Concept Annotation Research Articles

A new synonym-substitution method to enrich the human phenotype ontology

BackgroundNamed entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem.ResultsThis paper presents an automated synonym-substitution method to enrich the Human Phenotype Ontology (HPO) with new synonyms. The approach is mainly based on both the lexical properties of the terms and the hierarchical structure of the ontology. By scanning the lexical difference between a term and its descendant terms, the method can learn new names and modifiers in order to generate synonyms for the descendant terms. By searching for the exact phrases in MEDLINE, the method can automatically rule out illogical candidate synonyms. In total, 745 new terms were identified. These terms were indirectly evaluated through the concept annotations on a gold standard corpus and also by document retrieval on a collection of abstracts on hereditary diseases. A moderate improvement in the F-measure performance on the gold standard corpus was observed. Additionally, 6% more abstracts on hereditary diseases were retrieved, and this percentage was 33% higher if only the highly informative concepts were considered.ConclusionsA synonym-substitution procedure that leverages the HPO hierarchical structure works well for a reliable and automatic extension of the terminology. The results show that the generated synonyms have a positive impact on concept recognition, mainly those synonyms corresponding to highly informative HPO terms.

Explanatory Annotation of Literary Texts and the Reader: Seven Types of Problems

While most literary scholars wish to help readers understand literary texts by providing them with explanatory annotations, we want to go a step further and enable them, on the basis of structured information, to arrive at interpretations of their own. We therefore seek to establish a concept of explanatory annotation that is reader-oriented and combines hermeneutics with the opportunities provided by digital methods. In a first step, we are going to present a few examples of existing annotations that apparently do not take into account readerly needs. To us, they represent seven types of common problems in explanatory annotation. We then introduce a possible model of best practice which is based on categories and structured along the lines of the following questions: What kind(s) of annotations do improve text comprehension? Which contexts must be considered when annotating? Is it possible to develop a concept of the reader on the basis of annotations—and can, in turn, annotations address a particular kind of readership, i.e.: in how far can annotations be(come) individualised?

Challenges for Annotation Concepts in Music

Traditional historico-critical music editions provide scholars and musicians with an edited score based on the interpretation of various sources such as the composer's autograph(s), letters and other materials from copyists or publishers. Their digital counterparts have the potential to offer new and more expanded ways to explore the work. This is based on the possibility to provide large amounts of source materials and to annotate more extensively (since printing costs are irrelevant). Furthermore, audio and video recordings of performances can be integrated. But, similarly important, the user interface makes it much easier to navigate in the complex network of cross-references between various source materials and the editor's annotations. The XML-based Music Encoding Initiative (MEI) format is the standard music representation format for digital music editions. In this article we discuss current MEI-based annotation practices and outline the current challenges for music annotations, including a discussion of anchoring options, the embedding of addressable elements in the local musical context, the annotation of audio, and the categorisation of annotations. This leads to a discussion of open questions such as the ability to secure authorship in open and reusable editions.

Towards Building a Semantic Repository of Bioinformatics Resources

The paper presents a work in progress directed to the creation of a semantic digital repository of scholarly resources in the area of bioinformatics. A special attention has been paid to the design of a lightweight subject ontology that will be used for automated conceptual annotation of the dynamically entering new materials and as a knowledge source for intelligent search over the repository. Some issues concerning the implementation of the repository and a suitable tool for search and document retrieval over its resources are discussed as well.

Active learning reduces annotation time for clinical concept extraction

ObjectiveTo investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. Materials and methodsThere are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. ResultsWhen annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. DiscussionThe number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. ConclusionActive learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations.

Conceptual annotation of text patterns

AbstractPatterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better.

LinguaKit: uma ferramenta multilingue para a análise linguística e a extração de informação

This paper presents LinguaKit, a multilingual suite of tools for analysis, extraction, annotation and linguistic correction. LinguaKit allows the user to perform different tasks such as lemmatization, PoS-tagging or syntactic parsing (among others), including applications for sentiment analysis (or opinion mining), extraction of multiword expressions or conceptual annotation and entity linking to DBpedia. Most part of the developed modules work in four linguistic varieties: Portuguese, Spanish, English, and Galician. The system is programmed in Perl, and it is freely available under a GPLv3 license.

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. We find that Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In this study we evaluate to what extent the tags entered by players can be regarded as topical descriptors of the video material. Moreover, we characterize the quality of the user tags as topical descriptors with the aim to detect and filter out the bad ones. Our results show that after filtering, game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. We find that Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In this study we evaluate to what extent the tags entered by players can be regarded as topical descriptors of the video material. Moreover, we characterize the quality of the user tags as topical descriptors with the aim to detect and filter out the bad ones. Our results show that after filtering, game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

Entity linking for tweets

Named Entity Linking (NEL) is the task of semantically annotating entity mentions in a portion of text with links to a knowledge base. The automatic annotation, which requires the recognition and disambiguation of the entity mention, usually exploits contextual clues like the context of usage and the coherence with respect to other entities. In Twitter, the limits of 140 characters originates very short and noisy text messages that pose new challenges to the entity linking task. We propose an overview of NEL methods focusing on approaches specifically developed to deal with short messages, like tweets. NEL is a fundamental task for the extraction and annotation of concepts in tweets, which is necessary for making the Twitter’s huge amount of interconnected user-generated contents machine readable and enable the intelligent information access.

Computing Semantic Similarity of Concepts in Knowledge Graphs

This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.

Gold-standard ontology-based anatomical annotation in the CRAFT Corpus.

Gold-standard annotated corpora have become important resources for the training and testing of natural-language-processing (NLP) systems designed to support biocuration efforts, and ontologies are increasingly used to facilitate curational consistency and semantic integration across disparate resources. Bringing together the respective power of these, the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of full-length, open-access biomedical journal articles with extensive manually created syntactic, formatting and semantic markup, was previously created and released. This initial public release has already been used in multiple projects to drive development of systems focused on a variety of biocuration, search, visualization, and semantic and syntactic NLP tasks. Building on its demonstrated utility, we have expanded the CRAFT Corpus with a large set of manually created semantic annotations relying on Uberon, an ontology representing anatomical entities and life-cycle stages of multicellular organisms across species as well as types of multicellular organisms defined in terms of life-cycle stage and sexual characteristics. This newly created set of annotations, which has been added for v2.1 of the corpus, is by far the largest publicly available collection of gold-standard anatomical markup and is the first large-scale effort at manual markup of biomedical text relying on the entirety of an anatomical terminology, as opposed to annotation with a small number of high-level anatomical categories, as performed in previous corpora. In addition to presenting and discussing this newly available resource, we apply it to provide a performance baseline for the automatic annotation of anatomical concepts in biomedical text using a prominent concept recognition system. The full corpus, released with a CC BY 3.0 license, may be downloaded from http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml. Database URL: http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml

Sustainable Formal Representation Of Breast Cancer Grading Histopathological Knowledge

Introduction/ Background Recently, histopathology has seen the introduction of several tools such as slide scanners and virtual slide technologies, creating the conditions for broader adoption of computer aided diagnosis based on whole slide images (WSI) to reduce observation variability between pathologists. This change brings up a number of new scientific challenges such as the sustainable management of the semantics associated to the grading process, image analysis and annotation in order to facilitate pre-filled report generation. The College of American Pathologists cancer checklists and protocols (CAP-CC&P) [1] are reference resources for complete Anatomic Pathology (AP) reporting of malignant tumors. Current terminology systems for AP structured reporting gather terms of very different granularity [2][3] and have not yet been compiled in a systematic approach. Semantic data models are formal representations of knowledge in a given domain that allow both human users and software applications to consistently and accurately interpret domain terminology [4][5]. Aims Our objective is to i) analyze the histopathological knowledge for breast cancer grading available in the reference CAP CC&P and ii) to build a sustainable formal representation of this knowledge based on existing bio- medical ontologies in NCBO Bioportal [6][7] and UMLS semantic types [8]. Methods Our methodology was first experimented in the context of two cancer grading methods for invasive (Nottinghamsystem) and ductal in situ breast carcinoma. A corpus consisting of 5 texts or “notes” was first selected by an AP expert from the two corresponding CAP CC&Ps. From each note the expert also extracted a list of keyconcepts to be used as a “gold standard”. We used NCBO Annotator [9] for automatic analysis of the corpus. Annotator supports the biomedical community in tagging raw texts automatically with concepts from relevant biomedical ontology and terminology repositories. The methodology used consists in: i) Automatic textual analysis and annotation of the corpus based on the 417 ontologies available on the NCBO platform. We selected a subset of ontologies based on the number of identified concepts and evaluated their relevancy with respect to the gold standard. ii) Semantic modeling of the automatically extracted concepts into a sustainable formal representation based on their UMLS semantic types. Results We identified NCIT, SNOMED-CT, NCI CaDSR Values set, LOINC and PathLex as the ontologies providing the highest number of annotated concepts. Table 1 shows as percentages the coverages of the concepts of each note by the annotations of the 5 reference ontologies. Percentages can add to more than 100 for a single note due to the possible overlap in ontologies coverages. Table 2 uses the same format when only concepts from the gold standards are counted to quantify annotations. From the list of extracted concepts, we made a preliminary formal representation of the histopathological knowledge based on the UMLS semantic types of concepts. Figure 1 shows the so proposed semantic modeling in the context of tubular differentiation. The novelty of this approach is the federation of the knowledge issued from different sources (CAP CC&P, NCBO ontologies and UMLS Metathesaurus) and the sustainable management of the associated semantics. This opens the perspective of building an AP observation ontology that will allow an accurate representation of AP reports understandable by both human and software applications.

Content based Video Retrieval using Text Annotation and Low Level Features Technique

The information retrieval processes are playing essential role in the computer based database exploration or finding the essential contents from the databases. Now in these days a number of search techniques and retrieval models are exist by using which the users can find the data. According to the different data formats the information retrieval processes are also varying therefore different data format based retrieval process are works in different manner. In this presented work the content based video retrieval model is presented. In the content based video retrieval model the work is initiated from the segmentation of the videos into the set of frames. The segmentation of video is performed using the FFMPEG media library that works on the basis of two parameters first the video clip and second the time slot duration by which the frames are extracted. After segmentation to enable the text based query the text annotation concept is used and the individual frames of video is tagged with the user defined text. On the other hand to enable the query by example the low level features of individual frames are also computed. To compute the low level descriptors shape, color and texture analysis is performed. Thus the canny edge detection, local binary pattern and the color movement analysis techniques are used. Finally for classifying the relevant videos according to the user query the KNN classifier is implemented. That classifier accepts the user text query or the example query for classifying the similar video objects from the database. The implementation of the presented methodology is performed using JAVA technology. Additionally for finding the performance precision, recall, and f-measures are computed, according to the computed values the example based techniques provides more accurate outcomes as compared to text based query. In addition of that the resource consumption of the proposed technique is also computed in terms of time and space, according to the results the example based query processing consumes additional resources as compared to text based methods. Thus the proposed work is accomplished with the satisfactory performance.

Updating and extending the concept annotations of the CRAFT corpus

Updating and extending the concept annotations of the CRAFT corpus

A computer-supported method to reveal and assess Personal Professional Theories in vocational education

This article introduces a dedicated, computer-supported method to construct and formatively assess open, annotated concept maps of Personal Professional Theories (PPTs). These theories are internalised, personal bodies of formal and practical knowledge, values, norms and convictions that professionals use as a reference to interpret and acquire knowledge, and to direct their behaviour, and which vocational students are expected to develop. Monitoring the development of PPTs and assessing their quality are difficult as they are, essentially, mental schemes. Traditional methods, such as semi-structured interviews and concept mapping, are either too labour-intensive to be used in an educational setting or are not able to reveal their full quality. The study presents a new method which is valid, reliable and easy to use in education and which reveals the quality in a way that is comparable to or better than interviews.

JOAQUÍN PÉREZ SALAS: ENTRE LA DEFENSA DEL ORDEN REPUBLICANO Y LA CONTRAREVOLUCIÓN (1936-1939) = JOAQUIN PEREZ SALAS: BETWEEN THE DEFENCE OF THE REPUBLICAN ORDER AND THE COUNTER-REVOLUTION (1936-1939)

We are interested in the career of Lieutenant Colonel Joaquin Perez Salas, valued as a staunch Republican. It is considered that their last performance at the service of the casadista counter-revolution allows us to review the annotated concept, so widespread as accepted. We will try to uncover in that time and because started a one-way trip. The summary trial to which he was subjected –located in the Cartagena Naval Archives-, It allows to illuminate certain unpublished aspects of his performance, underlining its well-established reputation of Gunner, and outline some personality traits. The information gathered reveals the evolution of a soldier considered Republican without “add-ons”, his determined alignment next to the regime that has sworn allegiance, his opposition to class violence, his friction for this reason with the popular militia, his role in the coup of Casado, and the story of the last hours of the Republic as head of the naval base of Cartagena. A material that we have contrasted with other contributions - unpublished and printed - and the relevant bibliography.

Phylogenetic revision of Minyomerus Horn, 1876 sec. Jansen & Franz, 2015 (Coleoptera, Curculionidae) using taxonomic concept annotations and alignments.

This contribution adopts the taxonomic concept annotation and alignment approach. Accordingly, and where indicated, previous and newly inferred meanings of taxonomic names are individuated according to one specific source. Articulations among these concepts and pairwise, logically consistent alignments of original and revisionary classifications are also provided, in addition to conventional nomenclatural provenance information. A phylogenetic revision of the broad-nosed weevil genera Minyomerus Horn, 1876 sec. O’Brien & Wibmer (1982), and Piscatopus Sleeper, 1960 sec. O’Brien & Wibmer (1982) (Curculionidae [non-focal]: Entiminae [non-focal]: Tanymecini [non-focal]) is presented. Prior to this study, Minyomerus sec. O’Brien & Wibmer (1982) contained seven species, whereas the monotypic Piscatopus sec. O’Brien & Wibmer (1982) was comprised solely of Piscatopus griseus Sleeper, 1960 sec. O’Brien & Wibmer (1982). We thoroughly redescribe these recognized species-level entities and furthermore describe ten species as new to science: Minyomerus bulbifrons sec. Jansen & Franz (2015) (henceforth: [JF2015]), sp. n., Minyomerus aeriballux [JF2015], sp. n., Minyomerus cracens [JF2015], sp. n., Minyomerus gravivultus [JF2015], sp. n., Minyomerus imberbus [JF2015], sp. n., Minyomerus reburrus [JF2015], sp. n., Minyomerus politus [JF2015], sp. n., Minyomerus puticulatus [JF2015], sp. n., Minyomerus rutellirostris [JF2015], sp. n., and Minyomerus trisetosus [JF2015], sp. n. A cladistic analysis using 46 morphological characters of 22 terminal taxa (5/17 outgroup/ingroup) yielded a single most-parsimonious cladogram (L = 82, CI = 65, RI = 82). The analysis strongly supports the monophyly of Minyomerus [JF2015] with eight unreversed synapomorphies, and places Piscatopus griseus sec. O’Brien & Wibmer (1982) within the genus as sister to Minyomerus rutellirostris [JF2015]. Accordingly, Piscatopus sec. Sleeper (1960), syn. n. is changed to junior synonymy of Minyomerus [JF2015], and its sole member Piscatopus griseus sec. Sleeper (1960) is moved to Minyomerus [JF2015] as Minyomerus griseus [JF2015], comb. n. In addition, the formerly designated type Minyomerus innocuus Horn, 1876 sec. Pierce (1913), syn. n. is changed to junior synonymy of Minyomerus microps (Say, 1831) [JF2015] which has priority. The genus is widespread throughout western North America, ranging from Canada to Mexico and Baja California. Apparent patterns of interspecific diversity of exterior and genitalic morphology, varying host plant ranges, overlapping and widely extending species distributions, suggest an early origin for Minyomerus [JF2015], with a diversification that likely followed the development of North American desert biomes. Three species in the genus – i.e., Minyomerus languidus Horn, 1876 [JF2015], Minyomerus microps [JF2015], and Minyomerus trisetosus [JF2015] – are putatively considered parthenogenetic.

Cost-Effective Conceptual Design for Information Extraction

It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improves the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are limited and/or its users may have urgent information needs, it may have to select only a subset of relevant concepts for extraction and annotation. We call this subset a conceptual design for the annotated collection. In this article, we introduce and formally define the problem of cost-effective conceptual design where, given a collection, a set of relevant concepts, and a fixed budget, one likes to find a conceptual design that most improves the effectiveness of answering queries over the collection. We provide efficient algorithms for special cases of the problem and prove it is generally NP-hard in the number of relevant concepts. We propose three efficient approximations to solve the problem: a greedy algorithm, an approximate popularity maximization (APM for short), and approximate annotation-benefit maximization (AAM for short). We show that, if there are no constraints regrading the overlap of concepts, APM is a fully polynomial time approximation scheme. We also prove that if the relevant concepts are mutually exclusive, the greedy algorithm delivers a constant approximation ratio if the concepts are equally costly, APM has a constant approximation ratio, and AAM is a fully polynomial-time approximation scheme. Our empirical results using a Wikipedia collection and a search engine query log validate the proposed formalization of the problem and show that APM and AAM efficiently compute conceptual designs. They also indicate that, in general, APM delivers the optimal conceptual designs if the relevant concepts are not mutually exclusive. Also, if the relevant concepts are mutually exclusive, the conceptual designs delivered by AAM improve the effectiveness of answering queries over the collection more than the solutions provided by APM.

Complex event recognition using constrained low-rank representation

Complex event recognition is the problem of recognizing events in long and unconstrained videos. In this extremely challenging task, concepts have recently shown a promising direction where core low-level events (referred to as concepts) are annotated and modeled using a portion of the training data, then each complex event is described using concept scores, which are features representing the occurrence confidence for the concepts in the event. However, because of the complex nature of the videos, both the concept models and the corresponding concept scores are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich concept scores. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the concept scores by a significant margin.