Semantic Fingerprint Research Articles

Structural invariants and semantic fingerprints in the "ego network" of words.

Well-established cognitive models coming from anthropology have shown that, due to the cognitive constraints that limit our "bandwidth" for social interactions, humans organize their social relations according to a regular structure. In this work, we postulate that similar regularities can be found in other cognitive processes, such as those involving language production. In order to investigate this claim, we analyse a dataset containing tweets of a heterogeneous group of Twitter users (regular users and professional writers). Leveraging a methodology similar to the one used to uncover the well-established social cognitive constraints, we find regularities at both the structural and semantic levels. In the former, we find that a concentric layered structure (which we call ego network of words, in analogy to the ego network of social relationships) very well captures how individuals organise the words they use. The size of the layers in this structure regularly grows (approximately 2-3 times with respect to the previous one) when moving outwards, and the two penultimate external layers consistently account for approximately 60% and 30% of the used words, irrespective of the number of layers of the user. For the semantic analysis, each ring of each ego network is described by a semantic profile, which captures the topics associated with the words in the ring. We find that ring #1 has a special role in the model. It is semantically the most dissimilar and the most diverse among the rings. We also show that the topics that are important in the innermost ring also have the characteristic of being predominant in each of the other rings, as well as in the entire ego network. In this respect, ring #1 can be seen as the semantic fingerprint of the ego network of words.

Linking asset prices to news without direct asset mentions

ABSTRACT Advances in Natural Language Processing (NLP), computing power and data availability are driving an explosion in research about the impact of news on asset prices. However, when relating news to individual assets, this research is based on mentions of specific assets or related terms in the news stories. Such an approach has two shortcomings. First, it requires a substantial time investment in a specific NLP technology. Second, and more importantly, it ignores news articles that do not directly mention a given asset or a pre-defined asset-related term, even if these articles are logically related to the asset in question. Our approach relies instead on a novel NLP technology called ‘semantic fingerprinting’, which projects any text onto a binary vector representing its meaning. The greater the overlap between the semantic fingerprint of a news article and a given asset description, the more relevant we expect the article to be, whether or not the given asset is mentioned in the news directly. We show that this approach successfully picks up the positive impact of news on prices of commonly traded commodities using a dataset of general news published by The Guardian. We include the needed data and instructions for implementing this approach.

Using Textual Analysis to Diversify Portfolios

Semantic fingerprinting is a leading AI solution that combines recent developments from cognitive neuroscience and psycholinguistics to analyze text with human-level accuracy. As an efficient method of quantifying text, it has already found its application in finance where the semantic fingerprints of company descriptions have been shown to successfully predict stock return correlations of Dow Jones Industrial Average (DJIA) constituents. By extension, it has been suggested that diversified portfolios could be constructed to exploit the fundamental (dis)similarity between companies’ core activities (measured by the semantic overlap of company descriptions). This paper follows the performance of two portfolios made of the same DJIA constituent companies: the “minimum semantic concentration” portfolio (constructed with text-based portfolio weights) and the traditional “minimum variance” portfolio, over a time span of 16 years including two high volatility events: the 2007 − 2009 financial crisis and the COVID pandemic. The results confirm that textual analysis using semantic fingerprinting is consistently successful in predicting stock return correlations and is valuable as a portfolio selection criterion. However, in times of high market volatility the fundamental information given by the companies’ core activities, while still relevant, might carry less weight.

Structural invariants and semantic fingerprints in the "ego network" of words

Structural invariants and semantic fingerprints in the "ego network" of words

Structural invariants and semantic fingerprints in the "ego network" of words

Structural invariants and semantic fingerprints in the "ego network" of words

A Mathematical Model for Universal Semantics.

We characterize the meaning of words with language-independent numerical fingerprints, through a mathematical analysis of recurring patterns in texts. Approximating texts by Markov processes on a long-range time scale, we are able to extract topics, discover synonyms, and sketch semantic fields from a particular document of moderate length, without consulting external knowledge-base or thesaurus. Our Markov semantic model allows us to represent each topical concept by a low-dimensional vector, interpretable as algebraic invariants in succinct statistical operations on the document, targeting local environments of individual words. These language-independent semantic representations enable a robot reader to both understand short texts in a given language (automated question-answering) and match medium-length texts across different languages (automated word translation). Our semantic fingerprints quantify local meaning of words in 14 representative languages across five major language families, suggesting a universal and cost-effective mechanism by which human languages are processed at the semantic level. Our protocols and source codes are publicly available on https://github.com/yajun-zhou/linguae-naturalis-principia-mathematica.

A Text Similarity Measurement Based on Semantic Fingerprint of Characteristic Phrases

Text similarity measurements are the basis for measuring the degree of matching between two or more texts. Traditional large-scale similarity detection methods based on a digital fingerprint have the advantage of high detection speed, which are only suitable for accurate detection. We propose a method of Chinese text similarity measurement based on feature phrase semantics. Natural language processing (NLP) technology is used to pre-process text and extract the keywords by the Term frequency-Inverse document frequency (TF-IDF) model and further screen out the feature words. We get the exact meaning of a word and semantic similarities between words and a HowNet semantic dictionary. We substitute concepts to get the feature phrases and generate a semantic fingerprint and calculate similarity. The experimental results indicate that the method proposed is superior in similarity detection in terms of its accuracy rate, recall rate, and F-value to the traditional and digital fingerprinting method.

Bilateral Home Bias: A New Measure of Proximity

This paper uses recent developments from the fields of cognitive neuroscience and psycho-linguistics to introduce a new measure of proximity to the set of typical gravity variables in a model for bilateral home bias. Given the `weightlessness' of financial assets, gravity variables (among which geographical distance is the most prominent) are meaningful only as proxies for information or familiarity. The new measure of country similarity aims to directly capture the conceptual closeness of countries by comparing their semantic fingerprints. An AI solution which emulates the way the human brain learns and establishes associations among concepts, called the Retina engine, makes it possible to analyse text with human-level accuracy and to extract its semantic fingerprint (a numerical representation of meanings associated with the given term or a text). Akin to an artificial `investor' who has read virtually the entire Wikipedia, the Retina engine is able to quantify and compare textual descriptions of any pair of countries. In a model for bilateral home bias, the resulting measures of country similarity appear informative above and beyond distance and other gravity variables (common language, border, colonial link etc.). At its best, country similarity outperforms distance both in terms of statistical significance and impact on the dependent variable.

Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching

Background and objectiveHealthcare systems deal with multiple challenges in releasing information from data silos, finding it almost impossible to be implemented, maintained and upgraded, with difficulties ranging in the technical, security and human interaction fields. Currently, the increasing availability of health data is demanding data-driven approaches, bringing the opportunities to automate healthcare related tasks, providing better disease detection, more accurate prognosis, faster clinical research advance and better fit for patient management. In order to share data with as many stakeholders as possible, interoperability is the only sustainable way for letting systems to talk with one another and getting the complete image of a patient. Thus, it becomes clear that an efficient solution in the data exchange incompatibility is of extreme importance. Consequently, interoperability can develop a communication framework between non-communicable systems, which can be achieved through transforming healthcare data into ontologies. However, the multidimensionality of healthcare domain and the way that is conceptualized, results in the creation of different ontologies with contradicting or overlapping parts. Thus, an effective solution to this problem is the development of methods for finding matches among the various components of ontologies in healthcare, in order to facilitate semantic interoperability. MethodsThe proposed mechanism promises healthcare interoperability through the transformation of healthcare data into the corresponding HL7 FHIR structure. In more detail, it aims at building ontologies of healthcare data, which are later stored into a triplestore. Afterwards, for each constructed ontology the syntactic and semantic similarities with the various HL7 FHIR Resources ontologies are calculated, based on their Levenshtein distance and their semantic fingerprints accordingly. Henceforth, after the aggregation of these results, the matching to the HL7 FHIR Resources takes place, translating the healthcare data into a widely adopted medical standard. ResultsThrough the derived results it can be seen that there exist cases that an ontology has been matched to a specific HL7 FHIR Resource due to its syntactic similarity, whereas the same ontology has been matched to a different HL7 FHIR Resource due to its semantic similarity. Nevertheless, the developed mechanism performed well since its matching results had exact match with the manual ontology matching results, which are considered as a reference value of high quality and accuracy. Moreover, in order to furtherly investigate the quality of the developed mechanism, it was also evaluated through its comparison with the Alignment API, as well as the non-dominated sorting genetic algorithm (NSGA-III) which provide ontology alignment. In both cases, the results of all the different implementations were almost identical, proving the developed mechanism’s high efficiency, whereas through the comparison with the NSGA-III algorithm, it was observed that the developed mechanism needs additional improvements, through a potential adoption of the NSGA-III technique. ConclusionsThe developed mechanism creates new opportunities in conquering the field of healthcare interoperability. However, according to the mechanism’s evaluation results, it is almost impossible to create syntactic or semantic patterns for understanding the nature of a healthcare dataset. Hence, additional work should be performed in evaluating the developed mechanism, and updating it with respect to the results that will derive from its comparison with similar ontology matching mechanisms and data of multiple nature.

Research on Author Name Disambiguation Based on Fusion Features and Semantic Fingerprints

Author name disambiguation has been a challenging problem in many applications. In order to promote researches to solve name disambiguation, Aminer launched the Open Academic Data Challenge 2018 jointly with Chinese Association for Artificial Intelligence and China Knowledge Centre for Engineering and Technology. Aminer is a scholar-cantered academic search and mining platform covering more than 200 million papers and more than 100 million scholars in various academic fields. Our team proposed a name disambiguation method based on fusion features and semantic fingerprint technique to participate in the competition. The method identified authors with same names through organization feature and co-author feature at first, and then it solves ambiguity names by way of semantic fingerprints which are 128-bit binary vector generated from textual features of papers by Simhash algorithm. Our method scored 0.609 on the verification set and 0.879 on the test set ranking first in the final submission.

Disambiguating USPTO inventor names with semantic fingerprinting and DBSCAN clustering

PurposeThe aim of this study is to present a novel approach based on semantic fingerprinting and a clustering algorithm called density-based spatial clustering of applications with noise (DBSCAN), which can be used to convert investor records into 128-bit semantic fingerprints. Inventor disambiguation is a method used to discover a unique set of underlying inventors and map a set of patents to their corresponding inventors. Resolving the ambiguities between inventors is necessary to improve the quality of the patent database and to ensure accurate entity-level analysis. Most existing methods are based on machine learning and, while they often show good performance, this comes at the cost of time, computational power and storage space.Design/methodology/approachUsing DBSCAN, the meta and textual data in inventor records are converted into 128-bit semantic fingerprints. However, rather than using a string comparison or cosine similarity to calculate the distance between pair-wise fingerprint records, a binary number comparison function was used in DBSCAN. DBSCAN then clusters the inventor records based on this distance to disambiguate inventor names.FindingsExperiments conducted on the PatentsView campaign database of the United States Patent and Trademark Office show that this method disambiguates inventor names with recall greater than 99 per cent in less time and with substantially smaller storage requirement.Research limitations/implicationsA better semantic fingerprint algorithm and a better distance function may improve precision. Setting of different clustering parameters for each block or other clustering algorithms will be considered to improve the accuracy of the disambiguation results even further.Originality/valueCompared with the existing methods, the proposed method does not rely on feature selection and complex feature comparison computation. Most importantly, running time and storage requirements are drastically reduced.

Enhancing theWeb WithAdvanced Engineering

Enhancing theWeb WithAdvanced Engineering

CalmWPC: A buffer management to calm down write performance cliff for NAND flash-based storage systems

NAND Flash-based solid state disks (i.e., SSDs) are widely applied in large-scale storage systems. However, NAND Flash is featured with the asymmetric read and write performance, high erase latency, and the limited number of program/erase cycles (P/Es). Under random write-intensive workloads, a garbage collection (i.e., GC) process inside SSDs causes write performance cliff, which causes high latency for I/O access and degrades SSD lifetime. In real-time transactional applications, such large write performance cliff affects the response time of I/O requests, thereby leading to serious critical errors in real-time applications.To handle this issue, we propose a buffer management strategy called CalmWPC to calm down SSD write performance cliff. CalmWPC seamlessly integrates a data cluster-based data management, a historical access-based prediction algorithm, a semantic fingerprint database. The prediction algorithm checks the future data-cluster activity while classifying the cluster based on its historical write operations. The fingerprint database stores semantic messages for write/update between the buffer and NAND Flash memory. With the fingerprint database in place, CalmWPC calculates the number of invalid data pages in a block in real time. CalmWPC flushes the data cluster into flash memory when the number of update pages reaches a predefined threshold. Our CalmWPC optimizes write performance cliff during GC under random-write workloads.Experimental results reveal that CalmWPC is able to reduce write performance cliff, improve the average latency of user I/Os, and optimize write amplification. Take Financial1 as an example, CalmWPC reduces the write performance cliff by averages of 60.9% and 60.0% compared with LRU and CFLRU. CalmWPC also shortens the response time of LRU and CFLRU by averages of 69.4% and 70.1%, respectively.

Cognitive dialog games as cognitive assistants: Tracking and adapting knowledge and interactions in student's dialogs

This study introduces a system design in a form of cognitive dialog game (DiaCog) to support pedagogical factors and student learning model ideas. The purpose of the study is to describe how such a design achieves tracking and adapting students' knowledge and mastery learning levels as a cognitive assistant. Also, this study shows alternative ways for supporting intelligent personal learning, tutoring systems, and MOOCS. This paper explains method called DiaCog that uses structure for students' thinking in an online dialog by tracking student's level of learning/knowledge status. The methodology of computing is the semantic that match between students' interactions in a dialog. By this way it informs DiaCog's learner model to inform the pedagogical model. Semantic fingerprint matching method of DiaCog allows making comparisons with expert knowledge to detect students' mastery levels in learning. The paper concludes with the DiaCog tool and methodologies that used for intelligent cognitive assistant design to implement pedagogical and learner model to track and adapt students' learning. Finally, this paper discusses future improvements and planned experimental set up to advance the techniques introduced in DiaCog design.

A Fine-Grained Indoor Location-Based Social Network

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users' locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent.

Semantic fingerprints-based author name disambiguation in Chinese documents

Author name disambiguation is an important problem that needs to be resolved in bibliometric analysis or tech mining. Many techniques have been presented; however, most of them require a long run time or additional information. A new method based on semantic fingerprints was presented to disambiguate author names without external data. A manually annotated dataset was built to testify on the efficiency of the presented method. Experiments using co-author features, institution features, and text fingerprints were conducted respectively. We found that the first two methods had higher precision, but their recall was low, and the text fingerprint method had higher recall and satisfied precision. Based on these results, we integrated co-author features, institution features, and text fingerprints to provide semantic fingerprints for disambiguating author names and achieving better performance on the F-measure.

Fast Duplicate Detection for Chinese Texts Based on Semantic Fingerprint

Fast Duplicate Detection for Chinese Texts Based on Semantic Fingerprint

Graph-based retrieval of building information models for supporting the early design stages

Building information modeling (BIM) principles are transforming today’s communication and working processes in the field of construction, however the early design phases are only rarely supported and information technology is therefore not exploited to its full potential. The early design phases are characterized by an iterative process of searching for plausible solutions. A common approach is to refer to similar examples, which are conventionally found using keyword-based search strategies.To this end we propose a method for indexing spatial configurations along with a sketch-based input method for search strategies that uses so-called semantic fingerprints of buildings. The topology of spatial configurations is extracted from building information models and represented as graphs. For both building information models and the user sketches, the extracted graphs are used as the basis for a subgraph-matching algorithm facilitating an intuitive novel query method for researching similar reference examples. The system is able to present corresponding existing solutions to even rudimentary sketches or fragments of a design idea. In addition to graph matching and sketch-based interaction, more recent BIM-based approaches are also taken into account.