Glossary

Algorithmic trading refers to the use of computer-driven algorithms to automatically execute trades based on specific rules and data-driven signals. By incorporating real-time intelligence and alternative data—such as early warning signals detected through semantic analysis—traders and investors can react faster to market events, risks, and opportunities that may not be visible through traditional channels.
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Alpha Opportunities are investment or business prospects that have the potential to deliver above-average returns (“alpha”) compared to the broader market. At Semantic Visions, this term refers to unique opportunities identified through advanced analysis of open-source data (OSINT) that are not typically visible to most market participants. These opportunities often emerge from the early detection of signals such as shifts in supply chains, geopolitical developments, or signs of financial distress in companies.
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Alternative data refers to non-traditional information sources—beyond standard financial statements and market data—that provide unique insights into companies, industries, or markets. This includes news articles, social media, satellite imagery, supply chain reports, and other open-source content. By analyzing alternative data at scale, organizations can uncover hidden risks, emerging trends, and investment opportunities earlier than competitors relying on conventional data alone.
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Business insights are actionable understandings gained from analyzing data, helping organizations make informed decisions, identify opportunities, and mitigate risks. By turning large volumes of complex information into clear, strategic guidance, business insights enable companies to stay ahead of market trends and respond effectively to changes in their environment.

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Classification is a data analysis technique that involves sorting information into predefined categories based on specific criteria or patterns. In practice, this means using algorithms to automatically label or organize content—such as news articles, documents, or events—according to topics, sentiment, risk level, or other relevant attributes. Effective classification helps transform vast volumes of unstructured data into structured insights that can be more easily understood and acted upon.

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Commercial intelligence is the process of gathering, analyzing, and interpreting information about markets, competitors, and customers to support better business decisions and strategic planning. By leveraging advanced analytics and alternative data sources, organizations gain a deeper understanding of shifting market dynamics, emerging risks, and new opportunities—empowering them to maintain a competitive edge and adapt quickly to change.

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A commodity is a basic good or raw material that is interchangeable with others of the same type, such as oil, wheat, copper, or coffee. Commodities are traded globally, with prices shaped by supply and demand, geopolitical events, and economic shifts. At Semantic Visions, we continuously monitor and analyze hundreds of commodities—and the list keeps growing. You can explore key commodity highlights in our Free Data section.

A commodity market is a marketplace where raw materials and primary goods—such as oil, metals, grains, and other resources—are bought, sold, and traded, often on a global scale. Prices in these markets are influenced by factors like supply and demand, weather events, political developments, and economic trends. By closely tracking developments in commodity markets, Semantic Visions helps organizations anticipate disruptions, identify emerging risks, and spot new opportunities.
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Commodity risk refers to the potential for financial loss or operational disruption resulting from fluctuations in the price or availability of raw materials and primary goods. These risks can be triggered by market volatility, geopolitical events, supply chain interruptions, or changes in global demand. Proactively managing commodity risk enables organizations to better safeguard profits, ensure supply continuity, and make more informed strategic decisions.

Competitive intelligence is the practice of gathering, analyzing, and interpreting information about competitors, industry trends, and the broader market landscape. This process helps organizations anticipate competitor moves, identify market shifts, and inform strategic decisions. Leveraging advanced analytics and alternative data sources enables businesses to stay ahead of the curve and maintain their competitive advantage.

Competitor analysis is the process of systematically researching and evaluating rival companies to understand their strategies, strengths, weaknesses, and market positioning. By combining traditional sources with advanced analytics and alternative data, organizations can identify emerging threats, benchmark performance, and uncover strategic opportunities. This deeper understanding enables more effective planning and a sharper competitive edge.

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Complex event processing (CEP) is a technology for tracking and analyzing streams of real-time data to identify meaningful patterns, relationships, or trends as they happen. By processing and combining multiple events from different sources, CEP helps organizations detect critical situations—such as supply chain disruptions, market shifts, or emerging risks—faster and with greater accuracy. This enables timely decision-making and a proactive response to dynamic changes in the business environment.

Corporate Entity Expansion transforms a simple list of company names into an enriched, interconnected network of corporate relationships and semantic variants. This process leverages semantic intelligence and advanced mapping to uncover parent companies, subsidiaries, branches, and name variations—including abbreviations, diacritics, and non-Latin scripts. By integrating sanction lists and compliance data, Corporate Entity Expansion ensures risk models, alerts, and queries within the Semantic Visions platform operate on the most accurate and comprehensive dataset possible.

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Corporate structure mapping is the process of visualizing and analyzing the relationships between a company’s various legal entities, subsidiaries, and affiliates. This approach reveals ownership hierarchies, control structures, and cross-border connections, providing deeper insight into complex corporate networks. Effective corporate structure mapping supports due diligence, risk assessment, and more informed decision-making.

Credit risk is the possibility that a company or individual will fail to meet their financial obligations, such as repaying loans or settling invoices. This risk can impact lenders, investors, and business partners—potentially leading to financial losses or disrupted operations. By monitoring early warning signals and analyzing a wide range of open-source data, organizations can detect signs of financial distress sooner and make more informed credit decisions.

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Customer screening is the process of evaluating new and existing clients to identify potential risks, such as financial instability, sanctions exposure, or links to illicit activities. This involves checking customer information against public records, sanction lists, and other data sources. Effective customer screening helps organizations comply with regulations, reduce fraud, and maintain safe business relationships.

Due diligence is a comprehensive process of investigating and assessing a business, individual, or transaction before entering into a formal relationship. This includes reviewing financial records, legal status, reputation, ownership structure, and potential risks. At Semantic Visions, due diligence harnesses the power of open-source intelligence (OSINT) to uncover hidden connections, map entire supply chains, and reveal relationships that may not be immediately apparent through traditional methods—providing a deeper, more holistic risk assessment.

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ESG stands for Environmental, Social, and Governance—three key criteria used to evaluate a company’s ethical impact and sustainability practices.Environmental considerations include climate change, resource use, and pollution.Social factors cover labor practices, human rights, and community impact.Governance relates to leadership, transparency, and business ethics.By monitoring global data and uncovering emerging ESG-related risks or controversies, Semantic Visions helps organizations track compliance, reputational standing, and responsible business practices across their networks.

Early warning signals are subtle indicators or patterns—often detected before major events occur—that suggest rising risks, disruptions, or opportunities. These signals can include changes in news sentiment, supply chain issues, regulatory shifts, or emerging controversies. With advanced semantic analysis and open-source intelligence, Semantic Visions identifies early warning signals that help organizations anticipate problems, respond faster, and make proactive decisions.

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An electronic trading platform is a digital system that enables buyers and sellers to execute financial transactions—such as stocks, commodities, or currencies—over the internet or private networks. These platforms automate order matching, settlement, and reporting, allowing for faster, more efficient, and transparent trading. By integrating real-time data and analytics, electronic trading platforms help market participants make informed decisions and react quickly to changing conditions.

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An entity is any distinct object or subject tracked and analyzed within the Semantic Visions platform. Entities can take various forms—including companies, brands, commodities, industries, or locations. By structuring information around these entities, Semantic Visions enables precise monitoring, risk assessment, and the discovery of connections across global business and economic networks.

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Entity linking is the process of accurately identifying and connecting references to the same real-world entity—such as a company, brand, or location—across multiple sources, languages, and contexts. Leveraging advanced semantic analysis and a dedicated ontology team, Semantic Visions resolves ambiguities, matches name variations, and supports entity linking in 12 languages. This approach ensures that all relevant information about an entity is unified, enabling deeper insights and precise risk monitoring across complex, multilingual data streams.

Event correlation is the process of connecting and analyzing multiple data points or incidents to uncover meaningful relationships, patterns, or root causes. By linking related events across time, sources, or geographies, organizations can detect broader trends, cascading effects, or early warning signals that might otherwise go unnoticed. Semantic Visions uses event correlation to help clients understand complex risk scenarios and anticipate emerging threats with greater accuracy.

External dependencies management assessment is the systematic evaluation of an organization’s reliance on third parties—such as suppliers, service providers, or technology partners—and the associated risks these relationships pose. This assessment identifies critical dependencies, maps interconnected networks, and analyzes potential vulnerabilities or points of failure. With advanced data analytics, organizations can gain visibility into their external risk landscape, strengthen supply chain resilience, and make informed decisions to mitigate disruption.

Financial engineering is the application of mathematical models, computational techniques, and innovative financial instruments to solve complex problems or create new products in the world of finance. This discipline combines elements of economics, statistics, and data science to design strategies for risk management, investment, pricing, and funding. By leveraging alternative data and advanced analytics, financial engineering enables more informed decision-making and the development of tailored financial solutions.

Fundamental analysis is the process of evaluating a company’s intrinsic value by examining its financial statements, business model, management quality, industry position, and broader economic factors. The goal is to determine whether a company is undervalued or overvalued relative to its market price. By incorporating alternative data and real-time insights—such as news sentiment or supply chain signals—fundamental analysis can provide a deeper, more dynamic understanding of a company’s true performance and risk profile.

A futures exchange is a regulated marketplace where participants can buy and sell standardized contracts (futures) to purchase or deliver assets—such as commodities, currencies, or financial instruments—at a specified future date and price. These exchanges provide transparency, liquidity, and risk management tools for traders, producers, and investors. Monitoring activity and trends on futures exchanges can reveal early signals of market shifts, volatility, or supply chain disruptions.

Geopolitical risk refers to the potential for financial loss, operational disruption, or strategic impact caused by political events, policy changes, conflicts, or instability between countries or regions. These risks can influence markets, supply chains, investment flows, and business operations globally. Semantic Visions stands out by continuously monitoring not only English-language sources, but also news and data in 11 additional languages. This multilingual capability enables organizations to anticipate and respond to geopolitical risks faster and more comprehensively—often before they escalate.

Global macro refers to an investment and analysis approach that focuses on broad economic and geopolitical trends across countries and regions. This strategy considers factors such as interest rates, inflation, monetary policy, political developments, and global trade flows to inform investment decisions and risk management. By integrating alternative data and multilingual news analysis, Semantic Visions provides timely insights into global macro shifts, helping organizations navigate volatility and spot emerging opportunities.

Global supply chain management is the coordination and oversight of the flow of goods, information, and resources across multiple countries and regions, from raw materials to end customers. It involves managing complex networks of suppliers, manufacturers, logistics providers, and distribution channels, while navigating risks such as geopolitical events, regulatory changes, and disruptions. Leveraging real-time data and multilingual monitoring, Semantic Visions empowers organizations to build resilient, transparent, and agile global supply chains.

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Governance, Risk and Compliance (GRC) is a strategic framework designed to help organizations ensure ethical leadership, manage business risks, and comply with all relevant laws and regulations. Governance refers to leadership structures and decision-making processes; risk management involves identifying, assessing, and addressing threats to the organization’s objectives; and compliance focuses on meeting legal, regulatory, and industry requirements. By leveraging advanced analytics and real-time data monitoring, Semantic Visions enhances GRC efforts, revealing hidden risks, tracking regulatory changes, and strengthening organizational resilience.

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Historical screening is the process of analyzing past data and events to identify patterns, risks, or emerging issues that may inform current decisions. Semantic Visions enables organizations to screen and investigate media, news, and open-source information going back as far as 10 years, uncovering hidden trends, historical controversies, and early warning signals that might impact today’s risk landscape.

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Horizon scanning is the systematic process of continuously monitoring and analyzing diverse information sources to identify emerging risks, opportunities, and trends that could impact an organization’s future. Semantic Visions leverages advanced semantic analysis and multilingual data coverage to detect early signals of change—and actively searches for patterns within these signals. This approach enables organizations to anticipate developments sooner, supporting proactive decision-making and strategic planning.

A knowledge graph is a structured representation of real-world entities—such as companies, brands, locations, commodities, or events—and the relationships between them. By connecting and organizing data from multiple sources, a knowledge graph reveals hidden links and contextual insights within complex networks. Semantic Visions uses this type of visualization in the svEye application to map global business ecosystems, track risks, and deliver richer, more actionable intelligence.

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M&A stands for mergers and acquisitions—the process of companies combining (merger) or one company purchasing another (acquisition). These transactions reshape business landscapes, open new markets, and can introduce significant risks and opportunities. By monitoring global media and alternative data, Semantic Visions helps organizations track M&A activity, identify emerging trends, and assess potential impacts across supply chains, competitors, and industry networks.

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Maritime supply chain management involves the coordination and oversight of goods, resources, and information as they move across global shipping routes and through key ports. This discipline requires managing complex networks of carriers, suppliers, customs authorities, and logistics partners. By tracking specific locations—such as ports and shipping lanes—Semantic Visions enables organizations to monitor disruptions, assess risks, and gain real-time visibility into the maritime supply chain.

Market analysis is the process of systematically assessing a specific market to understand its size, trends, growth potential, competition, and risk factors. This involves gathering and analyzing data from a wide range of sources—including news, industry reports, and alternative data—to gain insights into market dynamics and opportunities. Semantic Visions enhances market analysis with real-time, multilingual monitoring and advanced analytics, helping organizations make better-informed strategic decisions.

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Market intelligence is the systematic collection and interpretation of information about markets, industries, competitors, and customers to inform business strategy. Beyond traditional data, it draws on real-time signals and insights from diverse, multilingual sources to reveal shifting trends, hidden risks, and emerging opportunities. Tools like Semantic Visions’ svEye application give organizations a sharper perspective on market developments—empowering proactive moves and confident decision-making.

Market research is the process of systematically gathering, analyzing, and interpreting information about a market, its participants, and the broader environment. This includes studying customer needs, competitor behavior, and industry trends to support product development, marketing, and strategic planning. By incorporating real-time, multilingual data and advanced analytics, Semantic Visions helps organizations uncover actionable insights and make evidence-based decisions.

Market sentiment describes the overall attitude, emotions, or perceptions of investors, consumers, or the public toward a particular market, sector, or asset. It can be positive, negative, or neutral and often influences market movements. At Semantic Visions, advanced analysis distinguishes not just whether events are happening, but also whether these events carry positive or negative implications—offering a deeper, more actionable understanding of what’s driving sentiment.

A market trend is the general direction in which a market, sector, or asset moves over time—whether upward, downward, or sideways. Trends are influenced by economic forces, technological shifts, consumer behavior, regulations, and geopolitical events. Harnessing large-scale open-source intelligence (OSINT) and multilingual data analysis allows organizations to detect emerging trends sooner, adjust strategies proactively, and maintain a competitive edge.

Media monitoring is the process of tracking news articles, broadcasts, and online content to identify mentions of specific topics, companies, or individuals. Traditional media monitoring typically relies on keyword matching to capture relevant coverage. Semantic Visions goes further by understanding the full context around each mention—connecting related events, detecting sentiment, and uncovering hidden risks or opportunities that simple keyword searches may miss.

Metadata is data that provides information about other data, describing its content, context, structure, or origin. Examples include publication date, author, location, language, and document type. In the context of Semantic Visions, metadata helps organize, filter, and analyze large volumes of information, making it easier to extract relevant insights and understand connections across complex datasets.

Multi-tier supply chain mapping is the process of visualizing and analyzing all layers of an organization’s supply chain—not just direct suppliers (tier 1), but also their suppliers (tier 2), and so on through the entire network. This approach uncovers hidden dependencies, bottlenecks, and potential risks deeper in the supply chain. By leveraging advanced data analytics and global monitoring, organizations gain greater transparency and resilience, enabling faster responses to disruptions or vulnerabilities anywhere in their extended supply ecosystem.

Natural Language Processing (NLP) is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. NLP techniques are used to process and analyze large volumes of unstructured text—such as news articles, reports—identifying key entities, relationships, sentiment, and context. In Semantic Visions, NLP powers advanced analytics, helping transform raw text data into actionable intelligence across multiple languages.

Named Entity Recognition (NER) is a natural language processing technique that automatically identifies and classifies key entities—such as company names, brands, locations, or commodities—within unstructured text. NER enables large-scale analysis by transforming raw data into structured information, supporting deeper insights, pattern detection, and risk monitoring. In Semantic Visions, advanced NER is essential for mapping complex networks and making sense of multilingual data streams. Read more about How entity resolution changes working with data.

News analytics is the process of systematically collecting, processing, and analyzing news content to extract actionable insights, trends, and early warning signals. This involves leveraging natural language processing and semantic analysis to go beyond simple keyword searches—capturing context, sentiment, and emerging risks within massive volumes of news. For organizations, advanced news analytics supports faster, more informed decision-making and a deeper understanding of complex global events.

OSINT, or Open-Source Intelligence, refers to the process of collecting, analyzing, and interpreting information from publicly available sources such as news articles, websites, social media, government reports, and more. By systematically mining and processing this vast amount of open data, Semantic Visions uncovers early warning signals, hidden risks, and actionable insights that support better decision-making. Leveraging advanced analytics and multilingual capabilities, OSINT provides a comprehensive, real-time view of global developments beyond traditional intelligence channels.

Ontology is a structured framework that defines the categories, relationships, and attributes of concepts within a specific domain—enabling machines to understand and organize complex information. In Semantic Visions, a dedicated ontology team builds and maintains this framework across 12 languages, ensuring accurate data classification, entity linking, and contextual analysis on a global scale. Read more in interview with our head of ontology department.

Quantitative analysis is the process of examining numerical data and applying mathematical, statistical, or computational techniques to identify patterns, measure performance, or predict outcomes. In business and finance, this approach supports evidence-based decisions, risk assessment, and performance tracking. By combining quantitative analysis with large-scale data—such as news sentiment or event frequencies—organizations can uncover hidden trends and gain a deeper understanding of complex systems.

Real-time monitoring is the continuous collection, processing, and analysis of data as events unfold, providing up-to-the-minute visibility into risks, trends, or disruptions. This approach enables organizations to detect emerging threats, respond quickly to incidents, and make timely, informed decisions. With the svEye application, users can access real-time monitoring of more than 10 million entities across global markets and supply chains.

Risk detection is the process of identifying potential threats, vulnerabilities, or adverse events that could impact an organization’s operations, reputation, or financial health. This involves continuously monitoring diverse data sources, analyzing patterns, and flagging early warning signals. With a robust risk detection framework, organizations can prioritize threats, allocate resources efficiently, and move from reactive firefighting to proactive risk management.

Risk management is the structured process of identifying, assessing, and addressing potential threats to an organization’s objectives, assets, or reputation. Effective risk management combines traditional expertise with data-driven insights to prioritize actions, reduce uncertainty, and build organizational resilience.

Securities research is the systematic analysis and evaluation of financial instruments such as stocks, bonds, or derivatives to inform investment decisions. This process involves studying company fundamentals, industry trends, market conditions, and external factors that could affect asset performance. By incorporating alternative data and real-time news analytics, securities research can uncover risks and opportunities that traditional analysis may overlook.

Semantic enrichment is the process of enhancing raw data by adding contextual meaning, such as identifying entities, relationships, topics, sentiment, and relevance within unstructured text. This advanced layer of analysis transforms basic information into actionable intelligence, making it easier to search, connect, and interpret complex datasets.

A semantic network is a structured representation of concepts, entities, and the relationships between them—often visualized as interconnected nodes and links. This model helps organize and interpret complex information, revealing how different topics, organizations, events, or locations are connected. Semantic networks support advanced analysis and enable organizations to discover hidden patterns and connections.

Semantic search is an advanced search technology that goes beyond simple keyword matching to understand the meaning and context of a query. By analyzing the intent behind search terms and the relationships between concepts, semantic search delivers more accurate and relevant results—even when users phrase their queries in different ways or languages.

Sentiment analysis is the automated process of classifying news, events, or other data as positive or negative based on their impact on business outcomes. Instead of focusing on emotional tone, Semantic Visions identifies and labels business risk events and business opportunity events, providing an accurate view of whether developments around a company, brand, location, commodity, or entire industry are favorable or unfavorable. This enables precise monitoring of reputation, performance, and emerging risks or opportunities. Read more on Market Sentiment Indicators.

Source analysis is the process of evaluating and verifying the reliability, credibility, and relevance of information sources—such as news outlets, reports, or databases. This includes assessing factors like the source’s reputation, regional focus, historical accuracy, and potential biases. In Semantic Visions, rigorous source analysis ensures that insights and alerts are based on trustworthy, high-quality data. Read more on the Technology page.

The stock market is a regulated marketplace where shares of publicly traded companies are bought and sold. It serves as a barometer for economic activity and investor sentiment, with prices influenced by company performance, global events, and broader market trends. By monitoring news and alternative data in real time, organizations can gain deeper insights into factors that move the stock market and anticipate shifts in investor behavior.

Stock market prediction is the use of data analysis, statistical models, and advanced algorithms to forecast future movements of stock prices or market trends. This involves examining a wide range of factors—including financial performance, news sentiment, macroeconomic indicators, and global events—to anticipate market behavior. By integrating alternative data and real-time news analytics, organizations can enhance the accuracy of their stock market predictions and make more informed investment decisions. Explore the trends in our S&P 500 free data dashboard here.

Strategic intelligence is the collection and analysis of information to support long-term decision-making and planning at the organizational or national level. It involves understanding market dynamics, competitor actions, geopolitical developments, and emerging risks or opportunities that could impact future strategies. By leveraging diverse data sources, including multilingual open-source intelligence, organizations gain a comprehensive view that informs proactive and resilient strategic initiatives.

Supplier risk management is the process of identifying, assessing, and mitigating risks associated with suppliers and third-party vendors. This includes monitoring financial stability, compliance issues, geopolitical exposure, and operational disruptions that could affect the supply chain. The svEye application supports this by enabling multi-tier supply chain mapping—tracking risks across all supplier levels—providing management with a powerful tool to monitor, control, and strengthen supplier networks proactively.

Supply chain resilience is the ability of a supply network to anticipate, prepare for, respond to, and recover from disruptions—maintaining continuity and minimizing impact on operations. Building resilience involves risk identification, diversification of suppliers, real-time monitoring, and adaptive strategies. For example, a company might discover—through multi-tier mapping—that despite multiple suppliers listed, it ultimately relies on a single source at deeper tiers, revealing a hidden vulnerability. Tools like svEye provide this detailed supply chain mapping, ensuring organizations have full visibility so nothing comes as a surprise.

Supply chain risk refers to the potential for disruptions, delays, or failures within any part of the supply chain network that can negatively impact production, delivery, or business continuity. These risks can arise from factors such as supplier insolvency, geopolitical events, natural disasters, regulatory changes, or logistical challenges. By leveraging comprehensive data monitoring and multi-tier supply chain mapping, organizations can identify vulnerabilities early and develop strategies to enhance resilience and mitigate impact. More in our white paper – Leveraging Open Source Intelligence for Improved Supply Chain Risk Management.

Systematic trading is an investment approach that relies on predefined, rule-based strategies executed automatically by algorithms. These strategies use quantitative data, technical indicators, and alternative data sources to identify trading opportunities and manage risk without human discretion. By incorporating real-time insights and advanced analytics, systematic trading aims to enhance consistency, reduce emotional bias, and improve decision-making in dynamic markets.

Taxonomy is a hierarchical classification system that organizes concepts, entities, or data into categories and subcategories based on shared characteristics. It provides a structured framework to systematically label and group information, enabling more effective search, analysis, and understanding of complex datasets. In Semantic Visions, taxonomy helps to standardize data classification across multiple languages and domains, supporting accurate insights and consistent reporting.

Technical analysis is the evaluation of financial markets by analyzing historical price movements, trading volumes, and chart patterns to forecast future market behavior. It relies on statistical tools and indicators to identify trends, support and resistance levels, and potential entry or exit points. While traditionally focused on price data, integrating alternative data and real-time news can enhance technical analysis by providing additional context and early signals.

Text mining is the process of automatically extracting valuable information and patterns from large volumes of unstructured text data—such as news articles or reports. Using advanced algorithms and natural language processing, text mining transforms raw text into structured insights, revealing hidden trends, relationships, and risks that would be difficult to detect manually.

Third-party risk management (TPRM) is the process of identifying, assessing, and mitigating risks associated with external vendors, suppliers, and partners that organizations rely on. This includes evaluating financial stability, compliance, operational resilience, and cybersecurity risks across all tiers of the supply chain. Leveraging real-time data, open-source intelligence, and multi-tier mapping tools, organizations can gain comprehensive visibility and control over their third-party ecosystem, ensuring stronger risk mitigation and supply chain resilience.

A tier refers to a specific level or layer within a supply chain or organizational structure. For example, tier 1 suppliers are direct suppliers to a company, while tier 2 suppliers provide goods or services to tier 1, and so forth. Understanding multiple tiers is essential for mapping complex networks, assessing risks deeper in the supply chain, and gaining full visibility into dependencies. More in our white paper – How Multi-Tier Supply Chain Mapping Builds Resilience in a Complex World.

A trading strategy is a predefined set of rules and criteria used by investors or traders to make decisions about buying, selling, or holding financial assets. These strategies can be based on technical indicators, fundamental analysis, news events, or alternative data signals. Effective trading strategies combine data-driven insights with risk management principles to optimize returns and manage exposure in dynamic markets.

Trading the news is a strategy where investors and traders make buy or sell decisions based on real-time analysis of news events, economic reports, or market-moving announcements. By quickly interpreting the impact of such information, traders aim to capitalize on short-term price volatility and market reactions. Access to timely, accurate, and context-rich data—including semantic analysis of news—enhances the effectiveness of this approach.

Unstructured data refers to information that does not have a predefined data model or is not organized in a traditional database format. Analyzing unstructured data requires advanced techniques such as natural language processing and semantic analysis to extract meaningful insights, detect risks, and reveal patterns hidden within vast volumes of diverse content.

svEye is Semantic Visions’ application that allows users to search across entities and events—whether they seek a data-driven perspective or explanations of real-world occurrences. The platform supports real-time monitoring and historical screening for all entities, covering 12 languages. svEye enables comprehensive exploration and analysis, empowering users to uncover insights, track developments, and understand complex global dynamics with ease.