Nearly three years after ChatGPT’s launch in November of 2022, enterprises still struggle to get accurate, reliable and trustworthy outputs from generative AI.

Enterprises that have tried using LLMs to optimise important business processes or support critical business decision-making keep running into now-familiar roadblocks:

• search tools that deliver factually incorrect information
• analytics platforms that give unreliable and inaccurate reports
• conversational AI agents that dismay customers with their sycophancy and untrustworthy answers

These are among the reasons that many enterprises are still struggling to unlock the value in their AI investments and why many are revisiting their AI architecture strategies.

To be fair, many of these shortcomings aren’t the fault of LLMs. Generative AI is designed specifically to fabricate answers and the hallucinations for which LLMs are so well known are just a side effect of this feature. Anyone who has worked with these systems knows that you can’t prompt, fine-tune or guardrail your way through these problems. They are inherent in the technology.

So, how can enterprises finally realise the promise of AI and get the factually accurate, trustworthy answers that they and their customers need?

The answer is “neuro-symbolic AI.” That is, systems that combine neural-network technology like LLMs with symbolic technology like knowledge graphs.

LLMs offer human-friendly natural-language and powerful predictive and generative abilities. Knowledge graphs add conceptual understanding of an enterprise’s unique capabilities and knowledge.

Much like the two systems in Daniel Kahneman’s “Thinking, Fast and Slow,” these two technical paradigms work together to create a fully functioning, more intelligent “brain” for your organisation.

Table of Contents

• Neuro-symbolic AI is not new
  • The power of semantic meaning
• Better together: Knowledge Graphs and LLMs
• Putting it all together

Neuro-symbolic AI is not new

The launch of ChatGPT brought generative AI into the spotlight, but AI as a field has been around for decades, long before IBM’s Watson appeared on Jeopardy and Deep Blue beat chess grandmaster Garry Kasparov. The machine learning and other neural-network methods that power generative AI and LLMs date back to the 1940s, while symbolic AI arose in the 1950s|— and many AI practitioners have been integrating these two technology paradigms since the 1990s. So it’s not like people have been overlooking these technologies. It’s just that recent advances in computing power and AI methods have given them both new importance and significance.

LLMs are great at what they do because they are trained on vast amounts of data gleaned from a variety of sources. When you need to make a decision for your company or provide an answer about one of your enterprise’s products or services, though, general knowledge of the world, while helpful, is not sufficient. And, while it is possible to train and fine-tune LLMs with your enterprise data, only symbolic AI can contextualise and deliver your enterprise’s precise, unique knowledge.

Symbolic AI is the explicit representation of knowledge. It captures the facts about an enterprise and the knowledge it has accumulated and stores them in a way that both computers and humans can understand and use. It’s the use of universally understood symbols – numbers, words, logical expressions, etc. – that gives this type of AI its unique power. While LLMs, on the other hand, can offer remarkably plausible predictions based on their statistical analysis of what has been shared on the web and elsewhere, only symbolic AI, like a semantic knowledge graph, can deliver facts and knowledge that are grounded in the actual activities that an enterprise does and the wisdom its employees have accumulated.

As distinct as these two components of neuro-symbolic AI are, each can exhibit elements of the other. For example, LLMs can be trained on enterprise data to better inform their outputs. And implementations like the Wikidata project show how symbolic AI can be used to represent general facts about the world. This explains why some have used the yin-yang image to illustrate the concept of neuro-symbolic AI.

The power of semantic meaning

One of the most impactful aspects of symbolic AI is its ability to give semantic meaning to the information it works with. The symbols that are used to represent real-world entities can be defined, connected and otherwise contextualised so that both humans and computers can truly understand the concepts represented by the symbols. So when an analyst asks a knowledge graph about “new customer orders in Q3,” the system has the information it needs to understand how the enterprise defines a “new” customer, what constitutes an “order,” and the time span for the third quarter in their fiscal calendar.

LLMs can approximate semantic understanding by looking at which words and tokens appear close to one another in vector space, but only human-vetted knowledge ensconced in a symbolic AI system can deliver consistent answers that align with an enterprise’s actual practices and precise language. In the last example, for instance, if your enterprise has adopted an unconventional fiscal year, an LLM might assume a conventional January-through-December fiscal year, completely missing the meaning of “Q3” in your organisation.

One powerful implication of this clear articulation of business information in a knowledge system is the ability to create a “semantic layer,” an enterprise information architecture concept that lets an organisation understand and connect its business processes, technical objects and data and information. This ability to access semantic understanding lets enterprises streamline and accelerate their business processes and make crucial business decisions more quickly, giving them an almost unfair competitive advantage. As one industry expert recently observed about the emerging ubiquity of AI, “If everything becomes intelligent, then meaning becomes the differentiator.”

Better together: Knowledge Graphs and LLMs

As you evaluate the strengths and weaknesses of each of these technologies, their complementary benefits become more clear. At the highest level, LLMs and other neural-network technology are really good at learning, while knowledge graphs and other symbolic AI technology are good at knowing.

LLMs are very good at learning to identify patterns, make predictions and generate outputs. They do this by looking at huge repositories of information, breaking it down into mathematically computable elements and reassembling those elements into statistically plausible words, images and recordings. This learning doesn’t lead to actual knowledge that the LLM can use later. You can see this when you ask an LLM the same question and get a slightly (sometimes significantly) different answer each time you run the query. This reliance on statistical computation is why these systems are called “black boxes.” Not even the engineers who designed them can get them to explain their actions. This lack of transparency and explainability has been an obstacle to enterprises trying to build trustworthy AI systems.

Knowledge graphs capture and expose the things that an enterprise actually knows—its business practices, its policies and procedures, its customers and suppliers, its products and services, its data and other digital assets, and even the tacit knowledge in its employees’ heads. This knowledge is unique to any one enterprise. In fact, it’s arguably any enterprise’s most important asset. Unlike the general information captured at one point in time that an LLM works with, the enterprise-specific information in a knowledge graph is always up to date and accurate.

Until recently, building knowledge graphs strained the capabilities of most enterprises, but in a serendipitous turn of events, it turns out that LLMs can help build these sophisticated knowledge systems. LLMs’ natural-language interfaces, predictive power and generative abilities help knowledge engineers by automating labor-intensive tasks like entity extraction, accelerating ontology building and improving the quality of the data in the graph. With the help of generative AI, knowledge graphs are now accessible to many more enterprises.

It also turns out that knowledge graphs can help LLMs. Their authoritative, human-vetted knowledge of an enterprise’s unique capabilities and knowledge can address the transparency, accuracy and other trust issues that arise with unreliable LLM outputs.

Putting it all together

Neuro-symbolic AI is where these complementary capabilities come together. Just as you wouldn’t bring only half of your brain to work, enterprises shouldn’t bring just one half of artificial intelligence’s capabilities to their enterprise architectures. So, if your enterprise is not already exploring how knowledge graphs and symbolic AI can augment your organisation’s intelligence—both artificial and actual—now is a good time to start.

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Tourism, as a worldwide socioeconomic phenomenon, has experienced profound changes driven by the Fourth Industrial Revolution—marked by the merging of disruptive technologies such as Artificial Intelligence (AI), Big Data, the Internet of Things (IoT) and cloud computing. These innovations are transforming the tourism industry by enabling more personalised experiences, improving operational efficiency and promoting sustainable practices. Digital transformation in tourism is more than just adopting new tools; it represents a fundamental shift in how destinations are managed, marketed and experienced. As the sector continues to evolve, it becomes increasingly important to examine the challenges and opportunities that come with integrating technology into its core functions, especially in the development and use of tourism data spaces. In the post-COVID-19 world, the industry aims not only to recover but to reimagine its growth strategies in line with societal priorities around health, sustainability and resilience. The integration of smart technologies plays a crucial role in managing new challenges as tourism flows and shaping visitor behavior, thereby supporting the development of sustainable and resilient destinations.

This technological transformation has reshaped travelers’ expectations, who now seek hyper-personalised, sustainable and seamless experiences. At the same time, it presents complex challenges for destination managers, who must balance competitiveness, resource conservation, relations between residents and tourists, and social inclusion. Addressing these issues requires a shift from traditional, fragmented, siloed methods to a more integrated, data-driven approach, where data is viewed as a strategic asset and a catalyst for innovation. The European Union’s vision for data spaces offers a transformative framework for how data is accessed and used across different sectors, including tourism. Data spaces aim to eliminate data silos, enabling seamless and secure data exchange to unlock new opportunities for innovation and value creation. Developing tourism data spaces is particularly vital since tourism is an information-intensive sector, relying heavily on data for planning, decision-making and enhancing the traveler experience. While digital solutions have significant potential, much of this remains untapped. By enabling secure and trustworthy data sharing among stakeholders such as tourism boards, hospitality providers, transportation services and local businesses, data spaces can foster a more comprehensive and agile approach to tourism management.

Unlocking the Power of Data: Transforming the Landscape of Tourism

The modern tourist functions as a digital prosumer, creating data through every interaction—from online bookings to social media reviews—and consuming information to make better decisions. According to the European Commission (2022), 89% of European tourists use mobile apps during their trips and 76% view real-time personalised recommendations as essential. This shift has elevated data to a key strategic asset in the tourism industry, with an estimated annual value of €152 billion for the European tourism economy. Access to real-time information about visitor flows, preferences and behaviors allows destination managers to better allocate resources, reduce congestion and improve overall visitor satisfaction. Additionally, integrating socio-spatial data—both tangible and intangible—can provide a deeper understanding of landscape values, supporting sustainable, long-term planning.

However, the full potential of this data remains largely untapped because of fragmented data ecosystems, where information is stored in isolated silos across booking platforms, public authorities, social networks, transport systems and local tourism stakeholders. This disconnection hampers destinations’ capacity to offer integrated services and to proactively address crises like over-tourism or climate-related disruptions. Therefore, creating a sound European tourism data space is considered a priority to drive innovation, enhance competitiveness and promote sustainability in the tourism industry, on par with advancements in mobility.

Overcoming these challenges necessitates the development of an interconnected data ecosystem that enables secure, reliable and scalable data sharing. Tourism data spaces provide a solution by creating a framework where data can be securely shared, accessed, reused and analysed, promoting collaboration, innovation and better-informed decision-making. However, tourism data spaces are more than just technological infrastructure; they are complex ecosystems that require careful attention to governance, security and interoperability. Ensuring data security and privacy is especially critical given the rise of cyber threats and strict data protection laws. Building trust among data providers and users depends on implementing robust security measures, transparent governance structures and compliance with regulations such as GDPR.

Ensuring seamless interoperability among diverse data sources and systems is essential for the success of tourism data spaces. To achieve this, adopting standardised data formats, developing common vocabularies for semantic interoperability and implementing open APIs are crucial steps.

A shift toward low-carbon tourism, driven by big data, has the potential to transform the traditional tourism business model, paving the way for a more sustainable and intelligent future for the tourism supply chain. However, increasing reliance on digital technologies may also create feelings of alienation and isolation among tourists seeking authentic experiences and direct connections with local cultures. Therefore, forward-thinking tourism cities should look beyond sustainability alone, adopting a holistic approach that considers emerging trends, including the importance of encouraging both tourists and residents to periodically disconnect from digital media through digital detox routines to support mental health and resilience. Additionally, integrating AI and IoT can lead to the development of smarter, visitor-centered destinations. By leveraging real-time data and AI-driven insights, tourism services can be enhanced through greater personalisation, accuracy, innovation and inclusivity.

Revolutionising European Tourism: The Transformative Power of the Common European Tourism Data Space

The importance of Common European Tourism Data Spaces (ETDS) extends beyond just technological innovation—they signify a fundamental shift towards collaborative data governance. Their adoption can help address key paradoxes in the tourism sector, such as overcrowding in popular destinations like Barcelona and Venice and rural depopulation, by enabling data-driven redistribution of visitor flows. Tourists are increasingly seeking authentic experiences that reflect their values of ecological responsibility and cultural appreciation. Meanwhile, destinations recognise that adopting sustainable practices can offer long-term advantages, including greater resilience to climate change, the preservation of natural resources and economic diversification. Digital technology has the potential to greatly influence the sustainability of tourist destinations—both positively and negatively—and this research aims to explore and clarify these impacts.

The merging of digital technologies with sustainability principles offers a valuable opportunity to reshape the tourism industry into a more environmentally sustainable and economically resilient model.

The long-term attractiveness of a tourism destination is essential for its ongoing growth, requiring continual innovation and quality enhancements to stay competitive in the global market. Additionally, ETDS serves as the technological foundation of smart tourism destinations—a concept endorsed by UNWTO (2021)—which combines technology, sustainability and inclusivity to foster sustainable and vibrant tourism experiences.

Within this framework, DEPLOYTOUR was approved — the Common European Tourism Data Space – under the Digital Europe Programme. This is a three-year project led by AnySolution, with a consortium of 40 entities from 13 member states representing the entire tourism value chain. Its main objective is to develop a trusted and secure Common European Tourism Data Space to improve data access and sharing, fostering innovation and new business models aimed at boosting tourism competitiveness and sustainability by supporting digital and green transitions while empowering SMEs and DMOs in their transformation.

The implementation of the Common European Tourism Data Space will be evaluated through five pilot projects targeting various tourism destinations and types, such as sun & beach, mountain, MICE and cultural tourism. Facilitating data sharing among different tourism stakeholders will boost destination competitiveness and contribute to a seamless tourism experience, ultimately enhancing visitor satisfaction. All information about DEPLOYTOUR can be found at this link.

Fundamentally, European Tourism Data Spaces aim to harness the economic benefits of data while upholding ethical standards related to individual rights and sustainability. This approach aligns with the broader objectives of the European Green Deal and the UN Sustainable Development Goals.

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Evolving the concept of “data product” to meet the specific demands of AI applications: ease the adoption of existing data sharing frameworks by AI practitioners, and act as a catalyser of AI innovation.

The term “data product” was originally coined in the seminal work of Zhamak Dehghani in 2019[1], where she presented the data mesh paradigm, and, as a key aspect, applied the product thinking to datasets, to make them easily discoverable, addressable, trustworthy, interoperable and secure.

Since then, the concept of a data product has been embraced by many organisations to streamline data reuse across various use cases, reducing costs and saving time. According to the Gartner Hype Cycle for Data Management in 2024, data products appear to be still on the rise but close to the peak of expectations. Additionally, “inquiries for the term data product have more than doubled in 2023 compared to 2022”, and “1 in 2 organisations studied have already deployed data products”[2], showing a clear interest of the industry.

While there is no agreed-upon definition of data product, the concept of packaging datasets along with all the relevant elements identified by an organisation to facilitate their discovery, exchange, and consumption by others clearly supports data sharing and transactions. As an example, CEN proposes data product as a key element in Trusted Data Transactions[3], defined therein as “standardised data unit packaging data and relevant conditions into a useable form”. This is why the data product concept has also been adopted by designers and implementers of data spaces, the instruments identified by the European Commission in their “Data Strategy”[4] to break data silos in Europe and foster cross-sector and cross-country data sharing in a trusted and efficient way.

Nowadays, the primary application of data has moved from traditional data analytics to increasing sophisticated AI workflows. These impose unique demands on data, including specific descriptions, new data quality dimensions and metrics, and tools to facilitate risk assessment in compliance with standards or regulations like the AI Act. The traditional concept of a data product (focused on packaging and sharing datasets for general use) must then evolve towards supporting specialised requirements of AI, a new paradigm that in BDVA we refer to as “AI-ready Data Products”.

With this new paradigm in mind, BDVA organised a dedicated session during its recent Data Week 2024[5], held in Luxembourg on 10^th December 2024. The session brought together several experts to evaluate this approach from various perspectives and discuss which elements of the data product concept should be revisited, what additional features might be required, and how “AI-ready Data Products” can play a pivotal role in fostering AI innovation.

Coen Janssen, Policy Office at EC DG CNECT, opened the session by framing the discussion within the context of the European Data Union Strategy and the political guidelines established from 2024 to 2029 by Ursula von der Leyen, as well as the European Data Act and the related European Commission standardisation request (composed of five requested deliverables: Trusted Data Transaction standard, Data catalogue implementation framework, Semantic assets implementation framework, Data governance standard for data space participants and Maturity model for Common European Data Spaces).

Shane O’Seasnain (Eindhoven University of Technology) exposed the importance of the domain experts on data, AI and platforms behind the data product to facilitate how AI-ready Data Products connect to other applications, like digital twins.

Jordi Cabot (Luxemburg Institute of Science and Technology) highlighted the impact on AI outcomes when data is biased. He showed its impact on textual data and images, and how the outcomes of search engines and language models outcomes can be biased depending on the query and the data provided to the algorithms for training or caching. He concluded with the importance of data annotation on a multidimensional space – usage, distribution, composition, provenance and social aspects.

In the case of Anastasia Sofou (SEMIC), she presented the Machine Learning extension for DCAT-AP (MLDCAT-AP) as the ideal metadata solution to describe “AI-ready Data Product”, and also to address some of the requirements of the AI Act regarding data quality and data governance. The novel solution incorporates the quality of data, the algorithm class, the ML model, the risks of the model and the dataset used for training, essential to comply with the AI Act.

Chandra Challagonda (FIWARE CEO) emphasised data provenance as key at every stage of the data value chain to ensure transparency, origin tracking, rights management, and in summary foster adoption of data products, but also key for accountability, traceability and auditability of AI applications – essentials also for compliance with the AI Act legislation.

Finally, Fabrice Tocco (Dawex co-CEO) emphasised the fact that data products are not just data, highlighting the importance of trust as a non-negotiable feature to materialise “AI-ready Data Products”.

The topic fostered a nice discussion with the audience, who brought additional aspects such us the incorporation of knowledge graphs, LLMs to enable semantic interoperability between data products, how to apply the new concept to data spaces, and the process to assess the readiness of ‘AI-ready Data Products’ for deployment in real-world.

After this fruitful session, we are firmly convinced that ‘AI-ready Data Product’ is key to serve a dual purpose: to ease the adoption of existing data sharing frameworks, and to act as catalyser of AI innovation. First, meeting specific demands of AI based applications in an easy manner broadens the scope of utilisation of existing data-sharing frameworks, like dataspaces. By embedding those needs into the ‘AI-ready Data Product’ concept, the requirements can be addressed and satisfied across the various building blocks of the data space architecture. Second, it can act as a powerful enabler of AI innovation, offering industry-ready solutions tailored to address the complexities and unique requirements of cutting-edge AI applications.

Despite these relevant outcomes and progress, significant challenges must be overcome for ‘AI-ready Data Products’ to become a reality. BDVA is committed to addressing these challenges by engaging more experts and stakeholders from the community to develop a comprehensive data product framework tailored to the unique needs of AI applications. We firmly believe this paradigm will enhance data sharing for AI across SMEs and large industries in a trusted, seamless, and legally compliant manner, aligned with existing and emerging regulations.

[1] https://martinfowler.com/articles/data-monolith-to-mesh.html

[2] https://www.gartner.com/en/documents/5456063

[3] https://www.cencenelec.eu/media/CEN-CENELEC/News/Workshops/2024/2024-01-16%20-%20Data%20Transactions/cwa-draft-part1-0-8_clean.pdf

[4] https://digital-strategy.ec.europa.eu/en/policies/strategy-data

[5] https://data-week.eu/session/ai-ready-data-products/

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