-level design of a system that exploits the superiority of the Semantic Database Model to other data models in expressive power and ease of use to allow uniform access to heterogeneous data sources such as semantic databases, relational databases, web sites, ASCII files, and others via a common query interface is presented. The Sem-ODB engine is used to control all the data sources combined under a unified semantic schema. A particular application of the system to provide an ODBC interface to the WWW as a data source is discussed. ^

Design principles of semantic binary database management systems

The Semantic Binary Data Model (SBM) is a viable alternative to the now-dominant relational data model. SBM would be especially advantageous for applications dealing with complex interrelated networks of objects provided that a robust efficient implementation can be achieved. This dissertation presents an implementation design method for SBM, algorithms, and their analytical and empirical evaluation. Our method allows building a robust and flexible database engine with a wider applicability range and improved performance. ^ Extensions to SBM are introduced and an implementation of these extensions is proposed that allows the database engine to efficiently support applications with a predefined set of queries. A New Record data structure is proposed. Trade-offs of employing Fact, Record and Bitmap Data structures for storing information in a semantic database are analyzed. ^ A clustering ID distribution algorithm and an efficient algorithm for object ID encoding are proposed. Mapping to an XML data model is analyzed and a new XML-based XSDL language facilitating interoperability of the system is defined. Solutions to issues associated with making the database engine multi-platform are presented. An improvement to the atomic update algorithm suitable for certain scenarios of database recovery is proposed. ^ Specific guidelines are devised for implementing a robust and well-performing database engine based on the extended Semantic Data Model. ^

Scalable semantic aware context storage

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

URIs and Intertextuality: Incumbent Philosophical Commitments in the Development of the Semantic Web

Examines two commitments inherent in Resource Description Framework (RDF): intertextuality and rationalism. After introducing how rationalism has been studied in knowledge organization, this paper then introduces the concept of bracketed-rationalism. This paper closes with a discussion of ramifications of intertextuality and bracketed rationalism on evaluation of RDF.

Neural Machine Translation in the Field of Law. Analysis and Evaluation against Human Reference of NMT Raw Outputs of an Atto Costitutivo of S.r.l. from Italian into German.

Artificial Intelligence (AI) is gaining ever more ground in every sphere of human life, to the point that it is now even used to pass sentences in courts. The use of AI in the field of Law is however deemed quite controversial, as it could provide more objectivity yet entail an abuse of power as well, given that bias in algorithms behind AI may cause lack of accuracy. As a product of AI, machine translation is being increasingly used in the field of Law too in order to translate laws, judgements, contracts, etc. between different languages and different legal systems. In the legal setting of Company Law, accuracy of the content and suitability of terminology play a crucial role within a translation task, as any addition or omission of content or mistranslation of terms could entail legal consequences for companies. The purpose of the present study is to first assess which neural machine translation system between DeepL and ModernMT produces a more suitable translation from Italian into German of the atto costitutivo of an Italian s.r.l. in terms of accuracy of the content and correctness of terminology, and then to assess which translation proves to be closer to a human reference translation. In order to achieve the above-mentioned aims, two human and automatic evaluations are carried out based on the MQM taxonomy and the BLEU metric. Results of both evaluations show an overall better performance delivered by ModernMT in terms of content accuracy, suitability of terminology, and closeness to a human translation. As emerged from the MQM-based evaluation, its accuracy and terminology errors account for just 8.43% (as opposed to DeepL’s 9.22%), while it obtains an overall BLEU score of 29.14 (against DeepL’s 27.02). The overall performances however show that machines still face barriers in overcoming semantic complexity, tackling polysemy, and choosing domain-specific terminology, which suggests that the discrepancy with human translation may still be remarkable.

Evaluation Of The Radiopacity Of Esthetic Root Canal Posts.

The radiopacity of esthetic root canal posts may impair the assessment of their fit to the root canal when using radiographic images. This study determined in vitro the radiographic density of esthetic root canal posts using digital images. Thirty-six roots of human maxillary canines were assigned to six groups (N=6 per group): Reforpost (RP); Aestheti-Plus (AP); Reforpost MIX (RPM); D.T. Light Post (LP); Reforpost Radiopaque (RPR); and White Post DC (WP). Standardized digital images of the posts were obtained in different conditions: outside the root canal, inside the canal before and after cementation using luting material, and with a tissue simulator. Analysis of variance was used to compare the radiopacity mean values among the posts outside the root canal and among the posts under the other conditions, and the t unpaired test to compare the radiopacity between the posts and the dentin, and between the posts and the root canal space. There was no statistically significant difference in radiopacity between RP and RPM, and LP and WP. AP posts showed radiopacity values significantly lower than those for dentin. No statistically significant difference was found between posts (RP and AP) and the root canal space. A statistically significant difference was observed between the luted and non-luted posts; additionally, luted posts with and without tissue simulator showed no significant differences. Most of the cement-luted posts analyzed in this study were distinguishable from the density of adjacent dentin surfaces, allowing radiographic confirmation of the fit of the post in the canal. The success of using esthetic root canal posts depends mainly on the fit of the post within the canal.[1] The radiopacity of a post allows for radiographic imaging to be used to determine the fit, an important factor in a clinical perspective.

Otoneurological Evaluation: Current Good Practice.

80

Endoscopic Endonasal Transsphenoidal Resection Of Pituitary Adenomas: Preliminary Evaluation Of Consecutive Cases.

Endoscopic endonasal transsphenoidal surgery has gained increasing acceptance by otolaryngologists and neurosurgeons. In many centers throughout the world, this technique is now routinely used for the same indications as conventional microsurgical technique for pituitary tumors. To present a surgical experience of consecutive endoscopic endonasal trans-sphenoidal resections of pituitary adenomas. In this study, consecutive patients with pituitary adenomas submitted to endoscopic endonasal pituitary surgery were evaluated regarding the rate of residual tumor, functional remission, symptoms relief, complications, and tumor size. Forty-seven consecutive patients were evaluated; 17 had functioning adenomas, seven had GH producing tumors, five had Cushing's disease, and five had prolactinomas. Of the functioning adenomas, 12 were macroadenomas and five were microadenomas; 30 cases were non-functioning macroadenomas. Of the patients with functioning adenomas, 87% improved. 85% of the patients with visual deficits related to optic nerve compression progressed over time. Most of the patients with complaints of headaches improved (76%). Surgical complications occurred in 10% of patients, which included with two carotid lesions, two cerebrospinal fluid leaks, and one death of a patient with a previous history of complications. Endoscopic endonasal pituitary surgery is a feasible technique, yielding good surgical and functional outcomes, and low morbidity.